
toby1123yjh/arthas_mcp_server
Developer toolsArthas 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.
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. - 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 - 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 macOS: ~/.cursor/mcp.json Windows: C:\Users{username}.cursor\mcp.json There are multiple deployment methods: either attach mode or agent mode.
README
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
Install
This server does not publish a one-line install command.
Open the repository installation guide New to MCP? Start with the mcpskill.org guide →Configuration
{
"mcpServers": {
"arthas": {
"command": "uv",
"args": ["--directory", "F:\\path\\to\\arthas_mcp_server", "run", "python", "main.py"],
"env": { "ARTHAS_URL": "http://localhost:8563" }
}
}
}