text2sql-mcp
# text2sql-mcp
<!-- mcp-name: io.github.cpenniman12/text2sql-mcp -->
MCP server for [text2sql-framework](https://github.com/cpenniman12/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.
## Install
Out of the box, `text2sql-mcp` supports **SQLite + Anthropic**:
```bash
pip install text2sql-mcp
# or
uvx text2sql-mcp
```
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]'` |
## Configure
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 |
### Claude Desktop / Cursor / generic MCP
```json
{
"mcpServers": {
"text2sql": {
"command": "uvx",
"args": ["text2sql-mcp"],
"env": {
"TEXT2SQL_DATABASE_URL": "sqlite:///mydb.db",
"ANTHROPIC_API_KEY": "sk-ant-..."
}
}
}
}
```
### Goose CLI
```bash
goose configure
# Add Extension → Command-line Extension
# Name: text2sql
# Command: uvx text2sql-mcp
# Env: TEXT2SQL_DATABASE_URL, ANTHROPIC_API_KEY
```
## Tools
- **`query(question, max_rows=100)`** — ask the database a natural-language question. Returns `{sql, data, error, row_count, tool_calls_made}`.
## How it works
Under the hood this is a thin wrapper around [text2sql-framework](https://github.com/cpenniman12/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.
## License
MIT
TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion between overlapping functions. The tool's purpose is clearly defined for natural-language database queries, making selection trivial.
The single tool named 'query' is simple, intuitive, and uses a clear verb form. While there is no pattern to compare, the naming is unambiguous and follows common conventions for a query action.
The server has just one tool, which feels thin for a database interaction service. However, the tool is comprehensive and handles schema exploration, SQL generation, execution, and self-correction internally, so it may be sufficient for its narrow scope.
The tool covers the full natural-language query lifecycle: it interprets the question, writes SQL, executes it, and returns results with error handling. As a read-only query tool, it fully addresses the stated purpose without obvious gaps.