AI SQL Agent MCP Server

A production-hardened Model Context Protocol (MCP) server that lets any MCP-compatible AI client (Claude, LangGraph agents, etc.) ask natural-language questions about a SQL database — safely.

Why this exists

Most public "LLM writes SQL, we run it" examples stop at a single unguarded step: generate SQL, execute it, return rows. That's fine for a demo and dangerous in production — a single hallucinated DROP TABLE or an unbounded query is a real incident, not a hypothetical.

This project treats the SQL-generation step as untrusted input and puts a real safety pipeline around it.

Related MCP server: mcp-db-server

Architecture

MCP Client (Claude / LangGraph / MCP Inspector)
        │
        ▼
 FastMCP Server (this repo)
        │
   ┌────┼─────────────────────────────┐
   ▼    ▼                             ▼
Schema  LLM SQL Generation      Guardrail Layer
Reader  (schema-grounded         (read-only enforcement,
(real   prompt, no guessing)     forbidden-keyword scan,
tables)                          statement-stacking block,
   │                             row-limit injection)
   └──────────────┬──────────────────┘
                  ▼
        Read-only SQLite connection
                  │
                  ▼
      Structured JSON result + trace log

Features

• Schema-grounded generation — the LLM only ever sees real table/column names pulled live from the database, never guesses.

• Guardrail layer — blocks any non-SELECT statement, blocks statement-stacking (; DROP TABLE), strips comments before keyword scanning, and caps result size with an injected LIMIT.

• Defense in depth — the guardrail check runs and the DB connection itself is opened read-only.

• Optional bearer-token auth on the MCP tool.

• Structured, correlated logging — every request gets a request ID, logged SQL, latency, and outcome (success / rejected / error).

• 41 automated tests covering guardrails, schema introspection, generation, and the full integration pipeline — including adversarial cases (SQL hidden in comments, oversized LIMITs, disguised keywords).

• Health check endpoint (/health) for uptime monitoring.

Why this is different from a typical FastMCP demo

FastMCP (Apache-2.0, PrefectHQ) is used here as the underlying server framework — it handles MCP protocol plumbing so this project can focus entirely on the parts that matter for a real data-access agent: safety, grounding, and observability. Everything in guardrails.py, schema_reader.py, sql_generator.py, and observability.py is original to this project.

Tech stack

Python 3.11+ · FastMCP (Apache-2.0) · Anthropic API · SQLite · pytest · Docker · GitHub Actions

Installation

git clone https://github.com/<your-username>/ai-sql-agent-mcp.git
cd ai-sql-agent-mcp
pip install -e ".[dev]"

Environment variables

Usage

python -m ai_sql_agent_mcp.server

Connect with the MCP Inspector to try it interactively:

npx @modelcontextprotocol/inspector python -m ai_sql_agent_mcp.server

Testing

pytest -v

API

Exposes two MCP tools:

• ask_database(question: str, api_token: str | None) -> dict — the main tool.

• describe_schema() -> dict — returns the live database schema.

Deployment

See docs/DEPLOYMENT.md for the free-tier cloud deployment guide (Docker + Fly.io/Render) and live demo instructions.

Security

• Read-only by construction at two independent layers (guardrail + DB connection mode).

• No write, DDL, or multi-statement SQL can ever reach the database.

• API token auth available; recommend enabling it for any public deployment.

• No secrets are logged; only the generated SQL text and metadata are logged.

Limitations

• Currently supports SQLite; Postgres/MySQL support would extend SchemaReader.

• Guardrails are structural (keyword/statement based), not a full SQL parser — sufficient for this threat model but not a substitute for DB-level permissions in a high-stakes production environment.

Roadmap

• Postgres/MySQL schema reader

• Query result caching

• Evaluation harness (NL→SQL accuracy benchmark)

• Rate limiting

License

MIT — see LICENSE. Built on FastMCP (Apache-2.0, PrefectHQ).

Author

Built by [your name] as part of an AI/ML engineering portfolio.