The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the Text2sql listing page.
MCP server for text2sql-framework. Plugs into Claude Desktop, Cursor, Goose, or any other MCP-compatible assistant and lets it ask a SQL database questions in natural language.
The agent explores the schema, writes SQL, executes it against the real DB, and self-corrects on errors — no RAG layer, no schema descriptions, no pre-computed embeddings.
Out of the box, text2sql-mcp supports SQLite + Anthropic:
For other databases or LLM providers, install with the matching extra so the right driver gets installed:
| You want… | Install command |
|---|---|
| SQLite (default) | uvx text2sql-mcp |
| Postgres | uvx 'text2sql-mcp[postgres]' |
| MySQL | uvx 'text2sql-mcp[mysql]' |
| Snowflake | uvx 'text2sql-mcp[snowflake]' |
| BigQuery | uvx 'text2sql-mcp[bigquery]' |
| OpenAI models | add openai, e.g. uvx 'text2sql-mcp[postgres,openai]' |
Set environment variables in your MCP client config:
| Variable | Required | Description |
|---|---|---|
TEXT2SQL_DATABASE_URL | yes | SQLAlchemy URL, e.g. sqlite:///mydb.db, postgresql://user:pass@host/db |
ANTHROPIC_API_KEY or OPENAI_API_KEY | yes | LLM provider key |
TEXT2SQL_MODEL | no | LangChain model id (default: anthropic:claude-sonnet-4-6) |
TEXT2SQL_INSTRUCTIONS | no | Business rules / hints, e.g. "Revenue = net of refunds." |
TEXT2SQL_EXAMPLES | no | Path to a scenarios.md file for the agent's lookup_example tool |
query(question, max_rows=100) — ask the database a natural-language question. Returns {sql, data, error, row_count, tool_calls_made}.Under the hood this is a thin wrapper around text2sql-framework, which uses LangChain Deep Agents to do iterative tool-calling against a single execute_sql tool. See the framework README for benchmarks (19/20 on Spider zero-shot across 80 tables) and architecture details.
MIT