isdaniel/pgtuner_mcp

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provides AI-powered PostgreSQL performance tuning capabilities.

概要

A Model Context Protocol (MCP) server that provides AI-powered PostgreSQL performance tuning capabilities. This server helps identify slow queries, recommend optimal indexes, analyze execution plans, and leverage HypoPG for hypothetical index testing. - Retrieve slow queries from pg_stat_statements with detailed statistics - Analyze query execution plans with EXPLAIN and EXPLAIN ANALYZE - Identify performance bottlenecks with automated plan analysis - Monitor active queries and detect long-running transactions - AI-powered index recommendations based on query workload analysis - Hypothetical index testing with extension (no disk usage) - Find unused and duplicate indexes for cleanup - Estimate index sizes before creation - Test query plans with proposed indexes before implementing - Comprehensive health scoring with multiple checks - Connection utilization monitoring - Cache hit ratio analysis (buffer and index) - Lock contention detection - Vacuum health and transaction ID...

README

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PostgreSQL Performance Tuning MCP

Features

Query Analysis

Retrieve slow queries from pg_stat_statements with detailed statistics
Analyze query execution plans with EXPLAIN and EXPLAIN ANALYZE
Identify performance bottlenecks with automated plan analysis
Monitor active queries and detect long-running transactions

Index Tuning

AI-powered index recommendations based on query workload analysis
Hypothetical index testing with HypoPG extension (no disk usage)
Find unused and duplicate indexes for cleanup
Estimate index sizes before creation
Test query plans with proposed indexes before implementing

Database Health

Comprehensive health scoring with multiple checks
Connection utilization monitoring
Cache hit ratio analysis (buffer and index)
Lock contention detection
Vacuum health and transaction ID wraparound monitoring
Replication lag monitoring
Background writer and checkpoint analysis

Vacuum Monitoring

Track long-running VACUUM and VACUUM FULL operations in real-time
Monitor autovacuum progress and performance
Identify tables that need vacuuming
View recent vacuum activity history
Analyze autovacuum configuration effectiveness

I/O Performance Analysis

Analyze disk read/write patterns across tables and indexes
Identify I/O bottlenecks and hot tables
Monitor buffer cache hit ratios
Track temporary file usage indicating work_mem issues
Analyze checkpoint and background writer I/O
PostgreSQL 16+ enhanced pg_stat_io metrics support

Configuration Analysis

Review PostgreSQL settings by category
Get recommendations for memory, checkpoint, WAL, autovacuum, and connection settings
Identify suboptimal configurations

MCP Prompts & Resources

Pre-defined prompt templates for common tuning workflows
Dynamic resources for table stats, index info, and health checks
Comprehensive documentation resources

Installation

Standard Installation (for MCP clients like Claude Desktop)

GitHub で README 全文を見る

インストール

docker run -p 8080:8080 \

MCP 初心者？mcpskill.org のガイドから始めましょう →

設定

{
  "mcpServers": {
    "pgtuner_mcp": {
      "command": "python",
      "args": ["-m", "pgtuner_mcp"],
      "env": {
        "DATABASE_URI": "postgresql://user:password@localhost:5432/mydb"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}