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A Postgres MCP server with index tuning, explain plans, health checks, and safe sql execution.

Overview • Demo • Quick Start • Technical Notes • MCP API • Related Projects • FAQ

## Overview **Postgres MCP Pro** is an open source Model Context Protocol (MCP) server built to support you and your AI agents throughout the **entire development process**—from initial coding, through testing and deployment, and to production tuning and maintenance. Postgres MCP Pro does much more than wrap a database connection. Features include: - **🔍 Database Health** - analyze index health, connection utilization, buffer cache, vacuum health, sequence limits, replication lag, and more. - **⚡ Index Tuning** - explore thousands of possible indexes to find the best solution for your workload, using industrial-strength algorithms. - **📈 Query Plans** - validate and optimize performance by reviewing EXPLAIN plans and simulating the impact of hypothetical indexes. - **🧠 Schema Intelligence** - context-aware SQL generation based on detailed understanding of the database schema. - **🛡️ Safe SQL Execution** - configurable access control, including support for read-only mode and safe SQL parsing, making it usable for both development and production. Postgres MCP Pro supports both the [Standard Input/Output (stdio)](https://modelcontextprotocol.io/docs/concepts/transports#standard-input%2Foutput-stdio) and [Server-Sent Events (SSE)](https://modelcontextprotocol.io/docs/concepts/transports#server-sent-events-sse) transports, for flexibility in different environments. For additional background on why we built Postgres MCP Pro, see [our launch blog post](https://www.crystaldba.ai/blog/post/announcing-postgres-mcp-server-pro). ## Demo *From Unusable to Lightning Fast* - **Challenge:** We generated a movie app using an AI assistant, but the SQLAlchemy ORM code ran painfully slow. - **Solution:** Using Postgres MCP Pro with Cursor, we fixed the performance issues in minutes. What we did: - 🚀 Fixed performance - including ORM queries, indexing, and caching - 🛠️ Fixed a broken page - by prompting the agent to explore the data, fix queries, and add related content. - 🧠 Improved the top movies - by exploring the data and fixing the ORM query to surface more relevant results. See the video below or read the [play-by-play](examples/movie-app.md). https://github.com/user-attachments/assets/24e05745-65e9-4998-b877-a368f1eadc13 ## Quick Start ### Prerequisites Before getting started, ensure you have: 1. Access credentials for your database. 2. Docker *or* Python 3.12 or higher. #### Access Credentials You can confirm your access credentials are valid by using `psql` or a GUI tool such as [pgAdmin](https://www.pgadmin.org/). #### Docker or Python The choice to use Docker or Python is yours. We generally recommend Docker because Python users can encounter more environment-specific issues. However, it often makes sense to use whichever method you are most familiar with. ### Installation

Command Palette

Servers

Postgres MCP Server