isdaniel/pgtuner_mcp

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本页由 AI 翻译，技术细节请以原文为准。查看原文

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提供由 AI 驱动的 PostgreSQL 性能调优功能。

概览

一个模型上下文协议（MCP）服务器，提供基于 AI 驱动的 PostgreSQL 性能调优功能。该服务器帮助识别慢查询，推荐最优索引，分析执行计划，并利用 HypoPG 进行假设性的索引测试。 - 从pg_stat_statements中检索慢查询并附有详细统计数据 - 用EXPLAIN和EXPLAIN ANALYZE分析查询执行计划 - 通过自动计划分析识别性能瓶颈 - 监控活跃查询并检测长期交易 - 基于查询工作负载分析的AI驱动索引推荐 - 假设性索引测试带扩展（无磁盘使用） - 查找未使用的和重复索引进行清理 - 创建前估计索引大小 - 在实施前用拟议索引测试查询计划 - 全面的健康评分，包含多重检查 - 连接利用率监控 - 缓存命中率分析（缓冲区和索引） - 锁争用检测 - 真空健康状况和事务ID......

概述（AI 翻译）

一个模型上下文协议（MCP）服务器，提供基于 AI 驱动的 PostgreSQL 性能调优功能。该服务器帮助识别慢查询，推荐最优索引，分析执行计划，并利用 HypoPG 进行假设性的索引测试。

特色

查询分析

从pg_stat_statements获取带有详细统计的慢查询
分析带有EXPLAIN和EXPLAIN ANALYZE的查询执行计划。
通过自动化计划分析识别性能瓶颈
监控活跃查询并检测长期运行的事务

指标调音

基于查询工作负载分析的AI驱动索引推荐
假设性索引测试，使用 HypoPG 扩展（无磁盘使用）
查找未使用的和重复索引进行清理
在创建前估算指数大小
在实施前用拟议索引测试查询计划

数据库健康

多重检查的综合健康评分
连接利用率监控
缓存命中率分析（缓冲区和索引）
锁争用检测
真空健康状态和事务ID环绕监控
复制滞后监测
背景写作与检查点分析

真空监测

本页由 AI 翻译，技术细节请以原文为准。

README

以下为原文 README 节选，完整内容请查看原文。

PostgreSQL Performance Tuning MCP

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.

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)

在 GitHub 查看完整 README

安装

docker run -p 8080:8080 \

新手？先看 mcpskill.org 教程 →

配置

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