iflow-mcp_toby1123yjh-arthas-mcp-server 0.1.0

Java Performance Analysis & Diagnostics - LLM-powered MCP Server for real-time monitoring, memory analysis, thread profiling, and system optimization

Arthas MCP Server

Java diagnostics MCP server

Overview

Arthas MCP Server is an MCP-based diagnostic toolkit for Java applications, designed for LLM integration. It integrates with Alibaba Arthas so AI assistants can analyze and diagnose Java apps.

Features

• Intelligent diagnostics via LLM-friendly tools
• Real-time monitoring: JVM, threads, memory
• Performance analysis: CPU usage, call tracing, bottlenecks
• Runtime operations: dynamic class/method tools
• exmaple

Quick Start

Install

uv sync

Run

python main.py

MCP Tools

• connect_arthas: connect to Arthas WebConsole
• get_connection_status: get current status
• disconnect_arthas: disconnect
• get_jvm_info: JVM info
• get_thread_info: thread status and performance
• get_memory_info: memory usage and GC
• execute_arthas_command: run custom Arthas command
• analyze_performance: performance analysis
• trace_method_calls: method call tracing

Config

Add to Cursor / Claude Code

macOS: ~/.cursor/mcp.json Windows: C:\Users\{username}\.cursor\mcp.json

{
  "mcpServers": {
    "arthas": {
      "command": "uv",
      "args": ["--directory", "F:\\path\\to\\arthas_mcp_server", "run", "python", "main.py"],
      "env": { "ARTHAS_URL": "http://localhost:8563" }
    }
  }
}

Start Arthas

There are multiple deployment methods: either attach mode or agent mode. Both approaches ultimately result in listening for HTTP requests (Arthas commands) on port 8563.

Project Structure

arthas_mcp_server/
├── src/
│   ├── __init__.py
│   ├── models.py
│   ├── server.py
│   └── client.py
├── main.py
├── pyproject.toml
└── README.md

Development

uv sync --extra dev