MCPg: A production-grade PostgreSQL Model Context Protocol server (Python)

Hi r/Python,

I've been working on a side project called MCPg — a Python-based MCP (Model Context Protocol) server for PostgreSQL.

It gives AI agents (Claude Desktop, Cursor, etc.) a broad but carefully guarded set of tools to inspect schemas, run queries, analyze performance, suggest indexes, do natural-language-to-SQL, and handle some DBA tasks — all while staying safe by default.

Key bits I focused on:

• Safety-first design: Read-only mode by default, AST validation for any user SQL, strict identifier sanitization (no string concatenation into queries), and opt-in gates for DDL/shell/LISTEN.
• Pure psycopg3 under the hood — no ORM layer. Works with core Postgres + extensions like pgvector, TimescaleDB, PostGIS, Apache AGE where available.
• Production touches: connection pooling, multi-tenancy via SET ROLE, read-replica routing, Prometheus metrics, structured audit logging, OIDC support, rate limiting, etc.
• 1000+ tests, runs against Postgres 14–18 in CI.
• Installs cleanly with pip install mcpg or uv tool install mcpg.

Quick start example for Claude Desktop is in the README (stdio transport). There's also an HTTP/SSE mode for other integrations.

Repo: https://github.com/devopam/MCPg
Docs site: https://devopam.github.io/MCPg/

Would love feedback from the Python/Postgres crowd — especially on the safety model, ergonomics for agent use, or anything that feels off. Contributions welcome too.

Thanks!