Model Context Protocol server that exposes sql-sop's SQL linter to LLMs (Claude, Cursor, ChatGPT, Continue).
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# sql-sop-mcp [](https://pypi.org/project/sql-sop-mcp/) [](https://pypi.org/project/sql-sop-mcp/) [](LICENSE) Model Context Protocol server that wires [sql-sop](https://pypi.org/project/sql-sop/) into any MCP-aware LLM client. Lets Claude Desktop, Cursor, ChatGPT desktop, Continue, and similar tools call sql-sop's linter as a callable tool from inside a chat. The point: when an LLM generates SQL for you, it can lint that SQL itself before suggesting it. Or you can say "lint this query", paste the SQL, and the model uses the tool rather than guessing. ## What it exposes Two tools, both stdio-transport: | Tool | What it does | |---|---| | `lint_sql(sql, severity?, disable?)` | Run sql-sop against a SQL string. Returns `{passed, summary, findings[]}`. Each finding has `rule_id`, `severity`, `line`, `message`, `suggestion`. | | `list_rules()` | Return the full rule catalogue (43 rules in sql-sop v0.7.0; 48 with `--contract` enabled). | Backed by [sql-sop](https://github.com/Pawansingh3889/sql-guard), a fast rule-based SQL linter with 38 SQL rules (including 5 T-SQL specific ones) and 5 Python source rules for SQL injection on `cursor.execute()` / `sqlalchemy.text()`. As of v0.7.0 it also offers an opt-in Contracts pack (5 schema-aware rules) for projects that maintain a YAML data contract. There's a [browser playground](https://pawansingh3889.github.io/sql-guard/) if you want to feel out the rules before wiring this up. ## Install ```bash pip install sql-sop-mcp ``` Or with `pipx` if you want the CLI on PATH without polluting your project's venv: ```bash pipx install sql-sop-mcp ``` ## Wire it into your LLM client ### Claude Desktop Edit `~/Library/Application Support/Claude/claude_desktop_config.json` (macOS) or `%APPDATA%\Claude\claude_desktop_config.json` (Windows): ```json { "mcpServ…
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