iflow-mcp_toby1123yjh-arthas-mcp-server

# Arthas MCP Server [](README.zh-CN.md) 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 ```bash uv sync ``` ### Run ```bash 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` ```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 ```bash uv sync --extra dev ```