About Arthas MCP Server
Arthas MCP Server is an MCP server published by toby1123yjh in the AI category: 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.
The repository has 56 stars and 9 forks, with the last commit 11 months ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues before depending on it in production.
Install
uvx arthas-mcp-serverpip install arthas-mcp-serverThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
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Static checks derived from the repository 4 hours ago. Heuristics on the source, not an audit.
- ✓README presentThe repository ships documentation.
- !License declaredNo license file. Legally you have no right to use it in a product.
- !Tests presentNo tests found. Behaviour changes may go unnoticed between versions.
- ✓Dependencies pinnedA lockfile pins dependency versions.
- !No dynamic code executionSource scan found eval or shell execution. Review how tool inputs reach it before granting credentials.
- !Scoped permissionsDoesn't declare a permission scope. Assume it can do anything its process can.
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
README fetched from the repository 4 hours ago. The original is authoritative.