Arthas MCP Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Arthas MCP Servercheck JVM memory usage"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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 syncRun
python main.pyMCP 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.mdDevelopment
uv sync --extra devThis server cannot be installed
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