Database Query & Index Performance Optimizer MCP Server

A lightweight Model Context Protocol (MCP) server written in Python using FastMCP. It provides LLM clients (Cursor, Claude Desktop, AntiGravity) with tools to analyze SQL queries, suggest indexes, and flag unpaginated large-table query risks.

Features

• Analyze Query Plan: Connects to a PostgreSQL database, runs EXPLAIN (ANALYZE, BUFFERS, FORMAT JSON) inside a rolled-back transaction to safely detect performance bottlenecks like sequential scans.
• Suggest Indexes: Generates CREATE INDEX CONCURRENTLY DDL statements for a table given a list of commonly filtered columns.
• Check Pagination Safety: Audits query strings for OFFSET / LIMIT pagination that can degrade performance on large tables, suggesting keyset (cursor-based) alternatives.

Prerequisites

• Python 3.11+
• PostgreSQL (if using the analyze_query tool)

Installation

1. Clone this repository.
2. Install the required dependencies:

   pip install -r requirements.txt
   

Running the Server

To start the MCP server, run:

python server.py

Setup in Cursor

To use this MCP server in Cursor, you can add it to your Cursor MCP configuration.

1. Open Cursor Settings.
2. Go to Features > MCP Servers.
3. Add a new server with the following details:
   • Name: DB-Performance-MCP
   • Type: command
   • Command: python /path/to/DB-Performance-MCP/server.py (adjust the path appropriately)

Docker Support

You can also run the server via Docker:

docker build -t db-performance-mcp .
docker run db-performance-mcp