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Useful for cleaning import lists, dependencies, log files, and config entries."},{"name":"number_base_convert","description":"Convert numbers between bases: decimal, binary, octal, hexadecimal, or any base 2–36. Auto-detects 0x, 0b, 0o prefixes."},{"name":"validate_url","description":"Parse and validate a URL. Returns decomposed components: protocol, hostname, port, path, query parameters, hash, and origin."},{"name":"check_contrast_ratio","description":"Calculate WCAG 2.1 contrast ratio between two colors. Returns ratio and compliance for AA/AAA normal and large text."},{"name":"html_to_markdown","description":"Convert HTML to clean Markdown. Strips scripts, styles, nav, ads, and comments. Converts headings, lists, links, images, code blocks. Ideal for preparing web co"},{"name":"cron_parse","description":"Parse a cron expression into a human-readable schedule description. 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Supports 25+ mode"},{"name":"consistency_check","description":"Compare multiple LLM responses to the same prompt and detect inconsistencies using Jaccard word-overlap similarity and fact drift (number comparison). Fast, det"},{"name":"llm_json_schema_check","description":"Validate that an LLM JSON output matches a JSON Schema definition. Tests required fields, types, enums, nested objects, and arrays. Critical for function-callin"},{"name":"latency_benchmark","description":"Measure response time of one or more HTTP endpoints (GET/POST). Runs N iterations and returns min/max/avg/p95 latency. Useful for API and MCP server benchmarkin"},{"name":"response_quality_score","description":"Score an LLM response against the criteria you pass: coverage of expected_keywords and compliance with max_length. Returns a 0-100 score over the criteria actua"},{"name":"rag_relevance_rank","description":"Rank an array of text chunks by relevance to a query using TF-IDF scoring. 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"},{"name":"extract_json_path","description":"Extract a value from a JSON string using dot-notation path (e.g., \"user.address.city\", \"items.0.name\", \"meta.tags\"). Supports array index access via numeric pat"},{"name":"generate_json_ld","description":"Generate a ready-to-paste <script type=\"application/ld+json\"> snippet for GEO / structured data optimization. Supported types: WebSite, FAQPage, Article, Person"},{"name":"analyze_diff_bugs","description":"Pattern-based diff linter: flags a fixed set of risky shapes in changed code — query-string interpolation (SQL/Cypher/Mongo injection shape), shell interpolatio"},{"name":"generate_test_cases","description":"Generate a set of test cases (valid, edge, invalid, pairwise) for a given feature description. Declared constraints drive the boundaries: a length or numeric bo"},{"name":"run_pr_gate_pipeline","description":"Review triage for a pull request. Takes a unified git diff (`git diff HEAD`) and returns: diff-lint findings with the lines that produced them, regression impac"},{"name":"validate_mcp_response","description":"Validate that an MCP tool response conforms to expected format, schema, and content rules. Use this to QA-test any MCP server tool. Supply the tool's actual JSO"},{"name":"llm_output_validator","description":"Validate an LLM response against QA criteria: format checks (JSON, code, markdown), content rules (must-include, must-not-include), length constraints, language"},{"name":"compare_responses","description":"Compare two ALREADY-PRODUCED outputs (e.g. model A vs model B on the same task) side by side. Returns deterministic metrics (token cosine, ROUGE-L, Jaccard, len"},{"name":"analyze_responses","description":"Semantically analyze N already-produced model outputs for the SAME task (the MCP counterpart to the LLM Sandbox). Without a reference: computes consensus — pair"},{"name":"prompt_test_suite","description":"Define a test suite for a prompt: provide the system prompt, user prompt, and expected output criteria. Returns a test plan with scored rubric — use this as inp"},{"name":"mcp_server_health_check","description":"Generate a health check report for an MCP server's tool manifest. Validates tool definitions, schema quality, naming conventions, and documentation completeness"},{"name":"mcp_server_evaluate","description":"Run a full compliance evaluation against a live MCP server URL. Tests: server reachability (ping), manifest discovery (GET /mcp), schema quality (snake_case nam"},{"name":"json_schema_validate","description":"Validate a JSON value against a JSON Schema (draft-07 subset). Supports type, required, properties, items, enum, const, pattern, format (email/uri/date), minimu"},{"name":"flatten_json","description":"Flatten a nested JSON object to single-level dot-notation keys (e.g. {\"a\":{\"b\":1}} → {\"a.b\":1}), or unflatten dot-notation keys back to a nested object. Support"},{"name":"xml_to_json","description":"Convert an XML string to a JSON object. Supports attributes, nested elements, arrays, CDATA, and namespaces. Options: parse numbers, parse booleans, ignore attr"},{"name":"redact_pii","description":"Automatically detect and redact Personally Identifiable Information (PII) from text. Replaces emails, phone numbers, SSNs, credit cards, IP addresses, and JWT t"},{"name":"mock_from_schema","description":"Generate realistic mock data from a JSON Schema. Supports all common types (string, number, integer, boolean, array, object, null), format hints (email, date, d"},{"name":"transform_json_array","description":"Transform a JSON array using common operations: pluck (extract specific fields), filter (by field value), sort_by (field), group_by (field), count_by (field), u"},{"name":"json_to_csv","description":"Convert a JSON array of objects to CSV format. Automatically detects columns from all object keys. Handles quoting and escaping per RFC 4180."},{"name":"case_convert","description":"Convert a string between naming conventions: camelCase, PascalCase, snake_case, kebab-case, UPPER_SNAKE_CASE, dot.case, Title Case. Essential for code generatio"},{"name":"sort_lines","description":"Sort, deduplicate, reverse, or filter lines of text. Useful for cleaning import lists, dependencies, log files, and config entries."},{"name":"number_base_convert","description":"Convert numbers between bases: decimal, binary, octal, hexadecimal, or any base 2–36. Auto-detects 0x, 0b, 0o prefixes."},{"name":"validate_url","description":"Parse and validate a URL. Returns decomposed components: protocol, hostname, port, path, query parameters, hash, and origin."},{"name":"check_contrast_ratio","description":"Calculate WCAG 2.1 contrast ratio between two colors. Returns ratio and compliance for AA/AAA normal and large text."},{"name":"html_to_markdown","description":"Convert HTML to clean Markdown. Strips scripts, styles, nav, ads, and comments. Converts headings, lists, links, images, code blocks. Ideal for preparing web co"},{"name":"cron_parse","description":"Parse a cron expression into a human-readable schedule description. Supports standard 5-field cron (minute hour day month weekday)."},{"name":"cron_validator","description":"Validate a 5-field cron expression, explain the schedule, and preview the next execution times. Use this to debug cron jobs before they reach production. Return"},{"name":"calculate_readability","description":"Calculate readability scores: Flesch Reading Ease, Flesch-Kincaid Grade Level, Coleman-Liau Index, and Automated Readability Index. Useful for evaluating LLM ou"},{"name":"normalize_whitespace","description":"Normalize whitespace: trim trailing spaces, collapse blank lines, normalize line endings (LF/CRLF), convert tabs to spaces. Useful for cleaning code, configs, a"},{"name":"embedding_similarity","description":"Compute text similarity using local algorithms (Bag of Words, TF-IDF, Character N-grams). No API key needed — runs entirely in-process. NOT real embeddings: for"},{"name":"llm_format_check","description":"Validate that an LLM output matches an expected format: JSON, Markdown, code block, bullet list, numbered list, table, YAML, XML, or custom regex. Essential for"},{"name":"hallucination_check","description":"Lexical hallucination check: verifies an LLM answer's words, numbers and polarity against the provided source/context. Fast, deterministic, no API key needed. E"},{"name":"prompt_injection_scan","description":"Scan user input or prompts for common prompt injection patterns. Detects system prompt overrides, jailbreak attempts, role manipulation, encoding tricks, delimi"},{"name":"token_budget_calculator","description":"Plan token allocation across system prompt, user input, context/RAG chunks, and expected output. Warns if budget exceeds model context window. Supports 25+ mode"},{"name":"consistency_check","description":"Compare multiple LLM responses to the same prompt and detect inconsistencies using Jaccard word-overlap similarity and fact drift (number comparison). Fast, det"},{"name":"llm_json_schema_check","description":"Validate that an LLM JSON output matches a JSON Schema definition. Tests required fields, types, enums, nested objects, and arrays. Critical for function-callin"},{"name":"latency_benchmark","description":"Measure response time of one or more HTTP endpoints (GET/POST). Runs N iterations and returns min/max/avg/p95 latency. Useful for API and MCP server benchmarkin"},{"name":"response_quality_score","description":"Score an LLM response against the criteria you pass: coverage of expected_keywords and compliance with max_length. Returns a 0-100 score over the criteria actua"},{"name":"rag_relevance_rank","description":"Rank an array of text chunks by relevance to a query using TF-IDF scoring. Simulates retrieval ranking for RAG testing without needing embeddings or an API."},{"name":"toxicity_scan","description":"Scan text for toxic language, hate speech, bias/stereotype framing, violence, sexual and self-harm content. Lexical + structural pattern matching (identity term"},{"name":"guardrail_test","description":"Test an LLM response against a set of guardrail rules: must-include, must-not-include, max length, required format, language, forbidden patterns, and custom reg"},{"name":"function_call_validate","description":"Validate an LLM function call / tool_use output: check that function name is in allowed list, arguments match expected schema, no extra/missing args. For OpenAI"},{"name":"conversation_analyze","description":"Analyze a multi-turn conversation for context retention, topic drift, instruction following, and repetition. Accepts messages array [{role, content}]. Essential"},{"name":"mcp_schema_lint","description":"Lint an MCP tool definition for best practices: naming conventions, description quality, schema completeness, required fields consistency, description length. R"},{"name":"cot_analyzer","description":"Analyze a Chain-of-Thought (CoT) or reasoning trace from an LLM. Detects step count, logical flow, conclusion presence, backtracking, and estimates reasoning de"},{"name":"ab_test_report","description":"Generate an A/B test report comparing two prompts or model configurations. Accepts arrays of scores and returns statistical comparison: mean, median, std deviat"},{"name":"context_window_check","description":"Given an array of message objects [{role, content}], estimate total token usage and check if it fits in the target model's context window. Warns about truncatio"},{"name":"vector_similarity","description":"Compute similarity/distance between two float vectors: cosine similarity, dot product, Euclidean and Manhattan distance. Essential for vector DB relevance scori"},{"name":"normalize_vector","description":"L2-normalize a float vector (produce a unit vector with norm=1). Required by many vector DBs (Pinecone, Qdrant cosine). Supports batch normalization of up to 10"},{"name":"vector_quantize","description":"Simulate int8 or int4 quantization of float32 embedding vectors. Reduces storage by 4x (int8) or 8x (int4). Returns quantized values, scale factor, and precisio"},{"name":"vector_stats","description":"Compute statistics for a float vector or matrix of vectors: mean, std, L2 norm, min, max, sparsity, top-K indices. Useful for debugging embedding quality and an"},{"name":"bm25_score","description":"Compute BM25 relevance score between a query and one or more documents. BM25 is the industry-standard keyword-based ranking algorithm used in Elasticsearch, Ope"},{"name":"build_rag_prompt","description":"Assemble a complete RAG (Retrieval-Augmented Generation) prompt from retrieved context chunks and a user query. Handles token budgeting, citation numbering, sys"},{"name":"prompt_template_fill","description":"Fill a prompt template with variables. Supports {{variable}} syntax and {{#if key}}...{{/if}} conditional blocks. Returns the filled prompt and lists unfilled v"},{"name":"few_shot_formatter","description":"Format few-shot examples for LLM prompts. Converts example pairs into formatted blocks. Supports chat format (User/Assistant), XML tags, Markdown, or plain text"},{"name":"system_prompt_builder","description":"Build a structured system prompt from components: role, task, constraints, output format, tone, language, and examples. Generates a production-ready system prom"},{"name":"model_info","description":"Get detailed specs for an AI model: context window, pricing per 1K tokens, knowledge cutoff, provider, multimodal support, reasoning capabilities, and feature l"},{"name":"compare_models","description":"Compare 2-5 AI models side by side: context window, pricing, multimodal, reasoning capabilities, and provider. Returns a comparison table with a recommendation "},{"name":"http_status_lookup","description":"Look up detailed information about any HTTP status code: class, name, description, cacheability, typical causes, and handling best practices. Covers every code "},{"name":"parse_http_headers","description":"Parse a raw HTTP headers block into a structured JSON object. Detects multi-value headers, masks Authorization values, and optionally audits for missing securit"},{"name":"generate_curl","description":"Generate a curl command from request parameters. Supports GET/POST/PUT/DELETE, custom headers, JSON body, and form data. Useful for documentation, sharing, and "},{"name":"extract_todos","description":"Extract TODO, FIXME, HACK, BUG, NOTE, OPTIMIZE, and custom tags from any source code or text. Returns line numbers, tag types, and message text. Essential for t"},{"name":"detect_secrets","description":"Scan code or config files for hardcoded secrets: AWS keys, GitHub tokens, OpenAI/Anthropic API keys, Stripe secrets, JWTs, database connection strings, and gene"},{"name":"count_code_lines","description":"Count lines of code: total, code lines, comment lines, blank lines, and comment density. Supports JS/TS, Python, Java/C/C++, Ruby, Go, Shell, HTML/XML, and CSS."},{"name":"lint_commit_message","description":"Validate a git commit message against the Conventional Commits spec (feat, fix, docs, style, refactor, test, chore, ci, perf, build). Returns compliance score, "},{"name":"word_frequency","description":"Analyze word frequency in text. Returns top N words with counts and percentages. Supports English stopword filtering. Useful for content analysis, keyword extra"},{"name":"extract_links","description":"Extract all URLs, email addresses, and domain names from text. Returns categorized and deduplicated results. Useful for content auditing, link checking, and web"},{"name":"levenshtein_distance","description":"Compute the Levenshtein (edit) distance and normalized similarity ratio between two strings. Supports batch comparison. Useful for fuzzy string matching, dedupl"},{"name":"json_diff","description":"Compute a deep structural diff between two JSON values. Returns added, removed, and changed keys with dot-notation paths. Like git diff but for JSON objects — p"},{"name":"merge_json","description":"Deep merge two JSON objects. Supports three array strategies: replace (default), concat, or unique (dedup concat). Nested objects are recursively merged — overr"},{"name":"json_to_yaml","description":"Convert a JSON object to clean, human-readable YAML. Handles nested objects, arrays, multiline strings, and special characters. No external dependencies."}],"toolCount":152,"toolsHash":"68f15982d31ca5609750a928a2af7afe6206d359ce0e29af4d06a5c860409d58","serverName":"ia-qa-toolbox","capabilities":["tools"],"serverVersion":"1.0.0","protocolVersion":"2025-06-18"}},{"at":"2026-10-10T06:26:47.930Z","kind":"mcp_initialize","ok":true,"httpStatus":200,"latencyMs":17,"error":null,"detail":{"tools":[{"name":"format_json","description":"Validate and pretty-print a string that is ALREADY valid JSON. Strict by design — it is a validity gate: valid JSON comes back formatted, anything else is rejec"},{"name":"generate_uuid","description":"Generate one or more cryptographically random UUID v4 identifiers. Use this when you need unique IDs for test fixtures, database records, session tokens, or any"},{"name":"hash_text","description":"Compute a cryptographic hash of a text string. Use when you need to verify data integrity, generate content fingerprints, hash passwords (prefer SHA-256+), or p"},{"name":"count_tokens","description":"Estimate the token count of a text string using the cl100k_base approximation (~4 chars/token). Call this BEFORE sending any text to an LLM API to check if it f"},{"name":"base64_encode","description":"Encode a UTF-8 string to Base64. Use when you need to embed binary data, multi-line text, or special characters safely inside JSON fields, HTTP headers, or data"},{"name":"base64_decode","description":"Decode a Base64 string back to UTF-8 text. Use for inspecting Base64-encoded API responses, JWT payload claims, config file values, or attachment data."},{"name":"url_encode","description":"Percent-encode a string for safe use in URLs. Call this before programmatically building query strings, path segments, or form-encoded bodies to prevent injecti"},{"name":"url_decode","description":"Decode a percent-encoded URL string back to plain text. Use when parsing query parameters from raw URLs or when displaying encoded values to users."},{"name":"generate_slug","description":"Convert any string into a URL-friendly slug: lowercase, ASCII-normalized (é→e), special characters removed, spaces replaced with hyphens. Use for generating SEO"},{"name":"validate_email","description":"Validate an email address against RFC 5322 syntax before storing it, sending a transactional email, or adding it to a mailing list. Returns { valid, email } — u"},{"name":"minify_js","description":"Minify a JavaScript snippet, function, class, or module up to 50 KB using Terser. Returns minified code and byte savings. Use when embedding scripts in HTML tem"},{"name":"decode_jwt","description":"Decode a JWT (JSON Web Token) and return its header and payload without verifying the signature. Also reports whether the token is expired and the exact expiry "},{"name":"text_stats","description":"Compute comprehensive statistics for any text: character count (with and without spaces), word count, line count, sentence count, paragraph count, and estimated"},{"name":"generate_password","description":"Generate a cryptographically secure random password using crypto.randomBytes. Configurable length (4–128), uppercase letters, digits, and symbols. Use when rese"},{"name":"parse_csv","description":"Parse a CSV string into a JSON array of objects (or raw arrays). Full RFC 4180: quoted fields may contain the delimiter, embedded newlines (the Excel/Sheets mul"},{"name":"color_convert","description":"Convert a color between HEX, RGB, and HSL formats. Use when translating design tokens between CSS notations, verifying color accessibility, or normalizing color"},{"name":"regex_test","description":"Test a regular expression pattern against an input string and return all matches with their index positions and named capture groups. Use for validating user in"},{"name":"lorem_ipsum","description":"Generate Lorem Ipsum placeholder text for UI mockups, design prototypes, or test data population. Configurable paragraphs (1–10), sentences per paragraph (1–20)"},{"name":"timestamp_convert","description":"Convert between Unix timestamps (seconds or milliseconds) and ISO-8601 / UTC date strings. Auto-detects epoch vs. millisecond format. Omit input to get the curr"},{"name":"diff_text","description":"Compute a unified line-by-line diff between two text strings (LCS algorithm). Returns added/removed/unchanged line counts and formatted diff hunks with configur"},{"name":"truncate_to_tokens","description":"Truncate text to at most N tokens (cl100k_base: ~4 chars/token) to avoid exceeding an LLM context window. Optionally keeps the end of the text instead of the st"},{"name":"split_chunks","description":"Split text into chunks of at most N tokens (cl100k_base: ~4 chars/token) with optional overlap. Designed for RAG ingestion pipelines."},{"name":"extract_json_from_text","description":"Extract the first valid JSON object or array embedded in chaotic LLM output (surrounded by markdown fences, prose, or explanatory text). Handles ```json blocks "},{"name":"strip_markdown","description":"Strip all Markdown formatting (headers, bold, italic, code fences, links, lists) from text and return clean plain text. Run this before injecting scraped docume"},{"name":"estimate_llm_cost","description":"Estimate the API cost in USD for a given model and token counts. Supports all major 2024–2026 models: GPT-4o, GPT-4.1, o3, o4-mini, Claude Opus 4, Claude Sonnet"},{"name":"escape_html","description":"Escape HTML special characters (&, <, >, \", ') to their safe HTML entities. ALWAYS call this before inserting any user-provided or LLM-generated content into an"},{"name":"unescape_html","description":"Convert HTML entities (&amp;, &lt;, &gt;, &quot;, &#x27;, and numeric &#NNN;) back to plain characters. Use when processing HTML-encoded text from APIs, email c"},{"name":"fetch_veille_feed","description":"Fetch the latest QA & AI/LLM articles aggregated from curated RSS sources (Google Testing Blog, DEV.to Testing/QA/AI/LLM/Agents, Hugging Face Blog, Simon Willis"},{"name":"score_geo_signals","description":"Analyze a webpage <head> HTML (or full HTML) for GEO (Generative Engine Optimization) signals. Returns a score /60 with per-check results and improvement tips. "},{"name":"extract_json_path","description":"Extract a value from a JSON string using dot-notation path (e.g., \"user.address.city\", \"items.0.name\", \"meta.tags\"). Supports array index access via numeric pat"},{"name":"generate_json_ld","description":"Generate a ready-to-paste <script type=\"application/ld+json\"> snippet for GEO / structured data optimization. Supported types: WebSite, FAQPage, Article, Person"},{"name":"analyze_diff_bugs","description":"Pattern-based diff linter: flags a fixed set of risky shapes in changed code — query-string interpolation (SQL/Cypher/Mongo injection shape), shell interpolatio"},{"name":"generate_test_cases","description":"Generate a set of test cases (valid, edge, invalid, pairwise) for a given feature description. Declared constraints drive the boundaries: a length or numeric bo"},{"name":"run_pr_gate_pipeline","description":"Review triage for a pull request. Takes a unified git diff (`git diff HEAD`) and returns: diff-lint findings with the lines that produced them, regression impac"},{"name":"validate_mcp_response","description":"Validate that an MCP tool response conforms to expected format, schema, and content rules. Use this to QA-test any MCP server tool. Supply the tool's actual JSO"},{"name":"llm_output_validator","description":"Validate an LLM response against QA criteria: format checks (JSON, code, markdown), content rules (must-include, must-not-include), length constraints, language"},{"name":"compare_responses","description":"Compare two ALREADY-PRODUCED outputs (e.g. model A vs model B on the same task) side by side. Returns deterministic metrics (token cosine, ROUGE-L, Jaccard, len"},{"name":"analyze_responses","description":"Semantically analyze N already-produced model outputs for the SAME task (the MCP counterpart to the LLM Sandbox). Without a reference: computes consensus — pair"},{"name":"prompt_test_suite","description":"Define a test suite for a prompt: provide the system prompt, user prompt, and expected output criteria. Returns a test plan with scored rubric — use this as inp"},{"name":"mcp_server_health_check","description":"Generate a health check report for an MCP server's tool manifest. Validates tool definitions, schema quality, naming conventions, and documentation completeness"},{"name":"mcp_server_evaluate","description":"Run a full compliance evaluation against a live MCP server URL. Tests: server reachability (ping), manifest discovery (GET /mcp), schema quality (snake_case nam"},{"name":"json_schema_validate","description":"Validate a JSON value against a JSON Schema (draft-07 subset). Supports type, required, properties, items, enum, const, pattern, format (email/uri/date), minimu"},{"name":"flatten_json","description":"Flatten a nested JSON object to single-level dot-notation keys (e.g. {\"a\":{\"b\":1}} → {\"a.b\":1}), or unflatten dot-notation keys back to a nested object. Support"},{"name":"xml_to_json","description":"Convert an XML string to a JSON object. Supports attributes, nested elements, arrays, CDATA, and namespaces. Options: parse numbers, parse booleans, ignore attr"},{"name":"redact_pii","description":"Automatically detect and redact Personally Identifiable Information (PII) from text. Replaces emails, phone numbers, SSNs, credit cards, IP addresses, and JWT t"},{"name":"mock_from_schema","description":"Generate realistic mock data from a JSON Schema. Supports all common types (string, number, integer, boolean, array, object, null), format hints (email, date, d"},{"name":"transform_json_array","description":"Transform a JSON array using common operations: pluck (extract specific fields), filter (by field value), sort_by (field), group_by (field), count_by (field), u"},{"name":"json_to_csv","description":"Convert a JSON array of objects to CSV format. Automatically detects columns from all object keys. Handles quoting and escaping per RFC 4180."},{"name":"case_convert","description":"Convert a string between naming conventions: camelCase, PascalCase, snake_case, kebab-case, UPPER_SNAKE_CASE, dot.case, Title Case. Essential for code generatio"},{"name":"sort_lines","description":"Sort, deduplicate, reverse, or filter lines of text. Useful for cleaning import lists, dependencies, log files, and config entries."},{"name":"number_base_convert","description":"Convert numbers between bases: decimal, binary, octal, hexadecimal, or any base 2–36. Auto-detects 0x, 0b, 0o prefixes."},{"name":"validate_url","description":"Parse and validate a URL. Returns decomposed components: protocol, hostname, port, path, query parameters, hash, and origin."},{"name":"check_contrast_ratio","description":"Calculate WCAG 2.1 contrast ratio between two colors. Returns ratio and compliance for AA/AAA normal and large text."},{"name":"html_to_markdown","description":"Convert HTML to clean Markdown. Strips scripts, styles, nav, ads, and comments. Converts headings, lists, links, images, code blocks. Ideal for preparing web co"},{"name":"cron_parse","description":"Parse a cron expression into a human-readable schedule description. Supports standard 5-field cron (minute hour day month weekday)."},{"name":"cron_validator","description":"Validate a 5-field cron expression, explain the schedule, and preview the next execution times. Use this to debug cron jobs before they reach production. Return"},{"name":"calculate_readability","description":"Calculate readability scores: Flesch Reading Ease, Flesch-Kincaid Grade Level, Coleman-Liau Index, and Automated Readability Index. Useful for evaluating LLM ou"},{"name":"normalize_whitespace","description":"Normalize whitespace: trim trailing spaces, collapse blank lines, normalize line endings (LF/CRLF), convert tabs to spaces. Useful for cleaning code, configs, a"},{"name":"embedding_similarity","description":"Compute text similarity using local algorithms (Bag of Words, TF-IDF, Character N-grams). No API key needed — runs entirely in-process. NOT real embeddings: for"},{"name":"llm_format_check","description":"Validate that an LLM output matches an expected format: JSON, Markdown, code block, bullet list, numbered list, table, YAML, XML, or custom regex. Essential for"},{"name":"hallucination_check","description":"Lexical hallucination check: verifies an LLM answer's words, numbers and polarity against the provided source/context. Fast, deterministic, no API key needed. E"},{"name":"prompt_injection_scan","description":"Scan user input or prompts for common prompt injection patterns. Detects system prompt overrides, jailbreak attempts, role manipulation, encoding tricks, delimi"},{"name":"token_budget_calculator","description":"Plan token allocation across system prompt, user input, context/RAG chunks, and expected output. Warns if budget exceeds model context window. Supports 25+ mode"},{"name":"consistency_check","description":"Compare multiple LLM responses to the same prompt and detect inconsistencies using Jaccard word-overlap similarity and fact drift (number comparison). Fast, det"},{"name":"llm_json_schema_check","description":"Validate that an LLM JSON output matches a JSON Schema definition. Tests required fields, types, enums, nested objects, and arrays. Critical for function-callin"},{"name":"latency_benchmark","description":"Measure response time of one or more HTTP endpoints (GET/POST). Runs N iterations and returns min/max/avg/p95 latency. Useful for API and MCP server benchmarkin"},{"name":"response_quality_score","description":"Score an LLM response against the criteria you pass: coverage of expected_keywords and compliance with max_length. Returns a 0-100 score over the criteria actua"},{"name":"rag_relevance_rank","description":"Rank an array of text chunks by relevance to a query using TF-IDF scoring. Simulates retrieval ranking for RAG testing without needing embeddings or an API."},{"name":"toxicity_scan","description":"Scan text for toxic language, hate speech, bias/stereotype framing, violence, sexual and self-harm content. Lexical + structural pattern matching (identity term"},{"name":"guardrail_test","description":"Test an LLM response against a set of guardrail rules: must-include, must-not-include, max length, required format, language, forbidden patterns, and custom reg"},{"name":"function_call_validate","description":"Validate an LLM function call / tool_use output: check that function name is in allowed list, arguments match expected schema, no extra/missing args. For OpenAI"},{"name":"conversation_analyze","description":"Analyze a multi-turn conversation for context retention, topic drift, instruction following, and repetition. Accepts messages array [{role, content}]. Essential"},{"name":"mcp_schema_lint","description":"Lint an MCP tool definition for best practices: naming conventions, description quality, schema completeness, required fields consistency, description length. R"},{"name":"cot_analyzer","description":"Analyze a Chain-of-Thought (CoT) or reasoning trace from an LLM. Detects step count, logical flow, conclusion presence, backtracking, and estimates reasoning de"},{"name":"ab_test_report","description":"Generate an A/B test report comparing two prompts or model configurations. Accepts arrays of scores and returns statistical comparison: mean, median, std deviat"},{"name":"context_window_check","description":"Given an array of message objects [{role, content}], estimate total token usage and check if it fits in the target model's context window. Warns about truncatio"},{"name":"vector_similarity","description":"Compute similarity/distance between two float vectors: cosine similarity, dot product, Euclidean and Manhattan distance. Essential for vector DB relevance scori"},{"name":"normalize_vector","description":"L2-normalize a float vector (produce a unit vector with norm=1). Required by many vector DBs (Pinecone, Qdrant cosine). Supports batch normalization of up to 10"},{"name":"vector_quantize","description":"Simulate int8 or int4 quantization of float32 embedding vectors. Reduces storage by 4x (int8) or 8x (int4). Returns quantized values, scale factor, and precisio"},{"name":"vector_stats","description":"Compute statistics for a float vector or matrix of vectors: mean, std, L2 norm, min, max, sparsity, top-K indices. Useful for debugging embedding quality and an"},{"name":"bm25_score","description":"Compute BM25 relevance score between a query and one or more documents. BM25 is the industry-standard keyword-based ranking algorithm used in Elasticsearch, Ope"},{"name":"build_rag_prompt","description":"Assemble a complete RAG (Retrieval-Augmented Generation) prompt from retrieved context chunks and a user query. Handles token budgeting, citation numbering, sys"},{"name":"prompt_template_fill","description":"Fill a prompt template with variables. Supports {{variable}} syntax and {{#if key}}...{{/if}} conditional blocks. Returns the filled prompt and lists unfilled v"},{"name":"few_shot_formatter","description":"Format few-shot examples for LLM prompts. Converts example pairs into formatted blocks. Supports chat format (User/Assistant), XML tags, Markdown, or plain text"},{"name":"system_prompt_builder","description":"Build a structured system prompt from components: role, task, constraints, output format, tone, language, and examples. Generates a production-ready system prom"},{"name":"model_info","description":"Get detailed specs for an AI model: context window, pricing per 1K tokens, knowledge cutoff, provider, multimodal support, reasoning capabilities, and feature l"},{"name":"compare_models","description":"Compare 2-5 AI models side by side: context window, pricing, multimodal, reasoning capabilities, and provider. Returns a comparison table with a recommendation "},{"name":"http_status_lookup","description":"Look up detailed information about any HTTP status code: class, name, description, cacheability, typical causes, and handling best practices. Covers every code "},{"name":"parse_http_headers","description":"Parse a raw HTTP headers block into a structured JSON object. Detects multi-value headers, masks Authorization values, and optionally audits for missing securit"},{"name":"generate_curl","description":"Generate a curl command from request parameters. Supports GET/POST/PUT/DELETE, custom headers, JSON body, and form data. Useful for documentation, sharing, and "},{"name":"extract_todos","description":"Extract TODO, FIXME, HACK, BUG, NOTE, OPTIMIZE, and custom tags from any source code or text. Returns line numbers, tag types, and message text. Essential for t"},{"name":"detect_secrets","description":"Scan code or config files for hardcoded secrets: AWS keys, GitHub tokens, OpenAI/Anthropic API keys, Stripe secrets, JWTs, database connection strings, and gene"},{"name":"count_code_lines","description":"Count lines of code: total, code lines, comment lines, blank lines, and comment density. Supports JS/TS, Python, Java/C/C++, Ruby, Go, Shell, HTML/XML, and CSS."},{"name":"lint_commit_message","description":"Validate a git commit message against the Conventional Commits spec (feat, fix, docs, style, refactor, test, chore, ci, perf, build). Returns compliance score, "},{"name":"word_frequency","description":"Analyze word frequency in text. Returns top N words with counts and percentages. Supports English stopword filtering. Useful for content analysis, keyword extra"},{"name":"extract_links","description":"Extract all URLs, email addresses, and domain names from text. Returns categorized and deduplicated results. Useful for content auditing, link checking, and web"},{"name":"levenshtein_distance","description":"Compute the Levenshtein (edit) distance and normalized similarity ratio between two strings. Supports batch comparison. Useful for fuzzy string matching, dedupl"},{"name":"json_diff","description":"Compute a deep structural diff between two JSON values. Returns added, removed, and changed keys with dot-notation paths. Like git diff but for JSON objects — p"},{"name":"merge_json","description":"Deep merge two JSON objects. Supports three array strategies: replace (default), concat, or unique (dedup concat). Nested objects are recursively merged — overr"},{"name":"json_to_yaml","description":"Convert a JSON object to clean, human-readable YAML. Handles nested objects, arrays, multiline strings, and special characters. No external dependencies."}],"toolCount":152,"toolsHash":"68f15982d31ca5609750a928a2af7afe6206d359ce0e29af4d06a5c860409d58","serverName":"ia-qa-toolbox","capabilities":["tools"],"serverVersion":"1.0.0","protocolVersion":"2025-06-18"}},{"at":"2026-10-09T23:30:53.860Z","kind":"mcp_initialize","ok":true,"httpStatus":200,"latencyMs":20,"error":null,"detail":{"tools":[{"name":"format_json","description":"Validate and pretty-print a string that is ALREADY valid JSON. Strict by design — it is a validity gate: valid JSON comes back formatted, anything else is rejec"},{"name":"generate_uuid","description":"Generate one or more cryptographically random UUID v4 identifiers. Use this when you need unique IDs for test fixtures, database records, session tokens, or any"},{"name":"hash_text","description":"Compute a cryptographic hash of a text string. Use when you need to verify data integrity, generate content fingerprints, hash passwords (prefer SHA-256+), or p"},{"name":"count_tokens","description":"Estimate the token count of a text string using the cl100k_base approximation (~4 chars/token). Call this BEFORE sending any text to an LLM API to check if it f"},{"name":"base64_encode","description":"Encode a UTF-8 string to Base64. Use when you need to embed binary data, multi-line text, or special characters safely inside JSON fields, HTTP headers, or data"},{"name":"base64_decode","description":"Decode a Base64 string back to UTF-8 text. Use for inspecting Base64-encoded API responses, JWT payload claims, config file values, or attachment data."},{"name":"url_encode","description":"Percent-encode a string for safe use in URLs. Call this before programmatically building query strings, path segments, or form-encoded bodies to prevent injecti"},{"name":"url_decode","description":"Decode a percent-encoded URL string back to plain text. Use when parsing query parameters from raw URLs or when displaying encoded values to users."},{"name":"generate_slug","description":"Convert any string into a URL-friendly slug: lowercase, ASCII-normalized (é→e), special characters removed, spaces replaced with hyphens. Use for generating SEO"},{"name":"validate_email","description":"Validate an email address against RFC 5322 syntax before storing it, sending a transactional email, or adding it to a mailing list. Returns { valid, email } — u"},{"name":"minify_js","description":"Minify a JavaScript snippet, function, class, or module up to 50 KB using Terser. Returns minified code and byte savings. Use when embedding scripts in HTML tem"},{"name":"decode_jwt","description":"Decode a JWT (JSON Web Token) and return its header and payload without verifying the signature. Also reports whether the token is expired and the exact expiry "},{"name":"text_stats","description":"Compute comprehensive statistics for any text: character count (with and without spaces), word count, line count, sentence count, paragraph count, and estimated"},{"name":"generate_password","description":"Generate a cryptographically secure random password using crypto.randomBytes. Configurable length (4–128), uppercase letters, digits, and symbols. Use when rese"},{"name":"parse_csv","description":"Parse a CSV string into a JSON array of objects (or raw arrays). Full RFC 4180: quoted fields may contain the delimiter, embedded newlines (the Excel/Sheets mul"},{"name":"color_convert","description":"Convert a color between HEX, RGB, and HSL formats. Use when translating design tokens between CSS notations, verifying color accessibility, or normalizing color"},{"name":"regex_test","description":"Test a regular expression pattern against an input string and return all matches with their index positions and named capture groups. Use for validating user in"},{"name":"lorem_ipsum","description":"Generate Lorem Ipsum placeholder text for UI mockups, design prototypes, or test data population. Configurable paragraphs (1–10), sentences per paragraph (1–20)"},{"name":"timestamp_convert","description":"Convert between Unix timestamps (seconds or milliseconds) and ISO-8601 / UTC date strings. Auto-detects epoch vs. millisecond format. Omit input to get the curr"},{"name":"diff_text","description":"Compute a unified line-by-line diff between two text strings (LCS algorithm). Returns added/removed/unchanged line counts and formatted diff hunks with configur"},{"name":"truncate_to_tokens","description":"Truncate text to at most N tokens (cl100k_base: ~4 chars/token) to avoid exceeding an LLM context window. Optionally keeps the end of the text instead of the st"},{"name":"split_chunks","description":"Split text into chunks of at most N tokens (cl100k_base: ~4 chars/token) with optional overlap. Designed for RAG ingestion pipelines."},{"name":"extract_json_from_text","description":"Extract the first valid JSON object or array embedded in chaotic LLM output (surrounded by markdown fences, prose, or explanatory text). Handles ```json blocks "},{"name":"strip_markdown","description":"Strip all Markdown formatting (headers, bold, italic, code fences, links, lists) from text and return clean plain text. Run this before injecting scraped docume"},{"name":"estimate_llm_cost","description":"Estimate the API cost in USD for a given model and token counts. Supports all major 2024–2026 models: GPT-4o, GPT-4.1, o3, o4-mini, Claude Opus 4, Claude Sonnet"},{"name":"escape_html","description":"Escape HTML special characters (&, <, >, \", ') to their safe HTML entities. ALWAYS call this before inserting any user-provided or LLM-generated content into an"},{"name":"unescape_html","description":"Convert HTML entities (&amp;, &lt;, &gt;, &quot;, &#x27;, and numeric &#NNN;) back to plain characters. Use when processing HTML-encoded text from APIs, email c"},{"name":"fetch_veille_feed","description":"Fetch the latest QA & AI/LLM articles aggregated from curated RSS sources (Google Testing Blog, DEV.to Testing/QA/AI/LLM/Agents, Hugging Face Blog, Simon Willis"},{"name":"score_geo_signals","description":"Analyze a webpage <head> HTML (or full HTML) for GEO (Generative Engine Optimization) signals. Returns a score /60 with per-check results and improvement tips. "},{"name":"extract_json_path","description":"Extract a value from a JSON string using dot-notation path (e.g., \"user.address.city\", \"items.0.name\", \"meta.tags\"). Supports array index access via numeric pat"},{"name":"generate_json_ld","description":"Generate a ready-to-paste <script type=\"application/ld+json\"> snippet for GEO / structured data optimization. Supported types: WebSite, FAQPage, Article, Person"},{"name":"analyze_diff_bugs","description":"Pattern-based diff linter: flags a fixed set of risky shapes in changed code — query-string interpolation (SQL/Cypher/Mongo injection shape), shell interpolatio"},{"name":"generate_test_cases","description":"Generate a set of test cases (valid, edge, invalid, pairwise) for a given feature description. Declared constraints drive the boundaries: a length or numeric bo"},{"name":"run_pr_gate_pipeline","description":"Review triage for a pull request. Takes a unified git diff (`git diff HEAD`) and returns: diff-lint findings with the lines that produced them, regression impac"},{"name":"validate_mcp_response","description":"Validate that an MCP tool response conforms to expected format, schema, and content rules. Use this to QA-test any MCP server tool. Supply the tool's actual JSO"},{"name":"llm_output_validator","description":"Validate an LLM response against QA criteria: format checks (JSON, code, markdown), content rules (must-include, must-not-include), length constraints, language"},{"name":"compare_responses","description":"Compare two ALREADY-PRODUCED outputs (e.g. model A vs model B on the same task) side by side. Returns deterministic metrics (token cosine, ROUGE-L, Jaccard, len"},{"name":"analyze_responses","description":"Semantically analyze N already-produced model outputs for the SAME task (the MCP counterpart to the LLM Sandbox). Without a reference: computes consensus — pair"},{"name":"prompt_test_suite","description":"Define a test suite for a prompt: provide the system prompt, user prompt, and expected output criteria. Returns a test plan with scored rubric — use this as inp"},{"name":"mcp_server_health_check","description":"Generate a health check report for an MCP server's tool manifest. Validates tool definitions, schema quality, naming conventions, and documentation completeness"},{"name":"mcp_server_evaluate","description":"Run a full compliance evaluation against a live MCP server URL. Tests: server reachability (ping), manifest discovery (GET /mcp), schema quality (snake_case nam"},{"name":"json_schema_validate","description":"Validate a JSON value against a JSON Schema (draft-07 subset). Supports type, required, properties, items, enum, const, pattern, format (email/uri/date), minimu"},{"name":"flatten_json","description":"Flatten a nested JSON object to single-level dot-notation keys (e.g. {\"a\":{\"b\":1}} → {\"a.b\":1}), or unflatten dot-notation keys back to a nested object. Support"},{"name":"xml_to_json","description":"Convert an XML string to a JSON object. Supports attributes, nested elements, arrays, CDATA, and namespaces. Options: parse numbers, parse booleans, ignore attr"},{"name":"redact_pii","description":"Automatically detect and redact Personally Identifiable Information (PII) from text. Replaces emails, phone numbers, SSNs, credit cards, IP addresses, and JWT t"},{"name":"mock_from_schema","description":"Generate realistic mock data from a JSON Schema. Supports all common types (string, number, integer, boolean, array, object, null), format hints (email, date, d"},{"name":"transform_json_array","description":"Transform a JSON array using common operations: pluck (extract specific fields), filter (by field value), sort_by (field), group_by (field), count_by (field), u"},{"name":"json_to_csv","description":"Convert a JSON array of objects to CSV format. Automatically detects columns from all object keys. Handles quoting and escaping per RFC 4180."},{"name":"case_convert","description":"Convert a string between naming conventions: camelCase, PascalCase, snake_case, kebab-case, UPPER_SNAKE_CASE, dot.case, Title Case. Essential for code generatio"},{"name":"sort_lines","description":"Sort, deduplicate, reverse, or filter lines of text. Useful for cleaning import lists, dependencies, log files, and config entries."},{"name":"number_base_convert","description":"Convert numbers between bases: decimal, binary, octal, hexadecimal, or any base 2–36. Auto-detects 0x, 0b, 0o prefixes."},{"name":"validate_url","description":"Parse and validate a URL. Returns decomposed components: protocol, hostname, port, path, query parameters, hash, and origin."},{"name":"check_contrast_ratio","description":"Calculate WCAG 2.1 contrast ratio between two colors. Returns ratio and compliance for AA/AAA normal and large text."},{"name":"html_to_markdown","description":"Convert HTML to clean Markdown. Strips scripts, styles, nav, ads, and comments. Converts headings, lists, links, images, code blocks. Ideal for preparing web co"},{"name":"cron_parse","description":"Parse a cron expression into a human-readable schedule description. Supports standard 5-field cron (minute hour day month weekday)."},{"name":"cron_validator","description":"Validate a 5-field cron expression, explain the schedule, and preview the next execution times. Use this to debug cron jobs before they reach production. Return"},{"name":"calculate_readability","description":"Calculate readability scores: Flesch Reading Ease, Flesch-Kincaid Grade Level, Coleman-Liau Index, and Automated Readability Index. Useful for evaluating LLM ou"},{"name":"normalize_whitespace","description":"Normalize whitespace: trim trailing spaces, collapse blank lines, normalize line endings (LF/CRLF), convert tabs to spaces. Useful for cleaning code, configs, a"},{"name":"embedding_similarity","description":"Compute text similarity using local algorithms (Bag of Words, TF-IDF, Character N-grams). No API key needed — runs entirely in-process. NOT real embeddings: for"},{"name":"llm_format_check","description":"Validate that an LLM output matches an expected format: JSON, Markdown, code block, bullet list, numbered list, table, YAML, XML, or custom regex. Essential for"},{"name":"hallucination_check","description":"Lexical hallucination check: verifies an LLM answer's words, numbers and polarity against the provided source/context. Fast, deterministic, no API key needed. E"},{"name":"prompt_injection_scan","description":"Scan user input or prompts for common prompt injection patterns. Detects system prompt overrides, jailbreak attempts, role manipulation, encoding tricks, delimi"},{"name":"token_budget_calculator","description":"Plan token allocation across system prompt, user input, context/RAG chunks, and expected output. Warns if budget exceeds model context window. Supports 25+ mode"},{"name":"consistency_check","description":"Compare multiple LLM responses to the same prompt and detect inconsistencies using Jaccard word-overlap similarity and fact drift (number comparison). Fast, det"},{"name":"llm_json_schema_check","description":"Validate that an LLM JSON output matches a JSON Schema definition. Tests required fields, types, enums, nested objects, and arrays. Critical for function-callin"},{"name":"latency_benchmark","description":"Measure response time of one or more HTTP endpoints (GET/POST). Runs N iterations and returns min/max/avg/p95 latency. Useful for API and MCP server benchmarkin"},{"name":"response_quality_score","description":"Score an LLM response against the criteria you pass: coverage of expected_keywords and compliance with max_length. Returns a 0-100 score over the criteria actua"},{"name":"rag_relevance_rank","description":"Rank an array of text chunks by relevance to a query using TF-IDF scoring. Simulates retrieval ranking for RAG testing without needing embeddings or an API."},{"name":"toxicity_scan","description":"Scan text for toxic language, hate speech, bias/stereotype framing, violence, sexual and self-harm content. Lexical + structural pattern matching (identity term"},{"name":"guardrail_test","description":"Test an LLM response against a set of guardrail rules: must-include, must-not-include, max length, required format, language, forbidden patterns, and custom reg"},{"name":"function_call_validate","description":"Validate an LLM function call / tool_use output: check that function name is in allowed list, arguments match expected schema, no extra/missing args. For OpenAI"},{"name":"conversation_analyze","description":"Analyze a multi-turn conversation for context retention, topic drift, instruction following, and repetition. Accepts messages array [{role, content}]. Essential"},{"name":"mcp_schema_lint","description":"Lint an MCP tool definition for best practices: naming conventions, description quality, schema completeness, required fields consistency, description length. R"},{"name":"cot_analyzer","description":"Analyze a Chain-of-Thought (CoT) or reasoning trace from an LLM. Detects step count, logical flow, conclusion presence, backtracking, and estimates reasoning de"},{"name":"ab_test_report","description":"Generate an A/B test report comparing two prompts or model configurations. Accepts arrays of scores and returns statistical comparison: mean, median, std deviat"},{"name":"context_window_check","description":"Given an array of message objects [{role, content}], estimate total token usage and check if it fits in the target model's context window. Warns about truncatio"},{"name":"vector_similarity","description":"Compute similarity/distance between two float vectors: cosine similarity, dot product, Euclidean and Manhattan distance. Essential for vector DB relevance scori"},{"name":"normalize_vector","description":"L2-normalize a float vector (produce a unit vector with norm=1). Required by many vector DBs (Pinecone, Qdrant cosine). Supports batch normalization of up to 10"},{"name":"vector_quantize","description":"Simulate int8 or int4 quantization of float32 embedding vectors. Reduces storage by 4x (int8) or 8x (int4). Returns quantized values, scale factor, and precisio"},{"name":"vector_stats","description":"Compute statistics for a float vector or matrix of vectors: mean, std, L2 norm, min, max, sparsity, top-K indices. Useful for debugging embedding quality and an"},{"name":"bm25_score","description":"Compute BM25 relevance score between a query and one or more documents. BM25 is the industry-standard keyword-based ranking algorithm used in Elasticsearch, Ope"},{"name":"build_rag_prompt","description":"Assemble a complete RAG (Retrieval-Augmented Generation) prompt from retrieved context chunks and a user query. Handles token budgeting, citation numbering, sys"},{"name":"prompt_template_fill","description":"Fill a prompt template with variables. Supports {{variable}} syntax and {{#if key}}...{{/if}} conditional blocks. Returns the filled prompt and lists unfilled v"},{"name":"few_shot_formatter","description":"Format few-shot examples for LLM prompts. Converts example pairs into formatted blocks. Supports chat format (User/Assistant), XML tags, Markdown, or plain text"},{"name":"system_prompt_builder","description":"Build a structured system prompt from components: role, task, constraints, output format, tone, language, and examples. Generates a production-ready system prom"},{"name":"model_info","description":"Get detailed specs for an AI model: context window, pricing per 1K tokens, knowledge cutoff, provider, multimodal support, reasoning capabilities, and feature l"},{"name":"compare_models","description":"Compare 2-5 AI models side by side: context window, pricing, multimodal, reasoning capabilities, and provider. Returns a comparison table with a recommendation "},{"name":"http_status_lookup","description":"Look up detailed information about any HTTP status code: class, name, description, cacheability, typical causes, and handling best practices. Covers every code "},{"name":"parse_http_headers","description":"Parse a raw HTTP headers block into a structured JSON object. Detects multi-value headers, masks Authorization values, and optionally audits for missing securit"},{"name":"generate_curl","description":"Generate a curl command from request parameters. Supports GET/POST/PUT/DELETE, custom headers, JSON body, and form data. Useful for documentation, sharing, and "},{"name":"extract_todos","description":"Extract TODO, FIXME, HACK, BUG, NOTE, OPTIMIZE, and custom tags from any source code or text. Returns line numbers, tag types, and message text. Essential for t"},{"name":"detect_secrets","description":"Scan code or config files for hardcoded secrets: AWS keys, GitHub tokens, OpenAI/Anthropic API keys, Stripe secrets, JWTs, database connection strings, and gene"},{"name":"count_code_lines","description":"Count lines of code: total, code lines, comment lines, blank lines, and comment density. Supports JS/TS, Python, Java/C/C++, Ruby, Go, Shell, HTML/XML, and CSS."},{"name":"lint_commit_message","description":"Validate a git commit message against the Conventional Commits spec (feat, fix, docs, style, refactor, test, chore, ci, perf, build). Returns compliance score, "},{"name":"word_frequency","description":"Analyze word frequency in text. Returns top N words with counts and percentages. Supports English stopword filtering. Useful for content analysis, keyword extra"},{"name":"extract_links","description":"Extract all URLs, email addresses, and domain names from text. Returns categorized and deduplicated results. Useful for content auditing, link checking, and web"},{"name":"levenshtein_distance","description":"Compute the Levenshtein (edit) distance and normalized similarity ratio between two strings. Supports batch comparison. Useful for fuzzy string matching, dedupl"},{"name":"json_diff","description":"Compute a deep structural diff between two JSON values. Returns added, removed, and changed keys with dot-notation paths. Like git diff but for JSON objects — p"},{"name":"merge_json","description":"Deep merge two JSON objects. Supports three array strategies: replace (default), concat, or unique (dedup concat). Nested objects are recursively merged — overr"},{"name":"json_to_yaml","description":"Convert a JSON object to clean, human-readable YAML. Handles nested objects, arrays, multiline strings, and special characters. No external dependencies."}],"toolCount":152,"toolsHash":"68f15982d31ca5609750a928a2af7afe6206d359ce0e29af4d06a5c860409d58","serverName":"ia-qa-toolbox","capabilities":["tools"],"serverVersion":"1.0.0","protocolVersion":"2025-06-18"}},{"at":"2026-10-09T17:28:07.585Z","kind":"mcp_initialize","ok":true,"httpStatus":200,"latencyMs":39,"error":null,"detail":{"tools":[{"name":"format_json","description":"Validate and pretty-print a string that is ALREADY valid JSON. Strict by design — it is a validity gate: valid JSON comes back formatted, anything else is rejec"},{"name":"generate_uuid","description":"Generate one or more cryptographically random UUID v4 identifiers. Use this when you need unique IDs for test fixtures, database records, session tokens, or any"},{"name":"hash_text","description":"Compute a cryptographic hash of a text string. Use when you need to verify data integrity, generate content fingerprints, hash passwords (prefer SHA-256+), or p"},{"name":"count_tokens","description":"Estimate the token count of a text string using the cl100k_base approximation (~4 chars/token). Call this BEFORE sending any text to an LLM API to check if it f"},{"name":"base64_encode","description":"Encode a UTF-8 string to Base64. Use when you need to embed binary data, multi-line text, or special characters safely inside JSON fields, HTTP headers, or data"},{"name":"base64_decode","description":"Decode a Base64 string back to UTF-8 text. Use for inspecting Base64-encoded API responses, JWT payload claims, config file values, or attachment data."},{"name":"url_encode","description":"Percent-encode a string for safe use in URLs. Call this before programmatically building query strings, path segments, or form-encoded bodies to prevent injecti"},{"name":"url_decode","description":"Decode a percent-encoded URL string back to plain text. Use when parsing query parameters from raw URLs or when displaying encoded values to users."},{"name":"generate_slug","description":"Convert any string into a URL-friendly slug: lowercase, ASCII-normalized (é→e), special characters removed, spaces replaced with hyphens. Use for generating SEO"},{"name":"validate_email","description":"Validate an email address against RFC 5322 syntax before storing it, sending a transactional email, or adding it to a mailing list. Returns { valid, email } — u"},{"name":"minify_js","description":"Minify a JavaScript snippet, function, class, or module up to 50 KB using Terser. Returns minified code and byte savings. Use when embedding scripts in HTML tem"},{"name":"decode_jwt","description":"Decode a JWT (JSON Web Token) and return its header and payload without verifying the signature. Also reports whether the token is expired and the exact expiry "},{"name":"text_stats","description":"Compute comprehensive statistics for any text: character count (with and without spaces), word count, line count, sentence count, paragraph count, and estimated"},{"name":"generate_password","description":"Generate a cryptographically secure random password using crypto.randomBytes. Configurable length (4–128), uppercase letters, digits, and symbols. Use when rese"},{"name":"parse_csv","description":"Parse a CSV string into a JSON array of objects (or raw arrays). Full RFC 4180: quoted fields may contain the delimiter, embedded newlines (the Excel/Sheets mul"},{"name":"color_convert","description":"Convert a color between HEX, RGB, and HSL formats. Use when translating design tokens between CSS notations, verifying color accessibility, or normalizing color"},{"name":"regex_test","description":"Test a regular expression pattern against an input string and return all matches with their index positions and named capture groups. Use for validating user in"},{"name":"lorem_ipsum","description":"Generate Lorem Ipsum placeholder text for UI mockups, design prototypes, or test data population. Configurable paragraphs (1–10), sentences per paragraph (1–20)"},{"name":"timestamp_convert","description":"Convert between Unix timestamps (seconds or milliseconds) and ISO-8601 / UTC date strings. Auto-detects epoch vs. millisecond format. Omit input to get the curr"},{"name":"diff_text","description":"Compute a unified line-by-line diff between two text strings (LCS algorithm). Returns added/removed/unchanged line counts and formatted diff hunks with configur"},{"name":"truncate_to_tokens","description":"Truncate text to at most N tokens (cl100k_base: ~4 chars/token) to avoid exceeding an LLM context window. Optionally keeps the end of the text instead of the st"},{"name":"split_chunks","description":"Split text into chunks of at most N tokens (cl100k_base: ~4 chars/token) with optional overlap. Designed for RAG ingestion pipelines."},{"name":"extract_json_from_text","description":"Extract the first valid JSON object or array embedded in chaotic LLM output (surrounded by markdown fences, prose, or explanatory text). Handles ```json blocks "},{"name":"strip_markdown","description":"Strip all Markdown formatting (headers, bold, italic, code fences, links, lists) from text and return clean plain text. Run this before injecting scraped docume"},{"name":"estimate_llm_cost","description":"Estimate the API cost in USD for a given model and token counts. Supports all major 2024–2026 models: GPT-4o, GPT-4.1, o3, o4-mini, Claude Opus 4, Claude Sonnet"},{"name":"escape_html","description":"Escape HTML special characters (&, <, >, \", ') to their safe HTML entities. ALWAYS call this before inserting any user-provided or LLM-generated content into an"},{"name":"unescape_html","description":"Convert HTML entities (&amp;, &lt;, &gt;, &quot;, &#x27;, and numeric &#NNN;) back to plain characters. Use when processing HTML-encoded text from APIs, email c"},{"name":"fetch_veille_feed","description":"Fetch the latest QA & AI/LLM articles aggregated from curated RSS sources (Google Testing Blog, DEV.to Testing/QA/AI/LLM/Agents, Hugging Face Blog, Simon Willis"},{"name":"score_geo_signals","description":"Analyze a webpage <head> HTML (or full HTML) for GEO (Generative Engine Optimization) signals. Returns a score /60 with per-check results and improvement tips. "},{"name":"extract_json_path","description":"Extract a value from a JSON string using dot-notation path (e.g., \"user.address.city\", \"items.0.name\", \"meta.tags\"). Supports array index access via numeric pat"},{"name":"generate_json_ld","description":"Generate a ready-to-paste <script type=\"application/ld+json\"> snippet for GEO / structured data optimization. Supported types: WebSite, FAQPage, Article, Person"},{"name":"analyze_diff_bugs","description":"Pattern-based diff linter: flags a fixed set of risky shapes in changed code — query-string interpolation (SQL/Cypher/Mongo injection shape), shell interpolatio"},{"name":"generate_test_cases","description":"Generate a set of test cases (valid, edge, invalid, pairwise) for a given feature description. Declared constraints drive the boundaries: a length or numeric bo"},{"name":"run_pr_gate_pipeline","description":"Review triage for a pull request. Takes a unified git diff (`git diff HEAD`) and returns: diff-lint findings with the lines that produced them, regression impac"},{"name":"validate_mcp_response","description":"Validate that an MCP tool response conforms to expected format, schema, and content rules. Use this to QA-test any MCP server tool. Supply the tool's actual JSO"},{"name":"llm_output_validator","description":"Validate an LLM response against QA criteria: format checks (JSON, code, markdown), content rules (must-include, must-not-include), length constraints, language"},{"name":"compare_responses","description":"Compare two ALREADY-PRODUCED outputs (e.g. model A vs model B on the same task) side by side. Returns deterministic metrics (token cosine, ROUGE-L, Jaccard, len"},{"name":"analyze_responses","description":"Semantically analyze N already-produced model outputs for the SAME task (the MCP counterpart to the LLM Sandbox). Without a reference: computes consensus — pair"},{"name":"prompt_test_suite","description":"Define a test suite for a prompt: provide the system prompt, user prompt, and expected output criteria. Returns a test plan with scored rubric — use this as inp"},{"name":"mcp_server_health_check","description":"Generate a health check report for an MCP server's tool manifest. Validates tool definitions, schema quality, naming conventions, and documentation completeness"},{"name":"mcp_server_evaluate","description":"Run a full compliance evaluation against a live MCP server URL. Tests: server reachability (ping), manifest discovery (GET /mcp), schema quality (snake_case nam"},{"name":"json_schema_validate","description":"Validate a JSON value against a JSON Schema (draft-07 subset). Supports type, required, properties, items, enum, const, pattern, format (email/uri/date), minimu"},{"name":"flatten_json","description":"Flatten a nested JSON object to single-level dot-notation keys (e.g. {\"a\":{\"b\":1}} → {\"a.b\":1}), or unflatten dot-notation keys back to a nested object. Support"},{"name":"xml_to_json","description":"Convert an XML string to a JSON object. Supports attributes, nested elements, arrays, CDATA, and namespaces. Options: parse numbers, parse booleans, ignore attr"},{"name":"redact_pii","description":"Automatically detect and redact Personally Identifiable Information (PII) from text. Replaces emails, phone numbers, SSNs, credit cards, IP addresses, and JWT t"},{"name":"mock_from_schema","description":"Generate realistic mock data from a JSON Schema. Supports all common types (string, number, integer, boolean, array, object, null), format hints (email, date, d"},{"name":"transform_json_array","description":"Transform a JSON array using common operations: pluck (extract specific fields), filter (by field value), sort_by (field), group_by (field), count_by (field), u"},{"name":"json_to_csv","description":"Convert a JSON array of objects to CSV format. Automatically detects columns from all object keys. Handles quoting and escaping per RFC 4180."},{"name":"case_convert","description":"Convert a string between naming conventions: camelCase, PascalCase, snake_case, kebab-case, UPPER_SNAKE_CASE, dot.case, Title Case. Essential for code generatio"},{"name":"sort_lines","description":"Sort, deduplicate, reverse, or filter lines of text. Useful for cleaning import lists, dependencies, log files, and config entries."},{"name":"number_base_convert","description":"Convert numbers between bases: decimal, binary, octal, hexadecimal, or any base 2–36. Auto-detects 0x, 0b, 0o prefixes."},{"name":"validate_url","description":"Parse and validate a URL. Returns decomposed components: protocol, hostname, port, path, query parameters, hash, and origin."},{"name":"check_contrast_ratio","description":"Calculate WCAG 2.1 contrast ratio between two colors. Returns ratio and compliance for AA/AAA normal and large text."},{"name":"html_to_markdown","description":"Convert HTML to clean Markdown. Strips scripts, styles, nav, ads, and comments. Converts headings, lists, links, images, code blocks. Ideal for preparing web co"},{"name":"cron_parse","description":"Parse a cron expression into a human-readable schedule description. Supports standard 5-field cron (minute hour day month weekday)."},{"name":"cron_validator","description":"Validate a 5-field cron expression, explain the schedule, and preview the next execution times. Use this to debug cron jobs before they reach production. Return"},{"name":"calculate_readability","description":"Calculate readability scores: Flesch Reading Ease, Flesch-Kincaid Grade Level, Coleman-Liau Index, and Automated Readability Index. Useful for evaluating LLM ou"},{"name":"normalize_whitespace","description":"Normalize whitespace: trim trailing spaces, collapse blank lines, normalize line endings (LF/CRLF), convert tabs to spaces. Useful for cleaning code, configs, a"},{"name":"embedding_similarity","description":"Compute text similarity using local algorithms (Bag of Words, TF-IDF, Character N-grams). No API key needed — runs entirely in-process. NOT real embeddings: for"},{"name":"llm_format_check","description":"Validate that an LLM output matches an expected format: JSON, Markdown, code block, bullet list, numbered list, table, YAML, XML, or custom regex. Essential for"},{"name":"hallucination_check","description":"Lexical hallucination check: verifies an LLM answer's words, numbers and polarity against the provided source/context. Fast, deterministic, no API key needed. E"},{"name":"prompt_injection_scan","description":"Scan user input or prompts for common prompt injection patterns. Detects system prompt overrides, jailbreak attempts, role manipulation, encoding tricks, delimi"},{"name":"token_budget_calculator","description":"Plan token allocation across system prompt, user input, context/RAG chunks, and expected output. Warns if budget exceeds model context window. Supports 25+ mode"},{"name":"consistency_check","description":"Compare multiple LLM responses to the same prompt and detect inconsistencies using Jaccard word-overlap similarity and fact drift (number comparison). Fast, det"},{"name":"llm_json_schema_check","description":"Validate that an LLM JSON output matches a JSON Schema definition. Tests required fields, types, enums, nested objects, and arrays. Critical for function-callin"},{"name":"latency_benchmark","description":"Measure response time of one or more HTTP endpoints (GET/POST). Runs N iterations and returns min/max/avg/p95 latency. Useful for API and MCP server benchmarkin"},{"name":"response_quality_score","description":"Score an LLM response against the criteria you pass: coverage of expected_keywords and compliance with max_length. Returns a 0-100 score over the criteria actua"},{"name":"rag_relevance_rank","description":"Rank an array of text chunks by relevance to a query using TF-IDF scoring. Simulates retrieval ranking for RAG testing without needing embeddings or an API."},{"name":"toxicity_scan","description":"Scan text for toxic language, hate speech, bias/stereotype framing, violence, sexual and self-harm content. Lexical + structural pattern matching (identity term"},{"name":"guardrail_test","description":"Test an LLM response against a set of guardrail rules: must-include, must-not-include, max length, required format, language, forbidden patterns, and custom reg"},{"name":"function_call_validate","description":"Validate an LLM function call / tool_use output: check that function name is in allowed list, arguments match expected schema, no extra/missing args. For OpenAI"},{"name":"conversation_analyze","description":"Analyze a multi-turn conversation for context retention, topic drift, instruction following, and repetition. Accepts messages array [{role, content}]. Essential"},{"name":"mcp_schema_lint","description":"Lint an MCP tool definition for best practices: naming conventions, description quality, schema completeness, required fields consistency, description length. R"},{"name":"cot_analyzer","description":"Analyze a Chain-of-Thought (CoT) or reasoning trace from an LLM. Detects step count, logical flow, conclusion presence, backtracking, and estimates reasoning de"},{"name":"ab_test_report","description":"Generate an A/B test report comparing two prompts or model configurations. Accepts arrays of scores and returns statistical comparison: mean, median, std deviat"},{"name":"context_window_check","description":"Given an array of message objects [{role, content}], estimate total token usage and check if it fits in the target model's context window. Warns about truncatio"},{"name":"vector_similarity","description":"Compute similarity/distance between two float vectors: cosine similarity, dot product, Euclidean and Manhattan distance. Essential for vector DB relevance scori"},{"name":"normalize_vector","description":"L2-normalize a float vector (produce a unit vector with norm=1). Required by many vector DBs (Pinecone, Qdrant cosine). Supports batch normalization of up to 10"},{"name":"vector_quantize","description":"Simulate int8 or int4 quantization of float32 embedding vectors. Reduces storage by 4x (int8) or 8x (int4). Returns quantized values, scale factor, and precisio"},{"name":"vector_stats","description":"Compute statistics for a float vector or matrix of vectors: mean, std, L2 norm, min, max, sparsity, top-K indices. Useful for debugging embedding quality and an"},{"name":"bm25_score","description":"Compute BM25 relevance score between a query and one or more documents. BM25 is the industry-standard keyword-based ranking algorithm used in Elasticsearch, Ope"},{"name":"build_rag_prompt","description":"Assemble a complete RAG (Retrieval-Augmented Generation) prompt from retrieved context chunks and a user query. Handles token budgeting, citation numbering, sys"},{"name":"prompt_template_fill","description":"Fill a prompt template with variables. Supports {{variable}} syntax and {{#if key}}...{{/if}} conditional blocks. Returns the filled prompt and lists unfilled v"},{"name":"few_shot_formatter","description":"Format few-shot examples for LLM prompts. Converts example pairs into formatted blocks. Supports chat format (User/Assistant), XML tags, Markdown, or plain text"},{"name":"system_prompt_builder","description":"Build a structured system prompt from components: role, task, constraints, output format, tone, language, and examples. Generates a production-ready system prom"},{"name":"model_info","description":"Get detailed specs for an AI model: context window, pricing per 1K tokens, knowledge cutoff, provider, multimodal support, reasoning capabilities, and feature l"},{"name":"compare_models","description":"Compare 2-5 AI models side by side: context window, pricing, multimodal, reasoning capabilities, and provider. Returns a comparison table with a recommendation "},{"name":"http_status_lookup","description":"Look up detailed information about any HTTP status code: class, name, description, cacheability, typical causes, and handling best practices. Covers every code "},{"name":"parse_http_headers","description":"Parse a raw HTTP headers block into a structured JSON object. Detects multi-value headers, masks Authorization values, and optionally audits for missing securit"},{"name":"generate_curl","description":"Generate a curl command from request parameters. Supports GET/POST/PUT/DELETE, custom headers, JSON body, and form data. Useful for documentation, sharing, and "},{"name":"extract_todos","description":"Extract TODO, FIXME, HACK, BUG, NOTE, OPTIMIZE, and custom tags from any source code or text. Returns line numbers, tag types, and message text. Essential for t"},{"name":"detect_secrets","description":"Scan code or config files for hardcoded secrets: AWS keys, GitHub tokens, OpenAI/Anthropic API keys, Stripe secrets, JWTs, database connection strings, and gene"},{"name":"count_code_lines","description":"Count lines of code: total, code lines, comment lines, blank lines, and comment density. Supports JS/TS, Python, Java/C/C++, Ruby, Go, Shell, HTML/XML, and CSS."},{"name":"lint_commit_message","description":"Validate a git commit message against the Conventional Commits spec (feat, fix, docs, style, refactor, test, chore, ci, perf, build). Returns compliance score, "},{"name":"word_frequency","description":"Analyze word frequency in text. Returns top N words with counts and percentages. Supports English stopword filtering. Useful for content analysis, keyword extra"},{"name":"extract_links","description":"Extract all URLs, email addresses, and domain names from text. Returns categorized and deduplicated results. Useful for content auditing, link checking, and web"},{"name":"levenshtein_distance","description":"Compute the Levenshtein (edit) distance and normalized similarity ratio between two strings. Supports batch comparison. Useful for fuzzy string matching, dedupl"},{"name":"json_diff","description":"Compute a deep structural diff between two JSON values. Returns added, removed, and changed keys with dot-notation paths. Like git diff but for JSON objects — p"},{"name":"merge_json","description":"Deep merge two JSON objects. Supports three array strategies: replace (default), concat, or unique (dedup concat). Nested objects are recursively merged — overr"},{"name":"json_to_yaml","description":"Convert a JSON object to clean, human-readable YAML. Handles nested objects, arrays, multiline strings, and special characters. No external dependencies."}],"toolCount":152,"toolsHash":"68f15982d31ca5609750a928a2af7afe6206d359ce0e29af4d06a5c860409d58","serverName":"ia-qa-toolbox","capabilities":["tools"],"serverVersion":"1.0.0","protocolVersion":"2025-06-18"}},{"at":"2026-10-09T11:27:41.294Z","kind":"mcp_initialize","ok":true,"httpStatus":200,"latencyMs":36,"error":null,"detail":{"tools":[{"name":"format_json","description":"Validate and pretty-print a string that is ALREADY valid JSON. Strict by design — it is a validity gate: valid JSON comes back formatted, anything else is rejec"},{"name":"generate_uuid","description":"Generate one or more cryptographically random UUID v4 identifiers. Use this when you need unique IDs for test fixtures, database records, session tokens, or any"},{"name":"hash_text","description":"Compute a cryptographic hash of a text string. Use when you need to verify data integrity, generate content fingerprints, hash passwords (prefer SHA-256+), or p"},{"name":"count_tokens","description":"Estimate the token count of a text string using the cl100k_base approximation (~4 chars/token). Call this BEFORE sending any text to an LLM API to check if it f"},{"name":"base64_encode","description":"Encode a UTF-8 string to Base64. Use when you need to embed binary data, multi-line text, or special characters safely inside JSON fields, HTTP headers, or data"},{"name":"base64_decode","description":"Decode a Base64 string back to UTF-8 text. Use for inspecting Base64-encoded API responses, JWT payload claims, config file values, or attachment data."},{"name":"url_encode","description":"Percent-encode a string for safe use in URLs. Call this before programmatically building query strings, path segments, or form-encoded bodies to prevent injecti"},{"name":"url_decode","description":"Decode a percent-encoded URL string back to plain text. Use when parsing query parameters from raw URLs or when displaying encoded values to users."},{"name":"generate_slug","description":"Convert any string into a URL-friendly slug: lowercase, ASCII-normalized (é→e), special characters removed, spaces replaced with hyphens. Use for generating SEO"},{"name":"validate_email","description":"Validate an email address against RFC 5322 syntax before storing it, sending a transactional email, or adding it to a mailing list. Returns { valid, email } — u"},{"name":"minify_js","description":"Minify a JavaScript snippet, function, class, or module up to 50 KB using Terser. Returns minified code and byte savings. Use when embedding scripts in HTML tem"},{"name":"decode_jwt","description":"Decode a JWT (JSON Web Token) and return its header and payload without verifying the signature. Also reports whether the token is expired and the exact expiry "},{"name":"text_stats","description":"Compute comprehensive statistics for any text: character count (with and without spaces), word count, line count, sentence count, paragraph count, and estimated"},{"name":"generate_password","description":"Generate a cryptographically secure random password using crypto.randomBytes. Configurable length (4–128), uppercase letters, digits, and symbols. Use when rese"},{"name":"parse_csv","description":"Parse a CSV string into a JSON array of objects (or raw arrays). Full RFC 4180: quoted fields may contain the delimiter, embedded newlines (the Excel/Sheets mul"},{"name":"color_convert","description":"Convert a color between HEX, RGB, and HSL formats. Use when translating design tokens between CSS notations, verifying color accessibility, or normalizing color"},{"name":"regex_test","description":"Test a regular expression pattern against an input string and return all matches with their index positions and named capture groups. Use for validating user in"},{"name":"lorem_ipsum","description":"Generate Lorem Ipsum placeholder text for UI mockups, design prototypes, or test data population. Configurable paragraphs (1–10), sentences per paragraph (1–20)"},{"name":"timestamp_convert","description":"Convert between Unix timestamps (seconds or milliseconds) and ISO-8601 / UTC date strings. Auto-detects epoch vs. millisecond format. Omit input to get the curr"},{"name":"diff_text","description":"Compute a unified line-by-line diff between two text strings (LCS algorithm). Returns added/removed/unchanged line counts and formatted diff hunks with configur"},{"name":"truncate_to_tokens","description":"Truncate text to at most N tokens (cl100k_base: ~4 chars/token) to avoid exceeding an LLM context window. Optionally keeps the end of the text instead of the st"},{"name":"split_chunks","description":"Split text into chunks of at most N tokens (cl100k_base: ~4 chars/token) with optional overlap. Designed for RAG ingestion pipelines."},{"name":"extract_json_from_text","description":"Extract the first valid JSON object or array embedded in chaotic LLM output (surrounded by markdown fences, prose, or explanatory text). Handles ```json blocks "},{"name":"strip_markdown","description":"Strip all Markdown formatting (headers, bold, italic, code fences, links, lists) from text and return clean plain text. Run this before injecting scraped docume"},{"name":"estimate_llm_cost","description":"Estimate the API cost in USD for a given model and token counts. Supports all major 2024–2026 models: GPT-4o, GPT-4.1, o3, o4-mini, Claude Opus 4, Claude Sonnet"},{"name":"escape_html","description":"Escape HTML special characters (&, <, >, \", ') to their safe HTML entities. ALWAYS call this before inserting any user-provided or LLM-generated content into an"},{"name":"unescape_html","description":"Convert HTML entities (&amp;, &lt;, &gt;, &quot;, &#x27;, and numeric &#NNN;) back to plain characters. Use when processing HTML-encoded text from APIs, email c"},{"name":"fetch_veille_feed","description":"Fetch the latest QA & AI/LLM articles aggregated from curated RSS sources (Google Testing Blog, DEV.to Testing/QA/AI/LLM/Agents, Hugging Face Blog, Simon Willis"},{"name":"score_geo_signals","description":"Analyze a webpage <head> HTML (or full HTML) for GEO (Generative Engine Optimization) signals. Returns a score /60 with per-check results and improvement tips. "},{"name":"extract_json_path","description":"Extract a value from a JSON string using dot-notation path (e.g., \"user.address.city\", \"items.0.name\", \"meta.tags\"). Supports array index access via numeric pat"},{"name":"generate_json_ld","description":"Generate a ready-to-paste <script type=\"application/ld+json\"> snippet for GEO / structured data optimization. Supported types: WebSite, FAQPage, Article, Person"},{"name":"analyze_diff_bugs","description":"Pattern-based diff linter: flags a fixed set of risky shapes in changed code — query-string interpolation (SQL/Cypher/Mongo injection shape), shell interpolatio"},{"name":"generate_test_cases","description":"Generate a set of test cases (valid, edge, invalid, pairwise) for a given feature description. Declared constraints drive the boundaries: a length or numeric bo"},{"name":"run_pr_gate_pipeline","description":"Review triage for a pull request. Takes a unified git diff (`git diff HEAD`) and returns: diff-lint findings with the lines that produced them, regression impac"},{"name":"validate_mcp_response","description":"Validate that an MCP tool response conforms to expected format, schema, and content rules. Use this to QA-test any MCP server tool. Supply the tool's actual JSO"},{"name":"llm_output_validator","description":"Validate an LLM response against QA criteria: format checks (JSON, code, markdown), content rules (must-include, must-not-include), length constraints, language"},{"name":"compare_responses","description":"Compare two ALREADY-PRODUCED outputs (e.g. model A vs model B on the same task) side by side. Returns deterministic metrics (token cosine, ROUGE-L, Jaccard, len"},{"name":"analyze_responses","description":"Semantically analyze N already-produced model outputs for the SAME task (the MCP counterpart to the LLM Sandbox). Without a reference: computes consensus — pair"},{"name":"prompt_test_suite","description":"Define a test suite for a prompt: provide the system prompt, user prompt, and expected output criteria. Returns a test plan with scored rubric — use this as inp"},{"name":"mcp_server_health_check","description":"Generate a health check report for an MCP server's tool manifest. Validates tool definitions, schema quality, naming conventions, and documentation completeness"},{"name":"mcp_server_evaluate","description":"Run a full compliance evaluation against a live MCP server URL. Tests: server reachability (ping), manifest discovery (GET /mcp), schema quality (snake_case nam"},{"name":"json_schema_validate","description":"Validate a JSON value against a JSON Schema (draft-07 subset). Supports type, required, properties, items, enum, const, pattern, format (email/uri/date), minimu"},{"name":"flatten_json","description":"Flatten a nested JSON object to single-level dot-notation keys (e.g. {\"a\":{\"b\":1}} → {\"a.b\":1}), or unflatten dot-notation keys back to a nested object. Support"},{"name":"xml_to_json","description":"Convert an XML string to a JSON object. Supports attributes, nested elements, arrays, CDATA, and namespaces. Options: parse numbers, parse booleans, ignore attr"},{"name":"redact_pii","description":"Automatically detect and redact Personally Identifiable Information (PII) from text. Replaces emails, phone numbers, SSNs, credit cards, IP addresses, and JWT t"},{"name":"mock_from_schema","description":"Generate realistic mock data from a JSON Schema. Supports all common types (string, number, integer, boolean, array, object, null), format hints (email, date, d"},{"name":"transform_json_array","description":"Transform a JSON array using common operations: pluck (extract specific fields), filter (by field value), sort_by (field), group_by (field), count_by (field), u"},{"name":"json_to_csv","description":"Convert a JSON array of objects to CSV format. Automatically detects columns from all object keys. Handles quoting and escaping per RFC 4180."},{"name":"case_convert","description":"Convert a string between naming conventions: camelCase, PascalCase, snake_case, kebab-case, UPPER_SNAKE_CASE, dot.case, Title Case. Essential for code generatio"},{"name":"sort_lines","description":"Sort, deduplicate, reverse, or filter lines of text. Useful for cleaning import lists, dependencies, log files, and config entries."},{"name":"number_base_convert","description":"Convert numbers between bases: decimal, binary, octal, hexadecimal, or any base 2–36. Auto-detects 0x, 0b, 0o prefixes."},{"name":"validate_url","description":"Parse and validate a URL. Returns decomposed components: protocol, hostname, port, path, query parameters, hash, and origin."},{"name":"check_contrast_ratio","description":"Calculate WCAG 2.1 contrast ratio between two colors. Returns ratio and compliance for AA/AAA normal and large text."},{"name":"html_to_markdown","description":"Convert HTML to clean Markdown. Strips scripts, styles, nav, ads, and comments. Converts headings, lists, links, images, code blocks. Ideal for preparing web co"},{"name":"cron_parse","description":"Parse a cron expression into a human-readable schedule description. Supports standard 5-field cron (minute hour day month weekday)."},{"name":"cron_validator","description":"Validate a 5-field cron expression, explain the schedule, and preview the next execution times. Use this to debug cron jobs before they reach production. Return"},{"name":"calculate_readability","description":"Calculate readability scores: Flesch Reading Ease, Flesch-Kincaid Grade Level, Coleman-Liau Index, and Automated Readability Index. Useful for evaluating LLM ou"},{"name":"normalize_whitespace","description":"Normalize whitespace: trim trailing spaces, collapse blank lines, normalize line endings (LF/CRLF), convert tabs to spaces. Useful for cleaning code, configs, a"},{"name":"embedding_similarity","description":"Compute text similarity using local algorithms (Bag of Words, TF-IDF, Character N-grams). No API key needed — runs entirely in-process. NOT real embeddings: for"},{"name":"llm_format_check","description":"Validate that an LLM output matches an expected format: JSON, Markdown, code block, bullet list, numbered list, table, YAML, XML, or custom regex. Essential for"},{"name":"hallucination_check","description":"Lexical hallucination check: verifies an LLM answer's words, numbers and polarity against the provided source/context. Fast, deterministic, no API key needed. E"},{"name":"prompt_injection_scan","description":"Scan user input or prompts for common prompt injection patterns. Detects system prompt overrides, jailbreak attempts, role manipulation, encoding tricks, delimi"},{"name":"token_budget_calculator","description":"Plan token allocation across system prompt, user input, context/RAG chunks, and expected output. Warns if budget exceeds model context window. Supports 25+ mode"},{"name":"consistency_check","description":"Compare multiple LLM responses to the same prompt and detect inconsistencies using Jaccard word-overlap similarity and fact drift (number comparison). Fast, det"},{"name":"llm_json_schema_check","description":"Validate that an LLM JSON output matches a JSON Schema definition. Tests required fields, types, enums, nested objects, and arrays. Critical for function-callin"},{"name":"latency_benchmark","description":"Measure response time of one or more HTTP endpoints (GET/POST). Runs N iterations and returns min/max/avg/p95 latency. Useful for API and MCP server benchmarkin"},{"name":"response_quality_score","description":"Score an LLM response against the criteria you pass: coverage of expected_keywords and compliance with max_length. Returns a 0-100 score over the criteria actua"},{"name":"rag_relevance_rank","description":"Rank an array of text chunks by relevance to a query using TF-IDF scoring. Simulates retrieval ranking for RAG testing without needing embeddings or an API."},{"name":"toxicity_scan","description":"Scan text for toxic language, hate speech, bias/stereotype framing, violence, sexual and self-harm content. Lexical + structural pattern matching (identity term"},{"name":"guardrail_test","description":"Test an LLM response against a set of guardrail rules: must-include, must-not-include, max length, required format, language, forbidden patterns, and custom reg"},{"name":"function_call_validate","description":"Validate an LLM function call / tool_use output: check that function name is in allowed list, arguments match expected schema, no extra/missing args. For OpenAI"},{"name":"conversation_analyze","description":"Analyze a multi-turn conversation for context retention, topic drift, instruction following, and repetition. Accepts messages array [{role, content}]. Essential"},{"name":"mcp_schema_lint","description":"Lint an MCP tool definition for best practices: naming conventions, description quality, schema completeness, required fields consistency, description length. R"},{"name":"cot_analyzer","description":"Analyze a Chain-of-Thought (CoT) or reasoning trace from an LLM. Detects step count, logical flow, conclusion presence, backtracking, and estimates reasoning de"},{"name":"ab_test_report","description":"Generate an A/B test report comparing two prompts or model configurations. Accepts arrays of scores and returns statistical comparison: mean, median, std deviat"},{"name":"context_window_check","description":"Given an array of message objects [{role, content}], estimate total token usage and check if it fits in the target model's context window. Warns about truncatio"},{"name":"vector_similarity","description":"Compute similarity/distance between two float vectors: cosine similarity, dot product, Euclidean and Manhattan distance. Essential for vector DB relevance scori"},{"name":"normalize_vector","description":"L2-normalize a float vector (produce a unit vector with norm=1). Required by many vector DBs (Pinecone, Qdrant cosine). Supports batch normalization of up to 10"},{"name":"vector_quantize","description":"Simulate int8 or int4 quantization of float32 embedding vectors. Reduces storage by 4x (int8) or 8x (int4). Returns quantized values, scale factor, and precisio"},{"name":"vector_stats","description":"Compute statistics for a float vector or matrix of vectors: mean, std, L2 norm, min, max, sparsity, top-K indices. Useful for debugging embedding quality and an"},{"name":"bm25_score","description":"Compute BM25 relevance score between a query and one or more documents. BM25 is the industry-standard keyword-based ranking algorithm used in Elasticsearch, Ope"},{"name":"build_rag_prompt","description":"Assemble a complete RAG (Retrieval-Augmented Generation) prompt from retrieved context chunks and a user query. Handles token budgeting, citation numbering, sys"},{"name":"prompt_template_fill","description":"Fill a prompt template with variables. Supports {{variable}} syntax and {{#if key}}...{{/if}} conditional blocks. Returns the filled prompt and lists unfilled v"},{"name":"few_shot_formatter","description":"Format few-shot examples for LLM prompts. Converts example pairs into formatted blocks. Supports chat format (User/Assistant), XML tags, Markdown, or plain text"},{"name":"system_prompt_builder","description":"Build a structured system prompt from components: role, task, constraints, output format, tone, language, and examples. Generates a production-ready system prom"},{"name":"model_info","description":"Get detailed specs for an AI model: context window, pricing per 1K tokens, knowledge cutoff, provider, multimodal support, reasoning capabilities, and feature l"},{"name":"compare_models","description":"Compare 2-5 AI models side by side: context window, pricing, multimodal, reasoning capabilities, and provider. Returns a comparison table with a recommendation "},{"name":"http_status_lookup","description":"Look up detailed information about any HTTP status code: class, name, description, cacheability, typical causes, and handling best practices. Covers every code "},{"name":"parse_http_headers","description":"Parse a raw HTTP headers block into a structured JSON object. Detects multi-value headers, masks Authorization values, and optionally audits for missing securit"},{"name":"generate_curl","description":"Generate a curl command from request parameters. Supports GET/POST/PUT/DELETE, custom headers, JSON body, and form data. Useful for documentation, sharing, and "},{"name":"extract_todos","description":"Extract TODO, FIXME, HACK, BUG, NOTE, OPTIMIZE, and custom tags from any source code or text. Returns line numbers, tag types, and message text. Essential for t"},{"name":"detect_secrets","description":"Scan code or config files for hardcoded secrets: AWS keys, GitHub tokens, OpenAI/Anthropic API keys, Stripe secrets, JWTs, database connection strings, and gene"},{"name":"count_code_lines","description":"Count lines of code: total, code lines, comment lines, blank lines, and comment density. Supports JS/TS, Python, Java/C/C++, Ruby, Go, Shell, HTML/XML, and CSS."},{"name":"lint_commit_message","description":"Validate a git commit message against the Conventional Commits spec (feat, fix, docs, style, refactor, test, chore, ci, perf, build). Returns compliance score, "},{"name":"word_frequency","description":"Analyze word frequency in text. Returns top N words with counts and percentages. Supports English stopword filtering. Useful for content analysis, keyword extra"},{"name":"extract_links","description":"Extract all URLs, email addresses, and domain names from text. Returns categorized and deduplicated results. Useful for content auditing, link checking, and web"},{"name":"levenshtein_distance","description":"Compute the Levenshtein (edit) distance and normalized similarity ratio between two strings. Supports batch comparison. Useful for fuzzy string matching, dedupl"},{"name":"json_diff","description":"Compute a deep structural diff between two JSON values. Returns added, removed, and changed keys with dot-notation paths. Like git diff but for JSON objects — p"},{"name":"merge_json","description":"Deep merge two JSON objects. Supports three array strategies: replace (default), concat, or unique (dedup concat). Nested objects are recursively merged — overr"},{"name":"json_to_yaml","description":"Convert a JSON object to clean, human-readable YAML. Handles nested objects, arrays, multiline strings, and special characters. No external dependencies."}],"toolCount":152,"toolsHash":"68f15982d31ca5609750a928a2af7afe6206d359ce0e29af4d06a5c860409d58","serverName":"ia-qa-toolbox","capabilities":["tools"],"serverVersion":"1.0.0","protocolVersion":"2025-06-18"}},{"at":"2026-10-09T05:25:10.631Z","kind":"mcp_initialize","ok":true,"httpStatus":200,"latencyMs":19,"error":null,"detail":{"tools":[{"name":"format_json","description":"Validate and pretty-print a string that is ALREADY valid JSON. Strict by design — it is a validity gate: valid JSON comes back formatted, anything else is rejec"},{"name":"generate_uuid","description":"Generate one or more cryptographically random UUID v4 identifiers. Use this when you need unique IDs for test fixtures, database records, session tokens, or any"},{"name":"hash_text","description":"Compute a cryptographic hash of a text string. Use when you need to verify data integrity, generate content fingerprints, hash passwords (prefer SHA-256+), or p"},{"name":"count_tokens","description":"Estimate the token count of a text string using the cl100k_base approximation (~4 chars/token). Call this BEFORE sending any text to an LLM API to check if it f"},{"name":"base64_encode","description":"Encode a UTF-8 string to Base64. Use when you need to embed binary data, multi-line text, or special characters safely inside JSON fields, HTTP headers, or data"},{"name":"base64_decode","description":"Decode a Base64 string back to UTF-8 text. Use for inspecting Base64-encoded API responses, JWT payload claims, config file values, or attachment data."},{"name":"url_encode","description":"Percent-encode a string for safe use in URLs. Call this before programmatically building query strings, path segments, or form-encoded bodies to prevent injecti"},{"name":"url_decode","description":"Decode a percent-encoded URL string back to plain text. Use when parsing query parameters from raw URLs or when displaying encoded values to users."},{"name":"generate_slug","description":"Convert any string into a URL-friendly slug: lowercase, ASCII-normalized (é→e), special characters removed, spaces replaced with hyphens. Use for generating SEO"},{"name":"validate_email","description":"Validate an email address against RFC 5322 syntax before storing it, sending a transactional email, or adding it to a mailing list. Returns { valid, email } — u"},{"name":"minify_js","description":"Minify a JavaScript snippet, function, class, or module up to 50 KB using Terser. Returns minified code and byte savings. Use when embedding scripts in HTML tem"},{"name":"decode_jwt","description":"Decode a JWT (JSON Web Token) and return its header and payload without verifying the signature. Also reports whether the token is expired and the exact expiry "},{"name":"text_stats","description":"Compute comprehensive statistics for any text: character count (with and without spaces), word count, line count, sentence count, paragraph count, and estimated"},{"name":"generate_password","description":"Generate a cryptographically secure random password using crypto.randomBytes. Configurable length (4–128), uppercase letters, digits, and symbols. Use when rese"},{"name":"parse_csv","description":"Parse a CSV string into a JSON array of objects (or raw arrays). Full RFC 4180: quoted fields may contain the delimiter, embedded newlines (the Excel/Sheets mul"},{"name":"color_convert","description":"Convert a color between HEX, RGB, and HSL formats. Use when translating design tokens between CSS notations, verifying color accessibility, or normalizing color"},{"name":"regex_test","description":"Test a regular expression pattern against an input string and return all matches with their index positions and named capture groups. Use for validating user in"},{"name":"lorem_ipsum","description":"Generate Lorem Ipsum placeholder text for UI mockups, design prototypes, or test data population. Configurable paragraphs (1–10), sentences per paragraph (1–20)"},{"name":"timestamp_convert","description":"Convert between Unix timestamps (seconds or milliseconds) and ISO-8601 / UTC date strings. Auto-detects epoch vs. millisecond format. Omit input to get the curr"},{"name":"diff_text","description":"Compute a unified line-by-line diff between two text strings (LCS algorithm). Returns added/removed/unchanged line counts and formatted diff hunks with configur"},{"name":"truncate_to_tokens","description":"Truncate text to at most N tokens (cl100k_base: ~4 chars/token) to avoid exceeding an LLM context window. Optionally keeps the end of the text instead of the st"},{"name":"split_chunks","description":"Split text into chunks of at most N tokens (cl100k_base: ~4 chars/token) with optional overlap. Designed for RAG ingestion pipelines."},{"name":"extract_json_from_text","description":"Extract the first valid JSON object or array embedded in chaotic LLM output (surrounded by markdown fences, prose, or explanatory text). Handles ```json blocks "},{"name":"strip_markdown","description":"Strip all Markdown formatting (headers, bold, italic, code fences, links, lists) from text and return clean plain text. Run this before injecting scraped docume"},{"name":"estimate_llm_cost","description":"Estimate the API cost in USD for a given model and token counts. Supports all major 2024–2026 models: GPT-4o, GPT-4.1, o3, o4-mini, Claude Opus 4, Claude Sonnet"},{"name":"escape_html","description":"Escape HTML special characters (&, <, >, \", ') to their safe HTML entities. ALWAYS call this before inserting any user-provided or LLM-generated content into an"},{"name":"unescape_html","description":"Convert HTML entities (&amp;, &lt;, &gt;, &quot;, &#x27;, and numeric &#NNN;) back to plain characters. Use when processing HTML-encoded text from APIs, email c"},{"name":"fetch_veille_feed","description":"Fetch the latest QA & AI/LLM articles aggregated from curated RSS sources (Google Testing Blog, DEV.to Testing/QA/AI/LLM/Agents, Hugging Face Blog, Simon Willis"},{"name":"score_geo_signals","description":"Analyze a webpage <head> HTML (or full HTML) for GEO (Generative Engine Optimization) signals. Returns a score /60 with per-check results and improvement tips. "},{"name":"extract_json_path","description":"Extract a value from a JSON string using dot-notation path (e.g., \"user.address.city\", \"items.0.name\", \"meta.tags\"). Supports array index access via numeric pat"},{"name":"generate_json_ld","description":"Generate a ready-to-paste <script type=\"application/ld+json\"> snippet for GEO / structured data optimization. Supported types: WebSite, FAQPage, Article, Person"},{"name":"analyze_diff_bugs","description":"Pattern-based diff linter: flags a fixed set of risky shapes in changed code — query-string interpolation (SQL/Cypher/Mongo injection shape), shell interpolatio"},{"name":"generate_test_cases","description":"Generate a set of test cases (valid, edge, invalid, pairwise) for a given feature description. Declared constraints drive the boundaries: a length or numeric bo"},{"name":"run_pr_gate_pipeline","description":"Review triage for a pull request. Takes a unified git diff (`git diff HEAD`) and returns: diff-lint findings with the lines that produced them, regression impac"},{"name":"validate_mcp_response","description":"Validate that an MCP tool response conforms to expected format, schema, and content rules. Use this to QA-test any MCP server tool. Supply the tool's actual JSO"},{"name":"llm_output_validator","description":"Validate an LLM response against QA criteria: format checks (JSON, code, markdown), content rules (must-include, must-not-include), length constraints, language"},{"name":"compare_responses","description":"Compare two ALREADY-PRODUCED outputs (e.g. model A vs model B on the same task) side by side. Returns deterministic metrics (token cosine, ROUGE-L, Jaccard, len"},{"name":"analyze_responses","description":"Semantically analyze N already-produced model outputs for the SAME task (the MCP counterpart to the LLM Sandbox). Without a reference: computes consensus — pair"},{"name":"prompt_test_suite","description":"Define a test suite for a prompt: provide the system prompt, user prompt, and expected output criteria. Returns a test plan with scored rubric — use this as inp"},{"name":"mcp_server_health_check","description":"Generate a health check report for an MCP server's tool manifest. Validates tool definitions, schema quality, naming conventions, and documentation completeness"},{"name":"mcp_server_evaluate","description":"Run a full compliance evaluation against a live MCP server URL. Tests: server reachability (ping), manifest discovery (GET /mcp), schema quality (snake_case nam"},{"name":"json_schema_validate","description":"Validate a JSON value against a JSON Schema (draft-07 subset). Supports type, required, properties, items, enum, const, pattern, format (email/uri/date), minimu"},{"name":"flatten_json","description":"Flatten a nested JSON object to single-level dot-notation keys (e.g. {\"a\":{\"b\":1}} → {\"a.b\":1}), or unflatten dot-notation keys back to a nested object. Support"},{"name":"xml_to_json","description":"Convert an XML string to a JSON object. Supports attributes, nested elements, arrays, CDATA, and namespaces. Options: parse numbers, parse booleans, ignore attr"},{"name":"redact_pii","description":"Automatically detect and redact Personally Identifiable Information (PII) from text. Replaces emails, phone numbers, SSNs, credit cards, IP addresses, and JWT t"},{"name":"mock_from_schema","description":"Generate realistic mock data from a JSON Schema. Supports all common types (string, number, integer, boolean, array, object, null), format hints (email, date, d"},{"name":"transform_json_array","description":"Transform a JSON array using common operations: pluck (extract specific fields), filter (by field value), sort_by (field), group_by (field), count_by (field), u"},{"name":"json_to_csv","description":"Convert a JSON array of objects to CSV format. Automatically detects columns from all object keys. Handles quoting and escaping per RFC 4180."},{"name":"case_convert","description":"Convert a string between naming conventions: camelCase, PascalCase, snake_case, kebab-case, UPPER_SNAKE_CASE, dot.case, Title Case. Essential for code generatio"},{"name":"sort_lines","description":"Sort, deduplicate, reverse, or filter lines of text. Useful for cleaning import lists, dependencies, log files, and config entries."},{"name":"number_base_convert","description":"Convert numbers between bases: decimal, binary, octal, hexadecimal, or any base 2–36. Auto-detects 0x, 0b, 0o prefixes."},{"name":"validate_url","description":"Parse and validate a URL. Returns decomposed components: protocol, hostname, port, path, query parameters, hash, and origin."},{"name":"check_contrast_ratio","description":"Calculate WCAG 2.1 contrast ratio between two colors. Returns ratio and compliance for AA/AAA normal and large text."},{"name":"html_to_markdown","description":"Convert HTML to clean Markdown. Strips scripts, styles, nav, ads, and comments. Converts headings, lists, links, images, code blocks. Ideal for preparing web co"},{"name":"cron_parse","description":"Parse a cron expression into a human-readable schedule description. Supports standard 5-field cron (minute hour day month weekday)."},{"name":"cron_validator","description":"Validate a 5-field cron expression, explain the schedule, and preview the next execution times. Use this to debug cron jobs before they reach production. Return"},{"name":"calculate_readability","description":"Calculate readability scores: Flesch Reading Ease, Flesch-Kincaid Grade Level, Coleman-Liau Index, and Automated Readability Index. Useful for evaluating LLM ou"},{"name":"normalize_whitespace","description":"Normalize whitespace: trim trailing spaces, collapse blank lines, normalize line endings (LF/CRLF), convert tabs to spaces. Useful for cleaning code, configs, a"},{"name":"embedding_similarity","description":"Compute text similarity using local algorithms (Bag of Words, TF-IDF, Character N-grams). No API key needed — runs entirely in-process. NOT real embeddings: for"},{"name":"llm_format_check","description":"Validate that an LLM output matches an expected format: JSON, Markdown, code block, bullet list, numbered list, table, YAML, XML, or custom regex. Essential for"},{"name":"hallucination_check","description":"Lexical hallucination check: verifies an LLM answer's words, numbers and polarity against the provided source/context. Fast, deterministic, no API key needed. E"},{"name":"prompt_injection_scan","description":"Scan user input or prompts for common prompt injection patterns. Detects system prompt overrides, jailbreak attempts, role manipulation, encoding tricks, delimi"},{"name":"token_budget_calculator","description":"Plan token allocation across system prompt, user input, context/RAG chunks, and expected output. Warns if budget exceeds model context window. Supports 25+ mode"},{"name":"consistency_check","description":"Compare multiple LLM responses to the same prompt and detect inconsistencies using Jaccard word-overlap similarity and fact drift (number comparison). Fast, det"},{"name":"llm_json_schema_check","description":"Validate that an LLM JSON output matches a JSON Schema definition. Tests required fields, types, enums, nested objects, and arrays. Critical for function-callin"},{"name":"latency_benchmark","description":"Measure response time of one or more HTTP endpoints (GET/POST). Runs N iterations and returns min/max/avg/p95 latency. Useful for API and MCP server benchmarkin"},{"name":"response_quality_score","description":"Score an LLM response against the criteria you pass: coverage of expected_keywords and compliance with max_length. Returns a 0-100 score over the criteria actua"},{"name":"rag_relevance_rank","description":"Rank an array of text chunks by relevance to a query using TF-IDF scoring. Simulates retrieval ranking for RAG testing without needing embeddings or an API."},{"name":"toxicity_scan","description":"Scan text for toxic language, hate speech, bias/stereotype framing, violence, sexual and self-harm content. Lexical + structural pattern matching (identity term"},{"name":"guardrail_test","description":"Test an LLM response against a set of guardrail rules: must-include, must-not-include, max length, required format, language, forbidden patterns, and custom reg"},{"name":"function_call_validate","description":"Validate an LLM function call / tool_use output: check that function name is in allowed list, arguments match expected schema, no extra/missing args. For OpenAI"},{"name":"conversation_analyze","description":"Analyze a multi-turn conversation for context retention, topic drift, instruction following, and repetition. Accepts messages array [{role, content}]. Essential"},{"name":"mcp_schema_lint","description":"Lint an MCP tool definition for best practices: naming conventions, description quality, schema completeness, required fields consistency, description length. R"},{"name":"cot_analyzer","description":"Analyze a Chain-of-Thought (CoT) or reasoning trace from an LLM. Detects step count, logical flow, conclusion presence, backtracking, and estimates reasoning de"},{"name":"ab_test_report","description":"Generate an A/B test report comparing two prompts or model configurations. Accepts arrays of scores and returns statistical comparison: mean, median, std deviat"},{"name":"context_window_check","description":"Given an array of message objects [{role, content}], estimate total token usage and check if it fits in the target model's context window. Warns about truncatio"},{"name":"vector_similarity","description":"Compute similarity/distance between two float vectors: cosine similarity, dot product, Euclidean and Manhattan distance. Essential for vector DB relevance scori"},{"name":"normalize_vector","description":"L2-normalize a float vector (produce a unit vector with norm=1). Required by many vector DBs (Pinecone, Qdrant cosine). Supports batch normalization of up to 10"},{"name":"vector_quantize","description":"Simulate int8 or int4 quantization of float32 embedding vectors. Reduces storage by 4x (int8) or 8x (int4). Returns quantized values, scale factor, and precisio"},{"name":"vector_stats","description":"Compute statistics for a float vector or matrix of vectors: mean, std, L2 norm, min, max, sparsity, top-K indices. Useful for debugging embedding quality and an"},{"name":"bm25_score","description":"Compute BM25 relevance score between a query and one or more documents. BM25 is the industry-standard keyword-based ranking algorithm used in Elasticsearch, Ope"},{"name":"build_rag_prompt","description":"Assemble a complete RAG (Retrieval-Augmented Generation) prompt from retrieved context chunks and a user query. Handles token budgeting, citation numbering, sys"},{"name":"prompt_template_fill","description":"Fill a prompt template with variables. Supports {{variable}} syntax and {{#if key}}...{{/if}} conditional blocks. Returns the filled prompt and lists unfilled v"},{"name":"few_shot_formatter","description":"Format few-shot examples for LLM prompts. Converts example pairs into formatted blocks. Supports chat format (User/Assistant), XML tags, Markdown, or plain text"},{"name":"system_prompt_builder","description":"Build a structured system prompt from components: role, task, constraints, output format, tone, language, and examples. Generates a production-ready system prom"},{"name":"model_info","description":"Get detailed specs for an AI model: context window, pricing per 1K tokens, knowledge cutoff, provider, multimodal support, reasoning capabilities, and feature l"},{"name":"compare_models","description":"Compare 2-5 AI models side by side: context window, pricing, multimodal, reasoning capabilities, and provider. Returns a comparison table with a recommendation "},{"name":"http_status_lookup","description":"Look up detailed information about any HTTP status code: class, name, description, cacheability, typical causes, and handling best practices. Covers every code "},{"name":"parse_http_headers","description":"Parse a raw HTTP headers block into a structured JSON object. Detects multi-value headers, masks Authorization values, and optionally audits for missing securit"},{"name":"generate_curl","description":"Generate a curl command from request parameters. Supports GET/POST/PUT/DELETE, custom headers, JSON body, and form data. Useful for documentation, sharing, and "},{"name":"extract_todos","description":"Extract TODO, FIXME, HACK, BUG, NOTE, OPTIMIZE, and custom tags from any source code or text. Returns line numbers, tag types, and message text. Essential for t"},{"name":"detect_secrets","description":"Scan code or config files for hardcoded secrets: AWS keys, GitHub tokens, OpenAI/Anthropic API keys, Stripe secrets, JWTs, database connection strings, and gene"},{"name":"count_code_lines","description":"Count lines of code: total, code lines, comment lines, blank lines, and comment density. Supports JS/TS, Python, Java/C/C++, Ruby, Go, Shell, HTML/XML, and CSS."},{"name":"lint_commit_message","description":"Validate a git commit message against the Conventional Commits spec (feat, fix, docs, style, refactor, test, chore, ci, perf, build). Returns compliance score, "},{"name":"word_frequency","description":"Analyze word frequency in text. Returns top N words with counts and percentages. Supports English stopword filtering. Useful for content analysis, keyword extra"},{"name":"extract_links","description":"Extract all URLs, email addresses, and domain names from text. Returns categorized and deduplicated results. Useful for content auditing, link checking, and web"},{"name":"levenshtein_distance","description":"Compute the Levenshtein (edit) distance and normalized similarity ratio between two strings. Supports batch comparison. Useful for fuzzy string matching, dedupl"},{"name":"json_diff","description":"Compute a deep structural diff between two JSON values. Returns added, removed, and changed keys with dot-notation paths. Like git diff but for JSON objects — p"},{"name":"merge_json","description":"Deep merge two JSON objects. Supports three array strategies: replace (default), concat, or unique (dedup concat). Nested objects are recursively merged — overr"},{"name":"json_to_yaml","description":"Convert a JSON object to clean, human-readable YAML. Handles nested objects, arrays, multiline strings, and special characters. No external dependencies."}],"toolCount":152,"toolsHash":"68f15982d31ca5609750a928a2af7afe6206d359ce0e29af4d06a5c860409d58","serverName":"ia-qa-toolbox","capabilities":["tools"],"serverVersion":"1.0.0","protocolVersion":"2025-06-18"}},{"at":"2026-10-08T22:24:36.393Z","kind":"mcp_initialize","ok":true,"httpStatus":200,"latencyMs":20,"error":null,"detail":{"tools":[{"name":"format_json","description":"Validate and pretty-print a string that is ALREADY valid JSON. Strict by design — it is a validity gate: valid JSON comes back formatted, anything else is rejec"},{"name":"generate_uuid","description":"Generate one or more cryptographically random UUID v4 identifiers. Use this when you need unique IDs for test fixtures, database records, session tokens, or any"},{"name":"hash_text","description":"Compute a cryptographic hash of a text string. Use when you need to verify data integrity, generate content fingerprints, hash passwords (prefer SHA-256+), or p"},{"name":"count_tokens","description":"Estimate the token count of a text string using the cl100k_base approximation (~4 chars/token). Call this BEFORE sending any text to an LLM API to check if it f"},{"name":"base64_encode","description":"Encode a UTF-8 string to Base64. Use when you need to embed binary data, multi-line text, or special characters safely inside JSON fields, HTTP headers, or data"},{"name":"base64_decode","description":"Decode a Base64 string back to UTF-8 text. Use for inspecting Base64-encoded API responses, JWT payload claims, config file values, or attachment data."},{"name":"url_encode","description":"Percent-encode a string for safe use in URLs. Call this before programmatically building query strings, path segments, or form-encoded bodies to prevent injecti"},{"name":"url_decode","description":"Decode a percent-encoded URL string back to plain text. Use when parsing query parameters from raw URLs or when displaying encoded values to users."},{"name":"generate_slug","description":"Convert any string into a URL-friendly slug: lowercase, ASCII-normalized (é→e), special characters removed, spaces replaced with hyphens. Use for generating SEO"},{"name":"validate_email","description":"Validate an email address against RFC 5322 syntax before storing it, sending a transactional email, or adding it to a mailing list. Returns { valid, email } — u"},{"name":"minify_js","description":"Minify a JavaScript snippet, function, class, or module up to 50 KB using Terser. Returns minified code and byte savings. Use when embedding scripts in HTML tem"},{"name":"decode_jwt","description":"Decode a JWT (JSON Web Token) and return its header and payload without verifying the signature. Also reports whether the token is expired and the exact expiry "},{"name":"text_stats","description":"Compute comprehensive statistics for any text: character count (with and without spaces), word count, line count, sentence count, paragraph count, and estimated"},{"name":"generate_password","description":"Generate a cryptographically secure random password using crypto.randomBytes. Configurable length (4–128), uppercase letters, digits, and symbols. Use when rese"},{"name":"parse_csv","description":"Parse a CSV string into a JSON array of objects (or raw arrays). Full RFC 4180: quoted fields may contain the delimiter, embedded newlines (the Excel/Sheets mul"},{"name":"color_convert","description":"Convert a color between HEX, RGB, and HSL formats. Use when translating design tokens between CSS notations, verifying color accessibility, or normalizing color"},{"name":"regex_test","description":"Test a regular expression pattern against an input string and return all matches with their index positions and named capture groups. Use for validating user in"},{"name":"lorem_ipsum","description":"Generate Lorem Ipsum placeholder text for UI mockups, design prototypes, or test data population. Configurable paragraphs (1–10), sentences per paragraph (1–20)"},{"name":"timestamp_convert","description":"Convert between Unix timestamps (seconds or milliseconds) and ISO-8601 / UTC date strings. Auto-detects epoch vs. millisecond format. Omit input to get the curr"},{"name":"diff_text","description":"Compute a unified line-by-line diff between two text strings (LCS algorithm). Returns added/removed/unchanged line counts and formatted diff hunks with configur"},{"name":"truncate_to_tokens","description":"Truncate text to at most N tokens (cl100k_base: ~4 chars/token) to avoid exceeding an LLM context window. Optionally keeps the end of the text instead of the st"},{"name":"split_chunks","description":"Split text into chunks of at most N tokens (cl100k_base: ~4 chars/token) with optional overlap. Designed for RAG ingestion pipelines."},{"name":"extract_json_from_text","description":"Extract the first valid JSON object or array embedded in chaotic LLM output (surrounded by markdown fences, prose, or explanatory text). Handles ```json blocks "},{"name":"strip_markdown","description":"Strip all Markdown formatting (headers, bold, italic, code fences, links, lists) from text and return clean plain text. Run this before injecting scraped docume"},{"name":"estimate_llm_cost","description":"Estimate the API cost in USD for a given model and token counts. Supports all major 2024–2026 models: GPT-4o, GPT-4.1, o3, o4-mini, Claude Opus 4, Claude Sonnet"},{"name":"escape_html","description":"Escape HTML special characters (&, <, >, \", ') to their safe HTML entities. ALWAYS call this before inserting any user-provided or LLM-generated content into an"},{"name":"unescape_html","description":"Convert HTML entities (&amp;, &lt;, &gt;, &quot;, &#x27;, and numeric &#NNN;) back to plain characters. Use when processing HTML-encoded text from APIs, email c"},{"name":"fetch_veille_feed","description":"Fetch the latest QA & AI/LLM articles aggregated from curated RSS sources (Google Testing Blog, DEV.to Testing/QA/AI/LLM/Agents, Hugging Face Blog, Simon Willis"},{"name":"score_geo_signals","description":"Analyze a webpage <head> HTML (or full HTML) for GEO (Generative Engine Optimization) signals. Returns a score /60 with per-check results and improvement tips. "},{"name":"extract_json_path","description":"Extract a value from a JSON string using dot-notation path (e.g., \"user.address.city\", \"items.0.name\", \"meta.tags\"). Supports array index access via numeric pat"},{"name":"generate_json_ld","description":"Generate a ready-to-paste <script type=\"application/ld+json\"> snippet for GEO / structured data optimization. Supported types: WebSite, FAQPage, Article, Person"},{"name":"analyze_diff_bugs","description":"Pattern-based diff linter: flags a fixed set of risky shapes in changed code — query-string interpolation (SQL/Cypher/Mongo injection shape), shell interpolatio"},{"name":"generate_test_cases","description":"Generate a set of test cases (valid, edge, invalid, pairwise) for a given feature description. Declared constraints drive the boundaries: a length or numeric bo"},{"name":"run_pr_gate_pipeline","description":"Review triage for a pull request. Takes a unified git diff (`git diff HEAD`) and returns: diff-lint findings with the lines that produced them, regression impac"},{"name":"validate_mcp_response","description":"Validate that an MCP tool response conforms to expected format, schema, and content rules. Use this to QA-test any MCP server tool. Supply the tool's actual JSO"},{"name":"llm_output_validator","description":"Validate an LLM response against QA criteria: format checks (JSON, code, markdown), content rules (must-include, must-not-include), length constraints, language"},{"name":"compare_responses","description":"Compare two ALREADY-PRODUCED outputs (e.g. model A vs model B on the same task) side by side. Returns deterministic metrics (token cosine, ROUGE-L, Jaccard, len"},{"name":"analyze_responses","description":"Semantically analyze N already-produced model outputs for the SAME task (the MCP counterpart to the LLM Sandbox). Without a reference: computes consensus — pair"},{"name":"prompt_test_suite","description":"Define a test suite for a prompt: provide the system prompt, user prompt, and expected output criteria. Returns a test plan with scored rubric — use this as inp"},{"name":"mcp_server_health_check","description":"Generate a health check report for an MCP server's tool manifest. Validates tool definitions, schema quality, naming conventions, and documentation completeness"},{"name":"mcp_server_evaluate","description":"Run a full compliance evaluation against a live MCP server URL. Tests: server reachability (ping), manifest discovery (GET /mcp), schema quality (snake_case nam"},{"name":"json_schema_validate","description":"Validate a JSON value against a JSON Schema (draft-07 subset). Supports type, required, properties, items, enum, const, pattern, format (email/uri/date), minimu"},{"name":"flatten_json","description":"Flatten a nested JSON object to single-level dot-notation keys (e.g. {\"a\":{\"b\":1}} → {\"a.b\":1}), or unflatten dot-notation keys back to a nested object. Support"},{"name":"xml_to_json","description":"Convert an XML string to a JSON object. Supports attributes, nested elements, arrays, CDATA, and namespaces. Options: parse numbers, parse booleans, ignore attr"},{"name":"redact_pii","description":"Automatically detect and redact Personally Identifiable Information (PII) from text. Replaces emails, phone numbers, SSNs, credit cards, IP addresses, and JWT t"},{"name":"mock_from_schema","description":"Generate realistic mock data from a JSON Schema. Supports all common types (string, number, integer, boolean, array, object, null), format hints (email, date, d"},{"name":"transform_json_array","description":"Transform a JSON array using common operations: pluck (extract specific fields), filter (by field value), sort_by (field), group_by (field), count_by (field), u"},{"name":"json_to_csv","description":"Convert a JSON array of objects to CSV format. Automatically detects columns from all object keys. Handles quoting and escaping per RFC 4180."},{"name":"case_convert","description":"Convert a string between naming conventions: camelCase, PascalCase, snake_case, kebab-case, UPPER_SNAKE_CASE, dot.case, Title Case. Essential for code generatio"},{"name":"sort_lines","description":"Sort, deduplicate, reverse, or filter lines of text. Useful for cleaning import lists, dependencies, log files, and config entries."},{"name":"number_base_convert","description":"Convert numbers between bases: decimal, binary, octal, hexadecimal, or any base 2–36. Auto-detects 0x, 0b, 0o prefixes."},{"name":"validate_url","description":"Parse and validate a URL. Returns decomposed components: protocol, hostname, port, path, query parameters, hash, and origin."},{"name":"check_contrast_ratio","description":"Calculate WCAG 2.1 contrast ratio between two colors. Returns ratio and compliance for AA/AAA normal and large text."},{"name":"html_to_markdown","description":"Convert HTML to clean Markdown. Strips scripts, styles, nav, ads, and comments. Converts headings, lists, links, images, code blocks. Ideal for preparing web co"},{"name":"cron_parse","description":"Parse a cron expression into a human-readable schedule description. Supports standard 5-field cron (minute hour day month weekday)."},{"name":"cron_validator","description":"Validate a 5-field cron expression, explain the schedule, and preview the next execution times. Use this to debug cron jobs before they reach production. Return"},{"name":"calculate_readability","description":"Calculate readability scores: Flesch Reading Ease, Flesch-Kincaid Grade Level, Coleman-Liau Index, and Automated Readability Index. Useful for evaluating LLM ou"},{"name":"normalize_whitespace","description":"Normalize whitespace: trim trailing spaces, collapse blank lines, normalize line endings (LF/CRLF), convert tabs to spaces. Useful for cleaning code, configs, a"},{"name":"embedding_similarity","description":"Compute text similarity using local algorithms (Bag of Words, TF-IDF, Character N-grams). No API key needed — runs entirely in-process. NOT real embeddings: for"},{"name":"llm_format_check","description":"Validate that an LLM output matches an expected format: JSON, Markdown, code block, bullet list, numbered list, table, YAML, XML, or custom regex. Essential for"},{"name":"hallucination_check","description":"Lexical hallucination check: verifies an LLM answer's words, numbers and polarity against the provided source/context. Fast, deterministic, no API key needed. E"},{"name":"prompt_injection_scan","description":"Scan user input or prompts for common prompt injection patterns. Detects system prompt overrides, jailbreak attempts, role manipulation, encoding tricks, delimi"},{"name":"token_budget_calculator","description":"Plan token allocation across system prompt, user input, context/RAG chunks, and expected output. Warns if budget exceeds model context window. Supports 25+ mode"},{"name":"consistency_check","description":"Compare multiple LLM responses to the same prompt and detect inconsistencies using Jaccard word-overlap similarity and fact drift (number comparison). Fast, det"},{"name":"llm_json_schema_check","description":"Validate that an LLM JSON output matches a JSON Schema definition. Tests required fields, types, enums, nested objects, and arrays. Critical for function-callin"},{"name":"latency_benchmark","description":"Measure response time of one or more HTTP endpoints (GET/POST). Runs N iterations and returns min/max/avg/p95 latency. Useful for API and MCP server benchmarkin"},{"name":"response_quality_score","description":"Score an LLM response against the criteria you pass: coverage of expected_keywords and compliance with max_length. Returns a 0-100 score over the criteria actua"},{"name":"rag_relevance_rank","description":"Rank an array of text chunks by relevance to a query using TF-IDF scoring. Simulates retrieval ranking for RAG testing without needing embeddings or an API."},{"name":"toxicity_scan","description":"Scan text for toxic language, hate speech, bias/stereotype framing, violence, sexual and self-harm content. Lexical + structural pattern matching (identity term"},{"name":"guardrail_test","description":"Test an LLM response against a set of guardrail rules: must-include, must-not-include, max length, required format, language, forbidden patterns, and custom reg"},{"name":"function_call_validate","description":"Validate an LLM function call / tool_use output: check that function name is in allowed list, arguments match expected schema, no extra/missing args. For OpenAI"},{"name":"conversation_analyze","description":"Analyze a multi-turn conversation for context retention, topic drift, instruction following, and repetition. Accepts messages array [{role, content}]. Essential"},{"name":"mcp_schema_lint","description":"Lint an MCP tool definition for best practices: naming conventions, description quality, schema completeness, required fields consistency, description length. R"},{"name":"cot_analyzer","description":"Analyze a Chain-of-Thought (CoT) or reasoning trace from an LLM. Detects step count, logical flow, conclusion presence, backtracking, and estimates reasoning de"},{"name":"ab_test_report","description":"Generate an A/B test report comparing two prompts or model configurations. Accepts arrays of scores and returns statistical comparison: mean, median, std deviat"},{"name":"context_window_check","description":"Given an array of message objects [{role, content}], estimate total token usage and check if it fits in the target model's context window. Warns about truncatio"},{"name":"vector_similarity","description":"Compute similarity/distance between two float vectors: cosine similarity, dot product, Euclidean and Manhattan distance. Essential for vector DB relevance scori"},{"name":"normalize_vector","description":"L2-normalize a float vector (produce a unit vector with norm=1). Required by many vector DBs (Pinecone, Qdrant cosine). Supports batch normalization of up to 10"},{"name":"vector_quantize","description":"Simulate int8 or int4 quantization of float32 embedding vectors. Reduces storage by 4x (int8) or 8x (int4). Returns quantized values, scale factor, and precisio"},{"name":"vector_stats","description":"Compute statistics for a float vector or matrix of vectors: mean, std, L2 norm, min, max, sparsity, top-K indices. Useful for debugging embedding quality and an"},{"name":"bm25_score","description":"Compute BM25 relevance score between a query and one or more documents. BM25 is the industry-standard keyword-based ranking algorithm used in Elasticsearch, Ope"},{"name":"build_rag_prompt","description":"Assemble a complete RAG (Retrieval-Augmented Generation) prompt from retrieved context chunks and a user query. Handles token budgeting, citation numbering, sys"},{"name":"prompt_template_fill","description":"Fill a prompt template with variables. Supports {{variable}} syntax and {{#if key}}...{{/if}} conditional blocks. Returns the filled prompt and lists unfilled v"},{"name":"few_shot_formatter","description":"Format few-shot examples for LLM prompts. Converts example pairs into formatted blocks. Supports chat format (User/Assistant), XML tags, Markdown, or plain text"},{"name":"system_prompt_builder","description":"Build a structured system prompt from components: role, task, constraints, output format, tone, language, and examples. Generates a production-ready system prom"},{"name":"model_info","description":"Get detailed specs for an AI model: context window, pricing per 1K tokens, knowledge cutoff, provider, multimodal support, reasoning capabilities, and feature l"},{"name":"compare_models","description":"Compare 2-5 AI models side by side: context window, pricing, multimodal, reasoning capabilities, and provider. Returns a comparison table with a recommendation "},{"name":"http_status_lookup","description":"Look up detailed information about any HTTP status code: class, name, description, cacheability, typical causes, and handling best practices. Covers every code "},{"name":"parse_http_headers","description":"Parse a raw HTTP headers block into a structured JSON object. Detects multi-value headers, masks Authorization values, and optionally audits for missing securit"},{"name":"generate_curl","description":"Generate a curl command from request parameters. Supports GET/POST/PUT/DELETE, custom headers, JSON body, and form data. Useful for documentation, sharing, and "},{"name":"extract_todos","description":"Extract TODO, FIXME, HACK, BUG, NOTE, OPTIMIZE, and custom tags from any source code or text. Returns line numbers, tag types, and message text. Essential for t"},{"name":"detect_secrets","description":"Scan code or config files for hardcoded secrets: AWS keys, GitHub tokens, OpenAI/Anthropic API keys, Stripe secrets, JWTs, database connection strings, and gene"},{"name":"count_code_lines","description":"Count lines of code: total, code lines, comment lines, blank lines, and comment density. Supports JS/TS, Python, Java/C/C++, Ruby, Go, Shell, HTML/XML, and CSS."},{"name":"lint_commit_message","description":"Validate a git commit message against the Conventional Commits spec (feat, fix, docs, style, refactor, test, chore, ci, perf, build). Returns compliance score, "},{"name":"word_frequency","description":"Analyze word frequency in text. Returns top N words with counts and percentages. Supports English stopword filtering. Useful for content analysis, keyword extra"},{"name":"extract_links","description":"Extract all URLs, email addresses, and domain names from text. Returns categorized and deduplicated results. Useful for content auditing, link checking, and web"},{"name":"levenshtein_distance","description":"Compute the Levenshtein (edit) distance and normalized similarity ratio between two strings. Supports batch comparison. Useful for fuzzy string matching, dedupl"},{"name":"json_diff","description":"Compute a deep structural diff between two JSON values. Returns added, removed, and changed keys with dot-notation paths. Like git diff but for JSON objects — p"},{"name":"merge_json","description":"Deep merge two JSON objects. Supports three array strategies: replace (default), concat, or unique (dedup concat). Nested objects are recursively merged — overr"},{"name":"json_to_yaml","description":"Convert a JSON object to clean, human-readable YAML. Handles nested objects, arrays, multiline strings, and special characters. No external dependencies."}],"toolCount":152,"toolsHash":"68f15982d31ca5609750a928a2af7afe6206d359ce0e29af4d06a5c860409d58","serverName":"ia-qa-toolbox","capabilities":["tools"],"serverVersion":"1.0.0","protocolVersion":"2025-06-18"}},{"at":"2026-10-08T16:28:42.210Z","kind":"mcp_initialize","ok":true,"httpStatus":200,"latencyMs":21,"error":null,"detail":{"tools":[{"name":"format_json","description":"Validate and pretty-print a string that is ALREADY valid JSON. Strict by design — it is a validity gate: valid JSON comes back formatted, anything else is rejec"},{"name":"generate_uuid","description":"Generate one or more cryptographically random UUID v4 identifiers. Use this when you need unique IDs for test fixtures, database records, session tokens, or any"},{"name":"hash_text","description":"Compute a cryptographic hash of a text string. Use when you need to verify data integrity, generate content fingerprints, hash passwords (prefer SHA-256+), or p"},{"name":"count_tokens","description":"Estimate the token count of a text string using the cl100k_base approximation (~4 chars/token). Call this BEFORE sending any text to an LLM API to check if it f"},{"name":"base64_encode","description":"Encode a UTF-8 string to Base64. Use when you need to embed binary data, multi-line text, or special characters safely inside JSON fields, HTTP headers, or data"},{"name":"base64_decode","description":"Decode a Base64 string back to UTF-8 text. Use for inspecting Base64-encoded API responses, JWT payload claims, config file values, or attachment data."},{"name":"url_encode","description":"Percent-encode a string for safe use in URLs. Call this before programmatically building query strings, path segments, or form-encoded bodies to prevent injecti"},{"name":"url_decode","description":"Decode a percent-encoded URL string back to plain text. Use when parsing query parameters from raw URLs or when displaying encoded values to users."},{"name":"generate_slug","description":"Convert any string into a URL-friendly slug: lowercase, ASCII-normalized (é→e), special characters removed, spaces replaced with hyphens. Use for generating SEO"},{"name":"validate_email","description":"Validate an email address against RFC 5322 syntax before storing it, sending a transactional email, or adding it to a mailing list. Returns { valid, email } — u"},{"name":"minify_js","description":"Minify a JavaScript snippet, function, class, or module up to 50 KB using Terser. Returns minified code and byte savings. Use when embedding scripts in HTML tem"},{"name":"decode_jwt","description":"Decode a JWT (JSON Web Token) and return its header and payload without verifying the signature. Also reports whether the token is expired and the exact expiry "},{"name":"text_stats","description":"Compute comprehensive statistics for any text: character count (with and without spaces), word count, line count, sentence count, paragraph count, and estimated"},{"name":"generate_password","description":"Generate a cryptographically secure random password using crypto.randomBytes. Configurable length (4–128), uppercase letters, digits, and symbols. Use when rese"},{"name":"parse_csv","description":"Parse a CSV string into a JSON array of objects (or raw arrays). Full RFC 4180: quoted fields may contain the delimiter, embedded newlines (the Excel/Sheets mul"},{"name":"color_convert","description":"Convert a color between HEX, RGB, and HSL formats. Use when translating design tokens between CSS notations, verifying color accessibility, or normalizing color"},{"name":"regex_test","description":"Test a regular expression pattern against an input string and return all matches with their index positions and named capture groups. Use for validating user in"},{"name":"lorem_ipsum","description":"Generate Lorem Ipsum placeholder text for UI mockups, design prototypes, or test data population. Configurable paragraphs (1–10), sentences per paragraph (1–20)"},{"name":"timestamp_convert","description":"Convert between Unix timestamps (seconds or milliseconds) and ISO-8601 / UTC date strings. Auto-detects epoch vs. millisecond format. Omit input to get the curr"},{"name":"diff_text","description":"Compute a unified line-by-line diff between two text strings (LCS algorithm). Returns added/removed/unchanged line counts and formatted diff hunks with configur"},{"name":"truncate_to_tokens","description":"Truncate text to at most N tokens (cl100k_base: ~4 chars/token) to avoid exceeding an LLM context window. Optionally keeps the end of the text instead of the st"},{"name":"split_chunks","description":"Split text into chunks of at most N tokens (cl100k_base: ~4 chars/token) with optional overlap. Designed for RAG ingestion pipelines."},{"name":"extract_json_from_text","description":"Extract the first valid JSON object or array embedded in chaotic LLM output (surrounded by markdown fences, prose, or explanatory text). Handles ```json blocks "},{"name":"strip_markdown","description":"Strip all Markdown formatting (headers, bold, italic, code fences, links, lists) from text and return clean plain text. Run this before injecting scraped docume"},{"name":"estimate_llm_cost","description":"Estimate the API cost in USD for a given model and token counts. Supports all major 2024–2026 models: GPT-4o, GPT-4.1, o3, o4-mini, Claude Opus 4, Claude Sonnet"},{"name":"escape_html","description":"Escape HTML special characters (&, <, >, \", ') to their safe HTML entities. ALWAYS call this before inserting any user-provided or LLM-generated content into an"},{"name":"unescape_html","description":"Convert HTML entities (&amp;, &lt;, &gt;, &quot;, &#x27;, and numeric &#NNN;) back to plain characters. Use when processing HTML-encoded text from APIs, email c"},{"name":"fetch_veille_feed","description":"Fetch the latest QA & AI/LLM articles aggregated from curated RSS sources (Google Testing Blog, DEV.to Testing/QA/AI/LLM/Agents, Hugging Face Blog, Simon Willis"},{"name":"score_geo_signals","description":"Analyze a webpage <head> HTML (or full HTML) for GEO (Generative Engine Optimization) signals. Returns a score /60 with per-check results and improvement tips. "},{"name":"extract_json_path","description":"Extract a value from a JSON string using dot-notation path (e.g., \"user.address.city\", \"items.0.name\", \"meta.tags\"). Supports array index access via numeric pat"},{"name":"generate_json_ld","description":"Generate a ready-to-paste <script type=\"application/ld+json\"> snippet for GEO / structured data optimization. Supported types: WebSite, FAQPage, Article, Person"},{"name":"analyze_diff_bugs","description":"Pattern-based diff linter: flags a fixed set of risky shapes in changed code — query-string interpolation (SQL/Cypher/Mongo injection shape), shell interpolatio"},{"name":"generate_test_cases","description":"Generate a set of test cases (valid, edge, invalid, pairwise) for a given feature description. Declared constraints drive the boundaries: a length or numeric bo"},{"name":"run_pr_gate_pipeline","description":"Review triage for a pull request. Takes a unified git diff (`git diff HEAD`) and returns: diff-lint findings with the lines that produced them, regression impac"},{"name":"validate_mcp_response","description":"Validate that an MCP tool response conforms to expected format, schema, and content rules. Use this to QA-test any MCP server tool. Supply the tool's actual JSO"},{"name":"llm_output_validator","description":"Validate an LLM response against QA criteria: format checks (JSON, code, markdown), content rules (must-include, must-not-include), length constraints, language"},{"name":"compare_responses","description":"Compare two ALREADY-PRODUCED outputs (e.g. model A vs model B on the same task) side by side. Returns deterministic metrics (token cosine, ROUGE-L, Jaccard, len"},{"name":"analyze_responses","description":"Semantically analyze N already-produced model outputs for the SAME task (the MCP counterpart to the LLM Sandbox). Without a reference: computes consensus — pair"},{"name":"prompt_test_suite","description":"Define a test suite for a prompt: provide the system prompt, user prompt, and expected output criteria. Returns a test plan with scored rubric — use this as inp"},{"name":"mcp_server_health_check","description":"Generate a health check report for an MCP server's tool manifest. Validates tool definitions, schema quality, naming conventions, and documentation completeness"},{"name":"mcp_server_evaluate","description":"Run a full compliance evaluation against a live MCP server URL. Tests: server reachability (ping), manifest discovery (GET /mcp), schema quality (snake_case nam"},{"name":"json_schema_validate","description":"Validate a JSON value against a JSON Schema (draft-07 subset). Supports type, required, properties, items, enum, const, pattern, format (email/uri/date), minimu"},{"name":"flatten_json","description":"Flatten a nested JSON object to single-level dot-notation keys (e.g. {\"a\":{\"b\":1}} → {\"a.b\":1}), or unflatten dot-notation keys back to a nested object. Support"},{"name":"xml_to_json","description":"Convert an XML string to a JSON object. Supports attributes, nested elements, arrays, CDATA, and namespaces. Options: parse numbers, parse booleans, ignore attr"},{"name":"redact_pii","description":"Automatically detect and redact Personally Identifiable Information (PII) from text. Replaces emails, phone numbers, SSNs, credit cards, IP addresses, and JWT t"},{"name":"mock_from_schema","description":"Generate realistic mock data from a JSON Schema. Supports all common types (string, number, integer, boolean, array, object, null), format hints (email, date, d"},{"name":"transform_json_array","description":"Transform a JSON array using common operations: pluck (extract specific fields), filter (by field value), sort_by (field), group_by (field), count_by (field), u"},{"name":"json_to_csv","description":"Convert a JSON array of objects to CSV format. Automatically detects columns from all object keys. Handles quoting and escaping per RFC 4180."},{"name":"case_convert","description":"Convert a string between naming conventions: camelCase, PascalCase, snake_case, kebab-case, UPPER_SNAKE_CASE, dot.case, Title Case. Essential for code generatio"},{"name":"sort_lines","description":"Sort, deduplicate, reverse, or filter lines of text. Useful for cleaning import lists, dependencies, log files, and config entries."},{"name":"number_base_convert","description":"Convert numbers between bases: decimal, binary, octal, hexadecimal, or any base 2–36. Auto-detects 0x, 0b, 0o prefixes."},{"name":"validate_url","description":"Parse and validate a URL. Returns decomposed components: protocol, hostname, port, path, query parameters, hash, and origin."},{"name":"check_contrast_ratio","description":"Calculate WCAG 2.1 contrast ratio between two colors. Returns ratio and compliance for AA/AAA normal and large text."},{"name":"html_to_markdown","description":"Convert HTML to clean Markdown. Strips scripts, styles, nav, ads, and comments. Converts headings, lists, links, images, code blocks. Ideal for preparing web co"},{"name":"cron_parse","description":"Parse a cron expression into a human-readable schedule description. Supports standard 5-field cron (minute hour day month weekday)."},{"name":"cron_validator","description":"Validate a 5-field cron expression, explain the schedule, and preview the next execution times. Use this to debug cron jobs before they reach production. Return"},{"name":"calculate_readability","description":"Calculate readability scores: Flesch Reading Ease, Flesch-Kincaid Grade Level, Coleman-Liau Index, and Automated Readability Index. Useful for evaluating LLM ou"},{"name":"normalize_whitespace","description":"Normalize whitespace: trim trailing spaces, collapse blank lines, normalize line endings (LF/CRLF), convert tabs to spaces. Useful for cleaning code, configs, a"},{"name":"embedding_similarity","description":"Compute text similarity using local algorithms (Bag of Words, TF-IDF, Character N-grams). No API key needed — runs entirely in-process. NOT real embeddings: for"},{"name":"llm_format_check","description":"Validate that an LLM output matches an expected format: JSON, Markdown, code block, bullet list, numbered list, table, YAML, XML, or custom regex. Essential for"},{"name":"hallucination_check","description":"Lexical hallucination check: verifies an LLM answer's words, numbers and polarity against the provided source/context. Fast, deterministic, no API key needed. E"},{"name":"prompt_injection_scan","description":"Scan user input or prompts for common prompt injection patterns. Detects system prompt overrides, jailbreak attempts, role manipulation, encoding tricks, delimi"},{"name":"token_budget_calculator","description":"Plan token allocation across system prompt, user input, context/RAG chunks, and expected output. Warns if budget exceeds model context window. Supports 25+ mode"},{"name":"consistency_check","description":"Compare multiple LLM responses to the same prompt and detect inconsistencies using Jaccard word-overlap similarity and fact drift (number comparison). Fast, det"},{"name":"llm_json_schema_check","description":"Validate that an LLM JSON output matches a JSON Schema definition. Tests required fields, types, enums, nested objects, and arrays. Critical for function-callin"},{"name":"latency_benchmark","description":"Measure response time of one or more HTTP endpoints (GET/POST). Runs N iterations and returns min/max/avg/p95 latency. Useful for API and MCP server benchmarkin"},{"name":"response_quality_score","description":"Score an LLM response against the criteria you pass: coverage of expected_keywords and compliance with max_length. Returns a 0-100 score over the criteria actua"},{"name":"rag_relevance_rank","description":"Rank an array of text chunks by relevance to a query using TF-IDF scoring. Simulates retrieval ranking for RAG testing without needing embeddings or an API."},{"name":"toxicity_scan","description":"Scan text for toxic language, hate speech, bias/stereotype framing, violence, sexual and self-harm content. Lexical + structural pattern matching (identity term"},{"name":"guardrail_test","description":"Test an LLM response against a set of guardrail rules: must-include, must-not-include, max length, required format, language, forbidden patterns, and custom reg"},{"name":"function_call_validate","description":"Validate an LLM function call / tool_use output: check that function name is in allowed list, arguments match expected schema, no extra/missing args. For OpenAI"},{"name":"conversation_analyze","description":"Analyze a multi-turn conversation for context retention, topic drift, instruction following, and repetition. Accepts messages array [{role, content}]. Essential"},{"name":"mcp_schema_lint","description":"Lint an MCP tool definition for best practices: naming conventions, description quality, schema completeness, required fields consistency, description length. R"},{"name":"cot_analyzer","description":"Analyze a Chain-of-Thought (CoT) or reasoning trace from an LLM. Detects step count, logical flow, conclusion presence, backtracking, and estimates reasoning de"},{"name":"ab_test_report","description":"Generate an A/B test report comparing two prompts or model configurations. Accepts arrays of scores and returns statistical comparison: mean, median, std deviat"},{"name":"context_window_check","description":"Given an array of message objects [{role, content}], estimate total token usage and check if it fits in the target model's context window. Warns about truncatio"},{"name":"vector_similarity","description":"Compute similarity/distance between two float vectors: cosine similarity, dot product, Euclidean and Manhattan distance. Essential for vector DB relevance scori"},{"name":"normalize_vector","description":"L2-normalize a float vector (produce a unit vector with norm=1). Required by many vector DBs (Pinecone, Qdrant cosine). Supports batch normalization of up to 10"},{"name":"vector_quantize","description":"Simulate int8 or int4 quantization of float32 embedding vectors. Reduces storage by 4x (int8) or 8x (int4). Returns quantized values, scale factor, and precisio"},{"name":"vector_stats","description":"Compute statistics for a float vector or matrix of vectors: mean, std, L2 norm, min, max, sparsity, top-K indices. Useful for debugging embedding quality and an"},{"name":"bm25_score","description":"Compute BM25 relevance score between a query and one or more documents. BM25 is the industry-standard keyword-based ranking algorithm used in Elasticsearch, Ope"},{"name":"build_rag_prompt","description":"Assemble a complete RAG (Retrieval-Augmented Generation) prompt from retrieved context chunks and a user query. Handles token budgeting, citation numbering, sys"},{"name":"prompt_template_fill","description":"Fill a prompt template with variables. Supports {{variable}} syntax and {{#if key}}...{{/if}} conditional blocks. Returns the filled prompt and lists unfilled v"},{"name":"few_shot_formatter","description":"Format few-shot examples for LLM prompts. Converts example pairs into formatted blocks. Supports chat format (User/Assistant), XML tags, Markdown, or plain text"},{"name":"system_prompt_builder","description":"Build a structured system prompt from components: role, task, constraints, output format, tone, language, and examples. Generates a production-ready system prom"},{"name":"model_info","description":"Get detailed specs for an AI model: context window, pricing per 1K tokens, knowledge cutoff, provider, multimodal support, reasoning capabilities, and feature l"},{"name":"compare_models","description":"Compare 2-5 AI models side by side: context window, pricing, multimodal, reasoning capabilities, and provider. Returns a comparison table with a recommendation "},{"name":"http_status_lookup","description":"Look up detailed information about any HTTP status code: class, name, description, cacheability, typical causes, and handling best practices. Covers every code "},{"name":"parse_http_headers","description":"Parse a raw HTTP headers block into a structured JSON object. Detects multi-value headers, masks Authorization values, and optionally audits for missing securit"},{"name":"generate_curl","description":"Generate a curl command from request parameters. Supports GET/POST/PUT/DELETE, custom headers, JSON body, and form data. Useful for documentation, sharing, and "},{"name":"extract_todos","description":"Extract TODO, FIXME, HACK, BUG, NOTE, OPTIMIZE, and custom tags from any source code or text. Returns line numbers, tag types, and message text. Essential for t"},{"name":"detect_secrets","description":"Scan code or config files for hardcoded secrets: AWS keys, GitHub tokens, OpenAI/Anthropic API keys, Stripe secrets, JWTs, database connection strings, and gene"},{"name":"count_code_lines","description":"Count lines of code: total, code lines, comment lines, blank lines, and comment density. Supports JS/TS, Python, Java/C/C++, Ruby, Go, Shell, HTML/XML, and CSS."},{"name":"lint_commit_message","description":"Validate a git commit message against the Conventional Commits spec (feat, fix, docs, style, refactor, test, chore, ci, perf, build). Returns compliance score, "},{"name":"word_frequency","description":"Analyze word frequency in text. Returns top N words with counts and percentages. Supports English stopword filtering. Useful for content analysis, keyword extra"},{"name":"extract_links","description":"Extract all URLs, email addresses, and domain names from text. Returns categorized and deduplicated results. Useful for content auditing, link checking, and web"},{"name":"levenshtein_distance","description":"Compute the Levenshtein (edit) distance and normalized similarity ratio between two strings. Supports batch comparison. Useful for fuzzy string matching, dedupl"},{"name":"json_diff","description":"Compute a deep structural diff between two JSON values. Returns added, removed, and changed keys with dot-notation paths. Like git diff but for JSON objects — p"},{"name":"merge_json","description":"Deep merge two JSON objects. Supports three array strategies: replace (default), concat, or unique (dedup concat). Nested objects are recursively merged — overr"},{"name":"json_to_yaml","description":"Convert a JSON object to clean, human-readable YAML. Handles nested objects, arrays, multiline strings, and special characters. No external dependencies."}],"toolCount":152,"toolsHash":"68f15982d31ca5609750a928a2af7afe6206d359ce0e29af4d06a5c860409d58","serverName":"ia-qa-toolbox","capabilities":["tools"],"serverVersion":"1.0.0","protocolVersion":"2025-06-18"}},{"at":"2026-10-08T10:23:44.475Z","kind":"mcp_initialize","ok":true,"httpStatus":200,"latencyMs":24,"error":null,"detail":{"tools":[{"name":"format_json","description":"Validate and pretty-print a string that is ALREADY valid JSON. Strict by design — it is a validity gate: valid JSON comes back formatted, anything else is rejec"},{"name":"generate_uuid","description":"Generate one or more cryptographically random UUID v4 identifiers. Use this when you need unique IDs for test fixtures, database records, session tokens, or any"},{"name":"hash_text","description":"Compute a cryptographic hash of a text string. Use when you need to verify data integrity, generate content fingerprints, hash passwords (prefer SHA-256+), or p"},{"name":"count_tokens","description":"Estimate the token count of a text string using the cl100k_base approximation (~4 chars/token). Call this BEFORE sending any text to an LLM API to check if it f"},{"name":"base64_encode","description":"Encode a UTF-8 string to Base64. Use when you need to embed binary data, multi-line text, or special characters safely inside JSON fields, HTTP headers, or data"},{"name":"base64_decode","description":"Decode a Base64 string back to UTF-8 text. Use for inspecting Base64-encoded API responses, JWT payload claims, config file values, or attachment data."},{"name":"url_encode","description":"Percent-encode a string for safe use in URLs. Call this before programmatically building query strings, path segments, or form-encoded bodies to prevent injecti"},{"name":"url_decode","description":"Decode a percent-encoded URL string back to plain text. Use when parsing query parameters from raw URLs or when displaying encoded values to users."},{"name":"generate_slug","description":"Convert any string into a URL-friendly slug: lowercase, ASCII-normalized (é→e), special characters removed, spaces replaced with hyphens. Use for generating SEO"},{"name":"validate_email","description":"Validate an email address against RFC 5322 syntax before storing it, sending a transactional email, or adding it to a mailing list. Returns { valid, email } — u"},{"name":"minify_js","description":"Minify a JavaScript snippet, function, class, or module up to 50 KB using Terser. Returns minified code and byte savings. Use when embedding scripts in HTML tem"},{"name":"decode_jwt","description":"Decode a JWT (JSON Web Token) and return its header and payload without verifying the signature. Also reports whether the token is expired and the exact expiry "},{"name":"text_stats","description":"Compute comprehensive statistics for any text: character count (with and without spaces), word count, line count, sentence count, paragraph count, and estimated"},{"name":"generate_password","description":"Generate a cryptographically secure random password using crypto.randomBytes. Configurable length (4–128), uppercase letters, digits, and symbols. Use when rese"},{"name":"parse_csv","description":"Parse a CSV string into a JSON array of objects (or raw arrays). Full RFC 4180: quoted fields may contain the delimiter, embedded newlines (the Excel/Sheets mul"},{"name":"color_convert","description":"Convert a color between HEX, RGB, and HSL formats. Use when translating design tokens between CSS notations, verifying color accessibility, or normalizing color"},{"name":"regex_test","description":"Test a regular expression pattern against an input string and return all matches with their index positions and named capture groups. Use for validating user in"},{"name":"lorem_ipsum","description":"Generate Lorem Ipsum placeholder text for UI mockups, design prototypes, or test data population. Configurable paragraphs (1–10), sentences per paragraph (1–20)"},{"name":"timestamp_convert","description":"Convert between Unix timestamps (seconds or milliseconds) and ISO-8601 / UTC date strings. Auto-detects epoch vs. millisecond format. Omit input to get the curr"},{"name":"diff_text","description":"Compute a unified line-by-line diff between two text strings (LCS algorithm). Returns added/removed/unchanged line counts and formatted diff hunks with configur"},{"name":"truncate_to_tokens","description":"Truncate text to at most N tokens (cl100k_base: ~4 chars/token) to avoid exceeding an LLM context window. Optionally keeps the end of the text instead of the st"},{"name":"split_chunks","description":"Split text into chunks of at most N tokens (cl100k_base: ~4 chars/token) with optional overlap. Designed for RAG ingestion pipelines."},{"name":"extract_json_from_text","description":"Extract the first valid JSON object or array embedded in chaotic LLM output (surrounded by markdown fences, prose, or explanatory text). Handles ```json blocks "},{"name":"strip_markdown","description":"Strip all Markdown formatting (headers, bold, italic, code fences, links, lists) from text and return clean plain text. Run this before injecting scraped docume"},{"name":"estimate_llm_cost","description":"Estimate the API cost in USD for a given model and token counts. Supports all major 2024–2026 models: GPT-4o, GPT-4.1, o3, o4-mini, Claude Opus 4, Claude Sonnet"},{"name":"escape_html","description":"Escape HTML special characters (&, <, >, \", ') to their safe HTML entities. ALWAYS call this before inserting any user-provided or LLM-generated content into an"},{"name":"unescape_html","description":"Convert HTML entities (&amp;, &lt;, &gt;, &quot;, &#x27;, and numeric &#NNN;) back to plain characters. Use when processing HTML-encoded text from APIs, email c"},{"name":"fetch_veille_feed","description":"Fetch the latest QA & AI/LLM articles aggregated from curated RSS sources (Google Testing Blog, DEV.to Testing/QA/AI/LLM/Agents, Hugging Face Blog, Simon Willis"},{"name":"score_geo_signals","description":"Analyze a webpage <head> HTML (or full HTML) for GEO (Generative Engine Optimization) signals. Returns a score /60 with per-check results and improvement tips. "},{"name":"extract_json_path","description":"Extract a value from a JSON string using dot-notation path (e.g., \"user.address.city\", \"items.0.name\", \"meta.tags\"). Supports array index access via numeric pat"},{"name":"generate_json_ld","description":"Generate a ready-to-paste <script type=\"application/ld+json\"> snippet for GEO / structured data optimization. Supported types: WebSite, FAQPage, Article, Person"},{"name":"analyze_diff_bugs","description":"Pattern-based diff linter: flags a fixed set of risky shapes in changed code — query-string interpolation (SQL/Cypher/Mongo injection shape), shell interpolatio"},{"name":"generate_test_cases","description":"Generate a set of test cases (valid, edge, invalid, pairwise) for a given feature description. Declared constraints drive the boundaries: a length or numeric bo"},{"name":"run_pr_gate_pipeline","description":"Review triage for a pull request. Takes a unified git diff (`git diff HEAD`) and returns: diff-lint findings with the lines that produced them, regression impac"},{"name":"validate_mcp_response","description":"Validate that an MCP tool response conforms to expected format, schema, and content rules. Use this to QA-test any MCP server tool. Supply the tool's actual JSO"},{"name":"llm_output_validator","description":"Validate an LLM response against QA criteria: format checks (JSON, code, markdown), content rules (must-include, must-not-include), length constraints, language"},{"name":"compare_responses","description":"Compare two ALREADY-PRODUCED outputs (e.g. model A vs model B on the same task) side by side. Returns deterministic metrics (token cosine, ROUGE-L, Jaccard, len"},{"name":"analyze_responses","description":"Semantically analyze N already-produced model outputs for the SAME task (the MCP counterpart to the LLM Sandbox). Without a reference: computes consensus — pair"},{"name":"prompt_test_suite","description":"Define a test suite for a prompt: provide the system prompt, user prompt, and expected output criteria. Returns a test plan with scored rubric — use this as inp"},{"name":"mcp_server_health_check","description":"Generate a health check report for an MCP server's tool manifest. Validates tool definitions, schema quality, naming conventions, and documentation completeness"},{"name":"mcp_server_evaluate","description":"Run a full compliance evaluation against a live MCP server URL. Tests: server reachability (ping), manifest discovery (GET /mcp), schema quality (snake_case nam"},{"name":"json_schema_validate","description":"Validate a JSON value against a JSON Schema (draft-07 subset). Supports type, required, properties, items, enum, const, pattern, format (email/uri/date), minimu"},{"name":"flatten_json","description":"Flatten a nested JSON object to single-level dot-notation keys (e.g. {\"a\":{\"b\":1}} → {\"a.b\":1}), or unflatten dot-notation keys back to a nested object. Support"},{"name":"xml_to_json","description":"Convert an XML string to a JSON object. Supports attributes, nested elements, arrays, CDATA, and namespaces. Options: parse numbers, parse booleans, ignore attr"},{"name":"redact_pii","description":"Automatically detect and redact Personally Identifiable Information (PII) from text. Replaces emails, phone numbers, SSNs, credit cards, IP addresses, and JWT t"},{"name":"mock_from_schema","description":"Generate realistic mock data from a JSON Schema. Supports all common types (string, number, integer, boolean, array, object, null), format hints (email, date, d"},{"name":"transform_json_array","description":"Transform a JSON array using common operations: pluck (extract specific fields), filter (by field value), sort_by (field), group_by (field), count_by (field), u"},{"name":"json_to_csv","description":"Convert a JSON array of objects to CSV format. Automatically detects columns from all object keys. Handles quoting and escaping per RFC 4180."},{"name":"case_convert","description":"Convert a string between naming conventions: camelCase, PascalCase, snake_case, kebab-case, UPPER_SNAKE_CASE, dot.case, Title Case. Essential for code generatio"},{"name":"sort_lines","description":"Sort, deduplicate, reverse, or filter lines of text. Useful for cleaning import lists, dependencies, log files, and config entries."},{"name":"number_base_convert","description":"Convert numbers between bases: decimal, binary, octal, hexadecimal, or any base 2–36. Auto-detects 0x, 0b, 0o prefixes."},{"name":"validate_url","description":"Parse and validate a URL. Returns decomposed components: protocol, hostname, port, path, query parameters, hash, and origin."},{"name":"check_contrast_ratio","description":"Calculate WCAG 2.1 contrast ratio between two colors. Returns ratio and compliance for AA/AAA normal and large text."},{"name":"html_to_markdown","description":"Convert HTML to clean Markdown. Strips scripts, styles, nav, ads, and comments. Converts headings, lists, links, images, code blocks. Ideal for preparing web co"},{"name":"cron_parse","description":"Parse a cron expression into a human-readable schedule description. Supports standard 5-field cron (minute hour day month weekday)."},{"name":"cron_validator","description":"Validate a 5-field cron expression, explain the schedule, and preview the next execution times. Use this to debug cron jobs before they reach production. Return"},{"name":"calculate_readability","description":"Calculate readability scores: Flesch Reading Ease, Flesch-Kincaid Grade Level, Coleman-Liau Index, and Automated Readability Index. Useful for evaluating LLM ou"},{"name":"normalize_whitespace","description":"Normalize whitespace: trim trailing spaces, collapse blank lines, normalize line endings (LF/CRLF), convert tabs to spaces. Useful for cleaning code, configs, a"},{"name":"embedding_similarity","description":"Compute text similarity using local algorithms (Bag of Words, TF-IDF, Character N-grams). No API key needed — runs entirely in-process. NOT real embeddings: for"},{"name":"llm_format_check","description":"Validate that an LLM output matches an expected format: JSON, Markdown, code block, bullet list, numbered list, table, YAML, XML, or custom regex. Essential for"},{"name":"hallucination_check","description":"Lexical hallucination check: verifies an LLM answer's words, numbers and polarity against the provided source/context. Fast, deterministic, no API key needed. E"},{"name":"prompt_injection_scan","description":"Scan user input or prompts for common prompt injection patterns. Detects system prompt overrides, jailbreak attempts, role manipulation, encoding tricks, delimi"},{"name":"token_budget_calculator","description":"Plan token allocation across system prompt, user input, context/RAG chunks, and expected output. Warns if budget exceeds model context window. Supports 25+ mode"},{"name":"consistency_check","description":"Compare multiple LLM responses to the same prompt and detect inconsistencies using Jaccard word-overlap similarity and fact drift (number comparison). Fast, det"},{"name":"llm_json_schema_check","description":"Validate that an LLM JSON output matches a JSON Schema definition. Tests required fields, types, enums, nested objects, and arrays. Critical for function-callin"},{"name":"latency_benchmark","description":"Measure response time of one or more HTTP endpoints (GET/POST). Runs N iterations and returns min/max/avg/p95 latency. Useful for API and MCP server benchmarkin"},{"name":"response_quality_score","description":"Score an LLM response against the criteria you pass: coverage of expected_keywords and compliance with max_length. Returns a 0-100 score over the criteria actua"},{"name":"rag_relevance_rank","description":"Rank an array of text chunks by relevance to a query using TF-IDF scoring. Simulates retrieval ranking for RAG testing without needing embeddings or an API."},{"name":"toxicity_scan","description":"Scan text for toxic language, hate speech, bias/stereotype framing, violence, sexual and self-harm content. Lexical + structural pattern matching (identity term"},{"name":"guardrail_test","description":"Test an LLM response against a set of guardrail rules: must-include, must-not-include, max length, required format, language, forbidden patterns, and custom reg"},{"name":"function_call_validate","description":"Validate an LLM function call / tool_use output: check that function name is in allowed list, arguments match expected schema, no extra/missing args. For OpenAI"},{"name":"conversation_analyze","description":"Analyze a multi-turn conversation for context retention, topic drift, instruction following, and repetition. Accepts messages array [{role, content}]. Essential"},{"name":"mcp_schema_lint","description":"Lint an MCP tool definition for best practices: naming conventions, description quality, schema completeness, required fields consistency, description length. R"},{"name":"cot_analyzer","description":"Analyze a Chain-of-Thought (CoT) or reasoning trace from an LLM. Detects step count, logical flow, conclusion presence, backtracking, and estimates reasoning de"},{"name":"ab_test_report","description":"Generate an A/B test report comparing two prompts or model configurations. Accepts arrays of scores and returns statistical comparison: mean, median, std deviat"},{"name":"context_window_check","description":"Given an array of message objects [{role, content}], estimate total token usage and check if it fits in the target model's context window. Warns about truncatio"},{"name":"vector_similarity","description":"Compute similarity/distance between two float vectors: cosine similarity, dot product, Euclidean and Manhattan distance. Essential for vector DB relevance scori"},{"name":"normalize_vector","description":"L2-normalize a float vector (produce a unit vector with norm=1). Required by many vector DBs (Pinecone, Qdrant cosine). Supports batch normalization of up to 10"},{"name":"vector_quantize","description":"Simulate int8 or int4 quantization of float32 embedding vectors. Reduces storage by 4x (int8) or 8x (int4). Returns quantized values, scale factor, and precisio"},{"name":"vector_stats","description":"Compute statistics for a float vector or matrix of vectors: mean, std, L2 norm, min, max, sparsity, top-K indices. Useful for debugging embedding quality and an"},{"name":"bm25_score","description":"Compute BM25 relevance score between a query and one or more documents. BM25 is the industry-standard keyword-based ranking algorithm used in Elasticsearch, Ope"},{"name":"build_rag_prompt","description":"Assemble a complete RAG (Retrieval-Augmented Generation) prompt from retrieved context chunks and a user query. Handles token budgeting, citation numbering, sys"},{"name":"prompt_template_fill","description":"Fill a prompt template with variables. Supports {{variable}} syntax and {{#if key}}...{{/if}} conditional blocks. Returns the filled prompt and lists unfilled v"},{"name":"few_shot_formatter","description":"Format few-shot examples for LLM prompts. Converts example pairs into formatted blocks. Supports chat format (User/Assistant), XML tags, Markdown, or plain text"},{"name":"system_prompt_builder","description":"Build a structured system prompt from components: role, task, constraints, output format, tone, language, and examples. Generates a production-ready system prom"},{"name":"model_info","description":"Get detailed specs for an AI model: context window, pricing per 1K tokens, knowledge cutoff, provider, multimodal support, reasoning capabilities, and feature l"},{"name":"compare_models","description":"Compare 2-5 AI models side by side: context window, pricing, multimodal, reasoning capabilities, and provider. Returns a comparison table with a recommendation "},{"name":"http_status_lookup","description":"Look up detailed information about any HTTP status code: class, name, description, cacheability, typical causes, and handling best practices. Covers every code "},{"name":"parse_http_headers","description":"Parse a raw HTTP headers block into a structured JSON object. Detects multi-value headers, masks Authorization values, and optionally audits for missing securit"},{"name":"generate_curl","description":"Generate a curl command from request parameters. Supports GET/POST/PUT/DELETE, custom headers, JSON body, and form data. Useful for documentation, sharing, and "},{"name":"extract_todos","description":"Extract TODO, FIXME, HACK, BUG, NOTE, OPTIMIZE, and custom tags from any source code or text. Returns line numbers, tag types, and message text. Essential for t"},{"name":"detect_secrets","description":"Scan code or config files for hardcoded secrets: AWS keys, GitHub tokens, OpenAI/Anthropic API keys, Stripe secrets, JWTs, database connection strings, and gene"},{"name":"count_code_lines","description":"Count lines of code: total, code lines, comment lines, blank lines, and comment density. Supports JS/TS, Python, Java/C/C++, Ruby, Go, Shell, HTML/XML, and CSS."},{"name":"lint_commit_message","description":"Validate a git commit message against the Conventional Commits spec (feat, fix, docs, style, refactor, test, chore, ci, perf, build). Returns compliance score, "},{"name":"word_frequency","description":"Analyze word frequency in text. Returns top N words with counts and percentages. Supports English stopword filtering. Useful for content analysis, keyword extra"},{"name":"extract_links","description":"Extract all URLs, email addresses, and domain names from text. Returns categorized and deduplicated results. Useful for content auditing, link checking, and web"},{"name":"levenshtein_distance","description":"Compute the Levenshtein (edit) distance and normalized similarity ratio between two strings. Supports batch comparison. Useful for fuzzy string matching, dedupl"},{"name":"json_diff","description":"Compute a deep structural diff between two JSON values. Returns added, removed, and changed keys with dot-notation paths. Like git diff but for JSON objects — p"},{"name":"merge_json","description":"Deep merge two JSON objects. Supports three array strategies: replace (default), concat, or unique (dedup concat). Nested objects are recursively merged — overr"},{"name":"json_to_yaml","description":"Convert a JSON object to clean, human-readable YAML. Handles nested objects, arrays, multiline strings, and special characters. No external dependencies."}],"toolCount":152,"toolsHash":"68f15982d31ca5609750a928a2af7afe6206d359ce0e29af4d06a5c860409d58","serverName":"ia-qa-toolbox","capabilities":["tools"],"serverVersion":"1.0.0","protocolVersion":"2025-06-18"}}],"tools":[{"name":"format_json","description":"Validate and pretty-print a string that is ALREADY valid JSON. Strict by design — it is a validity gate: valid JSON comes back formatted, anything else is rejec"},{"name":"generate_uuid","description":"Generate one or more cryptographically random UUID v4 identifiers. Use this when you need unique IDs for test fixtures, database records, session tokens, or any"},{"name":"hash_text","description":"Compute a cryptographic hash of a text string. Use when you need to verify data integrity, generate content fingerprints, hash passwords (prefer SHA-256+), or p"},{"name":"count_tokens","description":"Estimate the token count of a text string using the cl100k_base approximation (~4 chars/token). Call this BEFORE sending any text to an LLM API to check if it f"},{"name":"base64_encode","description":"Encode a UTF-8 string to Base64. Use when you need to embed binary data, multi-line text, or special characters safely inside JSON fields, HTTP headers, or data"},{"name":"base64_decode","description":"Decode a Base64 string back to UTF-8 text. Use for inspecting Base64-encoded API responses, JWT payload claims, config file values, or attachment data."},{"name":"url_encode","description":"Percent-encode a string for safe use in URLs. Call this before programmatically building query strings, path segments, or form-encoded bodies to prevent injecti"},{"name":"url_decode","description":"Decode a percent-encoded URL string back to plain text. Use when parsing query parameters from raw URLs or when displaying encoded values to users."},{"name":"generate_slug","description":"Convert any string into a URL-friendly slug: lowercase, ASCII-normalized (é→e), special characters removed, spaces replaced with hyphens. Use for generating SEO"},{"name":"validate_email","description":"Validate an email address against RFC 5322 syntax before storing it, sending a transactional email, or adding it to a mailing list. Returns { valid, email } — u"},{"name":"minify_js","description":"Minify a JavaScript snippet, function, class, or module up to 50 KB using Terser. Returns minified code and byte savings. Use when embedding scripts in HTML tem"},{"name":"decode_jwt","description":"Decode a JWT (JSON Web Token) and return its header and payload without verifying the signature. Also reports whether the token is expired and the exact expiry "},{"name":"text_stats","description":"Compute comprehensive statistics for any text: character count (with and without spaces), word count, line count, sentence count, paragraph count, and estimated"},{"name":"generate_password","description":"Generate a cryptographically secure random password using crypto.randomBytes. Configurable length (4–128), uppercase letters, digits, and symbols. Use when rese"},{"name":"parse_csv","description":"Parse a CSV string into a JSON array of objects (or raw arrays). Full RFC 4180: quoted fields may contain the delimiter, embedded newlines (the Excel/Sheets mul"},{"name":"color_convert","description":"Convert a color between HEX, RGB, and HSL formats. Use when translating design tokens between CSS notations, verifying color accessibility, or normalizing color"},{"name":"regex_test","description":"Test a regular expression pattern against an input string and return all matches with their index positions and named capture groups. Use for validating user in"},{"name":"lorem_ipsum","description":"Generate Lorem Ipsum placeholder text for UI mockups, design prototypes, or test data population. Configurable paragraphs (1–10), sentences per paragraph (1–20)"},{"name":"timestamp_convert","description":"Convert between Unix timestamps (seconds or milliseconds) and ISO-8601 / UTC date strings. Auto-detects epoch vs. millisecond format. Omit input to get the curr"},{"name":"diff_text","description":"Compute a unified line-by-line diff between two text strings (LCS algorithm). Returns added/removed/unchanged line counts and formatted diff hunks with configur"},{"name":"truncate_to_tokens","description":"Truncate text to at most N tokens (cl100k_base: ~4 chars/token) to avoid exceeding an LLM context window. Optionally keeps the end of the text instead of the st"},{"name":"split_chunks","description":"Split text into chunks of at most N tokens (cl100k_base: ~4 chars/token) with optional overlap. Designed for RAG ingestion pipelines."},{"name":"extract_json_from_text","description":"Extract the first valid JSON object or array embedded in chaotic LLM output (surrounded by markdown fences, prose, or explanatory text). Handles ```json blocks "},{"name":"strip_markdown","description":"Strip all Markdown formatting (headers, bold, italic, code fences, links, lists) from text and return clean plain text. Run this before injecting scraped docume"},{"name":"estimate_llm_cost","description":"Estimate the API cost in USD for a given model and token counts. Supports all major 2024–2026 models: GPT-4o, GPT-4.1, o3, o4-mini, Claude Opus 4, Claude Sonnet"},{"name":"escape_html","description":"Escape HTML special characters (&, <, >, \", ') to their safe HTML entities. ALWAYS call this before inserting any user-provided or LLM-generated content into an"},{"name":"unescape_html","description":"Convert HTML entities (&amp;, &lt;, &gt;, &quot;, &#x27;, and numeric &#NNN;) back to plain characters. Use when processing HTML-encoded text from APIs, email c"},{"name":"fetch_veille_feed","description":"Fetch the latest QA & AI/LLM articles aggregated from curated RSS sources (Google Testing Blog, DEV.to Testing/QA/AI/LLM/Agents, Hugging Face Blog, Simon Willis"},{"name":"score_geo_signals","description":"Analyze a webpage <head> HTML (or full HTML) for GEO (Generative Engine Optimization) signals. Returns a score /60 with per-check results and improvement tips. "},{"name":"extract_json_path","description":"Extract a value from a JSON string using dot-notation path (e.g., \"user.address.city\", \"items.0.name\", \"meta.tags\"). Supports array index access via numeric pat"},{"name":"generate_json_ld","description":"Generate a ready-to-paste <script type=\"application/ld+json\"> snippet for GEO / structured data optimization. Supported types: WebSite, FAQPage, Article, Person"},{"name":"analyze_diff_bugs","description":"Pattern-based diff linter: flags a fixed set of risky shapes in changed code — query-string interpolation (SQL/Cypher/Mongo injection shape), shell interpolatio"},{"name":"generate_test_cases","description":"Generate a set of test cases (valid, edge, invalid, pairwise) for a given feature description. Declared constraints drive the boundaries: a length or numeric bo"},{"name":"run_pr_gate_pipeline","description":"Review triage for a pull request. Takes a unified git diff (`git diff HEAD`) and returns: diff-lint findings with the lines that produced them, regression impac"},{"name":"validate_mcp_response","description":"Validate that an MCP tool response conforms to expected format, schema, and content rules. Use this to QA-test any MCP server tool. Supply the tool's actual JSO"},{"name":"llm_output_validator","description":"Validate an LLM response against QA criteria: format checks (JSON, code, markdown), content rules (must-include, must-not-include), length constraints, language"},{"name":"compare_responses","description":"Compare two ALREADY-PRODUCED outputs (e.g. model A vs model B on the same task) side by side. Returns deterministic metrics (token cosine, ROUGE-L, Jaccard, len"},{"name":"analyze_responses","description":"Semantically analyze N already-produced model outputs for the SAME task (the MCP counterpart to the LLM Sandbox). Without a reference: computes consensus — pair"},{"name":"prompt_test_suite","description":"Define a test suite for a prompt: provide the system prompt, user prompt, and expected output criteria. Returns a test plan with scored rubric — use this as inp"},{"name":"mcp_server_health_check","description":"Generate a health check report for an MCP server's tool manifest. Validates tool definitions, schema quality, naming conventions, and documentation completeness"},{"name":"mcp_server_evaluate","description":"Run a full compliance evaluation against a live MCP server URL. Tests: server reachability (ping), manifest discovery (GET /mcp), schema quality (snake_case nam"},{"name":"json_schema_validate","description":"Validate a JSON value against a JSON Schema (draft-07 subset). Supports type, required, properties, items, enum, const, pattern, format (email/uri/date), minimu"},{"name":"flatten_json","description":"Flatten a nested JSON object to single-level dot-notation keys (e.g. {\"a\":{\"b\":1}} → {\"a.b\":1}), or unflatten dot-notation keys back to a nested object. Support"},{"name":"xml_to_json","description":"Convert an XML string to a JSON object. Supports attributes, nested elements, arrays, CDATA, and namespaces. Options: parse numbers, parse booleans, ignore attr"},{"name":"redact_pii","description":"Automatically detect and redact Personally Identifiable Information (PII) from text. Replaces emails, phone numbers, SSNs, credit cards, IP addresses, and JWT t"},{"name":"mock_from_schema","description":"Generate realistic mock data from a JSON Schema. Supports all common types (string, number, integer, boolean, array, object, null), format hints (email, date, d"},{"name":"transform_json_array","description":"Transform a JSON array using common operations: pluck (extract specific fields), filter (by field value), sort_by (field), group_by (field), count_by (field), u"},{"name":"json_to_csv","description":"Convert a JSON array of objects to CSV format. Automatically detects columns from all object keys. Handles quoting and escaping per RFC 4180."},{"name":"case_convert","description":"Convert a string between naming conventions: camelCase, PascalCase, snake_case, kebab-case, UPPER_SNAKE_CASE, dot.case, Title Case. Essential for code generatio"},{"name":"sort_lines","description":"Sort, deduplicate, reverse, or filter lines of text. Useful for cleaning import lists, dependencies, log files, and config entries."},{"name":"number_base_convert","description":"Convert numbers between bases: decimal, binary, octal, hexadecimal, or any base 2–36. Auto-detects 0x, 0b, 0o prefixes."},{"name":"validate_url","description":"Parse and validate a URL. Returns decomposed components: protocol, hostname, port, path, query parameters, hash, and origin."},{"name":"check_contrast_ratio","description":"Calculate WCAG 2.1 contrast ratio between two colors. Returns ratio and compliance for AA/AAA normal and large text."},{"name":"html_to_markdown","description":"Convert HTML to clean Markdown. Strips scripts, styles, nav, ads, and comments. Converts headings, lists, links, images, code blocks. Ideal for preparing web co"},{"name":"cron_parse","description":"Parse a cron expression into a human-readable schedule description. Supports standard 5-field cron (minute hour day month weekday)."},{"name":"cron_validator","description":"Validate a 5-field cron expression, explain the schedule, and preview the next execution times. Use this to debug cron jobs before they reach production. Return"},{"name":"calculate_readability","description":"Calculate readability scores: Flesch Reading Ease, Flesch-Kincaid Grade Level, Coleman-Liau Index, and Automated Readability Index. Useful for evaluating LLM ou"},{"name":"normalize_whitespace","description":"Normalize whitespace: trim trailing spaces, collapse blank lines, normalize line endings (LF/CRLF), convert tabs to spaces. Useful for cleaning code, configs, a"},{"name":"embedding_similarity","description":"Compute text similarity using local algorithms (Bag of Words, TF-IDF, Character N-grams). No API key needed — runs entirely in-process. NOT real embeddings: for"},{"name":"llm_format_check","description":"Validate that an LLM output matches an expected format: JSON, Markdown, code block, bullet list, numbered list, table, YAML, XML, or custom regex. Essential for"},{"name":"hallucination_check","description":"Lexical hallucination check: verifies an LLM answer's words, numbers and polarity against the provided source/context. Fast, deterministic, no API key needed. E"},{"name":"prompt_injection_scan","description":"Scan user input or prompts for common prompt injection patterns. Detects system prompt overrides, jailbreak attempts, role manipulation, encoding tricks, delimi"},{"name":"token_budget_calculator","description":"Plan token allocation across system prompt, user input, context/RAG chunks, and expected output. Warns if budget exceeds model context window. Supports 25+ mode"},{"name":"consistency_check","description":"Compare multiple LLM responses to the same prompt and detect inconsistencies using Jaccard word-overlap similarity and fact drift (number comparison). Fast, det"},{"name":"llm_json_schema_check","description":"Validate that an LLM JSON output matches a JSON Schema definition. Tests required fields, types, enums, nested objects, and arrays. Critical for function-callin"},{"name":"latency_benchmark","description":"Measure response time of one or more HTTP endpoints (GET/POST). Runs N iterations and returns min/max/avg/p95 latency. Useful for API and MCP server benchmarkin"},{"name":"response_quality_score","description":"Score an LLM response against the criteria you pass: coverage of expected_keywords and compliance with max_length. Returns a 0-100 score over the criteria actua"},{"name":"rag_relevance_rank","description":"Rank an array of text chunks by relevance to a query using TF-IDF scoring. 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