Name: Algenta MCP Server
Author: thyn-ai

Tools observed via MCP handshake

onboard_dataset — Register a dataset for semantic querying. Pass column names, inline records, or raw CSV. The engine profiles roles automatically and starts background training.
list_datasets — List registered datasets and their current model tier. Use search plus compact mode for low-token discovery, then poll status or use the primary data tools once
get_dataset_status — Get the live training status and model tier of one dataset: whether semantic training is still running or the dataset is ready, and which model serves queries —
retrain_dataset — Re-trigger background semantic training for one dataset and return immediately with status and a confirmation message — the build runs asynchronously, so poll g
connect_data — High-level data onboarding flow. Use this instead of advanced connector/source tools for normal users. Connect data once, pick the table/file/endpoint, and get
list_data — List visible datasets for the current user. Use search plus compact mode first for low-token dataset discovery, then get_data_schema on the chosen dataset_id. R
get_data_summary — Get the low-token dataset selection summary for a saved dataset_id. Use this after list_data(search=..., compact=true) before paying for the full schema payload
get_data_schema — Get a saved dataset plus its schema and relationship metadata by dataset_id. Read-only and non-destructive; reads only the active API key's organization and is
refresh_data — Re-pull a saved dataset from its original database, API, or object-store origin using the stored connection and selection, and return the same envelope as conne
disconnect_data — Delete a saved dataset and disconnect it from future use. When no other dataset in the workspace still uses the backing saved connection, that connection is del
register_source — Advanced tool. Register a data source and get full schema profiling + join detection. Profiles every column (type, cardinality, fill rate, distribution). Detect
list_sources — Advanced tool. List all registered data sources for this org with their schema summaries. Use this to discover available tables before calling query_data or reg
get_source_schema — Advanced tool. Get the full schema for a specific registered source: column types, cardinality, fill rates, formula relationships, and detected join keys to oth
list_connectors — List the data connectors saved under the caller's organization — databases, APIs, file-backed, and repository sources — with id, name, connector_type, status (u
create_connector — Save one connector configuration (host, credentials, options) for later data onboarding, health checks, and schema browsing. config is encrypted at rest and the
get_connector — Fetch one saved connector by connector_id: name, connector_type, status, visibility, timestamps, and the config fingerprint — never the stored credentials. Use
update_connector — Partially update one saved connector: only the supplied fields change. Passing a new config replaces the encrypted credentials and resets the connector to untes
test_connector — Run a real connectivity test against one saved connector's stored config and persist the outcome as its live or error status with last_tested_at. This opens an
browse_connector — Discover what one saved connector exposes — files, tables, endpoints, or items — with discovery labels and metadata for choosing what to onboard. The connector
preview_test_connector — Run a real connectivity test against an inline connector definition without saving anything — the dry run for create_connector. This opens an actual connection
preview_browse_connector — Browse one inline connector definition without saving it to discover files, tables, endpoints, or items. This opens a real connection to the source and is rate-
delete_connector — Delete one saved connector by id. Use update_connector to change config without losing the saved definition. Deleting is idempotent: repeating the call on an al
get_repository_intelligence_capabilities — List globally supported Repository Intelligence languages and ranked support progress. Read-only and non-destructive. Check language support here before create_
create_repository_snapshot — Create or reuse an immutable, content-hashed snapshot of a saved repository connector (a connector of a repository type — find its id with list_connectors). Re-
get_repository_snapshot — Fetch one persisted immutable repository snapshot by repository_id and snapshot_id, including its resolved_revision, content_hash, file_count, language_counts,
triage_repository — Condense one repository snapshot into a bounded workspace evidence bundle for the planner: ranked suspect files and symbols with scored, budget-capped snippets.
create_repository_decision_plan — Create one stored, immutable repository DecisionPlan revision from a triage workspace evidence bundle and return its decision_plan_id plus the validated patch d
query_repository_graph — Walk the dependency graph of one persisted repository snapshot from optional file_path/symbol_name seeds and return impacted files and symbols with change-risk
simulate_repository — Score the patch risk of a stored repository DecisionPlan with the deterministic simulation engine and return the gated DecisionEnvelope whose apply gate apply_r
run_repository_pipeline — Run the whole repository-intelligence chain — snapshot, triage, plan, simulate — in one call and return the canonical repository envelope with every stage's res
simulate_repository_patch — Simulate the risk of an in-flight unified diff against one persisted snapshot and return the canonical repository envelope — without creating a stored decision
run_repository_fix — Run the repository pipeline and then apply its result in one call, returning the canonical repository envelope for both stages. pipeline takes run_repository_pi
apply_repository — Materialize a simulated repository decision in one of three modes. patch_only just returns the validated patch diff with applied=false and writes nothing. local
query_data — Execute a structured query against connected data sources. Convert the user's question to a structured intent and call this tool — do NOT try to write SQL or pa
query_batch — Execute several governed exact queries in one API call. Use this for multi-metric prompts after choosing a dataset with list_data and get_data_summary. Each ite
query_sql_report — Execute a constrained read-only SQL rowset query over authorized datasets. Use this only for wide reports that do not fit the governed exact-query surface. SQL
ingest_data — Auto-map tabular data to a simulation payload. Detects variable distributions, polarity (revenue=positive, cost=negative), units, and builds the objective funct
list_models — List the current Algenta model catalog, including deterministic utility models and any provider-backed routed entries with their routing, failover, timeout, and
resolve_artifact_bridge — Resolve a Hugging Face artifact path through the Algenta compatibility-ring artifact bridge. Defaults to cache-only lookup and never downloads unless local_file
tokenize — Tokenize UTF-8 text into individual tokens with a supported deterministic Algenta tokenizer model (default text.tokenizer; call list_models for every supported
count_tokens — Count how many tokens a supported deterministic Algenta tokenizer model produces for UTF-8 text (default text.tokenizer; call list_models for every supported mo
chat_completions — Run one ordered chat transcript through an Algenta model and return the assistant message plus token usage. The default text.tokenizer model is a deterministic
responses — Run the unified Algenta utility response surface: deterministic tokenization/embeddings, or (for provider-backed chat models) a chat response with optional func
embeddings — Generate one embedding vector per input string (a single string or a list of strings). The default text.hash_embedding_v1 model produces deterministic lexical h
embedding_similarity — Score the similarity between two caller-supplied embedding vectors with a supported deterministic metric (default embeddings.cosine_similarity). This tool does
rerank — Rank caller-supplied document embeddings against a query embedding with a supported deterministic similarity metric (default embeddings.cosine_similarity), most
list_runtime_libraries — List executable local-runtime Mojo libraries. Use this when you need the runtime-backed compute catalog rather than the governed data/query tools. This surface
execute_runtime_library — Execute one local-runtime Mojo library function by module and function name. Pass args as either a JSON object, array, scalar, or null. This surface is local/ru
list_capability_providers — List the unified capability providers available to the organization — data sources, MCP servers, skill packs, native tools, and runtime libraries — with their p
list_capability_bindings — List the capability bindings saved under the caller's organization, optionally narrowed by provider_id or scope (user, workspace, organization). A binding pairs
create_capability_binding — Save one capability binding for a provider/profile pair and return it with its binding_id. scope (default workspace) decides who can use it, execution_owner dec
test_capability_binding — Run a health test on one capability binding and return the outcome. Pass binding_id to test a saved binding, or a full inline definition (provider_id, profile_i
discover_capability_binding — Discover the capabilities one binding exposes and return them as catalog entries. Pass binding_id to discover a saved binding (this publishes or refreshes its c
list_capabilities — List the unified capability catalog visible to the caller — datasets, MCP tools, resources and prompts, skills, native tools, and runtime libraries — with each
get_capability — Fetch one unified capability catalog entry by capability_id: kind, provider, binding, execution owner, approval requirement, and tags. include_instruction=true
route_capabilities — Pick the best unified capability for a natural-language objective and return the route plan: the selected capability, binding, and kind, the authoritative execu
execute_capability — Execute one routed or known algenta_managed capability by capability id and return the execution receipt. client_managed routes must execute in the customer app
list_skills — List the skill capabilities in the unified capability plane — prompt skills enabled for the caller's organization with their names, bindings, and execution owne
enable_skill — Enable one prompt skill as a first-class capability binding and return its discovered catalog entry. The skill's instruction text becomes an instruction_only ca
disable_skill — Disable one skill by deleting its capability binding (find binding ids with list_skills or list_capability_bindings). The skill immediately stops appearing in t
plan_decision — Run a validated simulation-style request (the same payload contract as simulate) but return only the structured DecisionPlan summary — the compact plan object w
product_decision — Recommend an action for a business decision from plain inputs, and return the risk summary behind it. Each input becomes a simulation variable: fixed at value,
product_agent_run — Execute a natural-language task synchronously with the simple product agent and return a compact task result. The agent picks one tool from the task wording (op
product_optimize — Estimate the best value for each decision variable given a plain-English objective, and return the per-variable optima. Every variable is sampled uniformly over
product_retrieve — Rank caller-supplied documents against a search query and return the top matches with snippets. Scoring is deterministic lexical word-overlap between query and
product_forecast — Forecast a business metric horizon periods ahead from its historical series and return per-period point forecasts with confidence intervals. The trend comes fro
simulate — Run a Monte Carlo simulation and get a structured decision recommendation. Use for: quantifying risk in a decision, comparing expected outcomes, getting probabi
recommend — Compare multiple named actions/options and get a ranked recommendation. Use when you need to choose between two or more alternatives with uncertainty. Synchrono
score — Run one simulation request (the same payload shape as simulate) and return the decision envelope fields plus a composite score with its breakdown. The score ble
batch — Run multiple simulation requests in one call and return per-item success or failure details. Synchronous deterministic compute; nothing is persisted and no sepa
compare — Run 2-10 named scenarios side by side and return the winner plus each scenario's deltas versus the best one. The winner is the scenario with the highest expecte
submit_job — Submit a long-running async simulation job. Use for n_simulations > 500,000 or when you need a callback. Returns a job_id — poll with get_job_status. Submitting
list_jobs — List the organization's async simulation jobs, newest first, with pagination (defaults page 1, limit 25, max 200) and an optional status filter such as queued,
get_job_status — Fetch the latest async simulation job status by id. Read-only and non-destructive; not separately rate-limited. Use poll_job to block until a terminal state. Re
poll_job — Wait for an async simulation job to reach a terminal state. Returns the final result when the job completes, or the terminal status when it fails, is cancelled,
get_job_result — Fetch the completed result payload for an async simulation job by id. Read-only and non-destructive; not separately rate-limited. Use get_job_status to check pr
cancel_job — Cancel a queued or running async simulation job by id. Use this for queued or running jobs; list_jobs shows their states. Cancelling is idempotent: repeating th
test_webhook_delivery — Send one real test webhook payload (event webhook.test with a sample message) to a callback URL and return the delivery result. This makes an actual outbound HT
create_agent_run — Create a persisted agent run lifecycle resource for a natural-language task. With approval_mode=auto (default) the run picks a tool from the task wording, execu
list_agent_runs — List the organization's persisted agent runs, paginated (defaults page 1, limit 25), with lineage-aware filters: status, request_hash (find reruns of the same r
get_agent_run — Fetch one persisted agent run by run_id: status, task, selected_tool, steps, result, tools_used, and the policy/schema snapshot ids it ran under. Use list_agent
get_agent_run_events — Fetch the append-only event stream of one agent run — run_created, tool_selected, tool_executed, run_completed, and the pause/approve/cancel transitions — in or
get_agent_run_checkpoints — List the persisted checkpoints of one agent run — the deterministic snapshots written at creation and every lifecycle transition that make the run replayable. U
query_agent_run_checkpoints — Search persisted checkpoints across all of the organization's agent runs, paginated (defaults page 1, limit 25). Filter by run_id or checkpoint_id to pinpoint o
get_agent_run_mission_events — Fetch the canonical mission-event records of one agent run — the typed, indexed projection of its lifecycle used for audit and replay. Use get_agent_run_events
query_agent_run_mission_events — Search canonical mission-event records across all of the organization's agent runs, paginated (defaults page 1, limit 25) and newest first. Filter by run_id or
get_agent_run_telemetry — Fetch the runtime telemetry batches recorded for one agent run — the module-level timing and execution detail captured while it ran. Use query_agent_run_telemet
query_agent_run_telemetry — Search runtime telemetry batches across all of the organization's agent runs, paginated (defaults page 1, limit 25). Filter by run_id, telemetry_kind, or module
resume_agent_run — Resume a paused agent run by run_id. A run created with approval_mode=auto executes to completion synchronously and returns completed; a manual-mode run moves t
cancel_agent_run — Cancel an agent run by run_id, ending its lifecycle at cancelled. Only a paused or requires_approval run can be cancelled — anything else fails with agent_run_i
approve_agent_run — Approve an agent run that is waiting on manual approval (status requires_approval) and execute it synchronously to completion. Runs in any other state fail with
list_deployment_regions — List available deployment providers and regions for the current organization. Read-only and non-destructive; not separately rate-limited. Call this before creat
get_deployment — Fetch the current deployment for the active organization, if one exists. Read-only and non-destructive; not separately rate-limited. Poll this after create_depl
create_deployment — Request a new isolated engine deployment for the active organization on the chosen provider and region. Returns immediately with status requested — provisioning
get_deployment_cost — Get the current-month cost details of one deployment by id: provider, region, cost_usd_month, billable_cost_usd_month after markup, the applied billing_markup_p
delete_deployment — Request deprovisioning for one deployment by id. Deprovision with this before create_deployment when a deployment already exists. Deleting is idempotent: repeat
list_team_members — List the active users of the caller's organization with user_id, name, email, role, and status. Called with no arguments it returns the full member array; passi
invite_team_member — Invite someone to the caller's organization by email and return the pending invite. This creates a pending invitation, emails an accept link, and reserves a sea
update_team_member_role — Change one organization member's role by user_id (find ids with list_team_members). Requires an admin API key. Guardrails: you cannot change your own role (self
remove_team_member — Remove one member from the caller's organization by user_id (find ids with list_team_members). Requires an admin API key. The member is suspended immediately —

when	check	result	http	latency	detail
3 d ago	mcp initialize	failed	525	2189 ms	http 525
4 d ago	mcp initialize	failed	525	203 ms	http 525
5 d ago	mcp initialize	failed	525	217 ms	http 525
6 d ago	mcp initialize	failed	525	185 ms	http 525
6 d ago	mcp initialize	ok	200	246 ms	server algenta-mcp 1.0.33 · protocol 2025-06-18 · 140 tools
6 d ago	mcp initialize	ok	200	136 ms	server algenta-mcp 1.0.33 · protocol 2025-06-18 · 140 tools
7 d ago	mcp initialize	ok	200	226 ms	server algenta-mcp 1.0.33 · protocol 2025-06-18 · 140 tools
7 d ago	mcp initialize	ok	200	140 ms	server algenta-mcp 1.0.33 · protocol 2025-06-18 · 140 tools
7 d ago	mcp initialize	ok	200	139 ms	server algenta-mcp 1.0.33 · protocol 2025-06-18 · 140 tools
8 d ago	mcp initialize	ok	200	241 ms	server algenta-mcp 1.0.33 · protocol 2025-06-18 · 140 tools

Algenta MCP Server

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