javaperf

MCP (Model Context Protocol) server for profiling Java applications via JDK utilities (jcmd, jfr, jps)

Enables AI assistants to diagnose performance, analyze threads, and inspect JFR recordings without manual CLI usage.

📦 Install: npm install -g javaperf or use via npx 🌐 npm: https://www.npmjs.com/package/javaperf

How to connect to Claude Desktop / IDE

Add the server to your MCP config. Example for claude_desktop_config.json:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json Linux: ~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "javaperf": {
      "command": "npx",
      "args": ["-y", "javaperf"]
    }
  }
}

For Cursor IDE: Settings → Features → Model Context Protocol → Edit Config, then add the same block inside mcpServers. See the Integration section for more options (local dev, custom JAVA_HOME, etc.).

Requirements

• Node.js v18+
• JDK 8u262+ or 11+ with JFR support

JDK tools (jps, jcmd, jfr) are auto-detected via JAVA_HOME or which java. If not found, set JAVA_HOME to your JDK root.

Quick Start

For Users (using npm package)

# No installation needed - use directly in Cursor/Claude Desktop
# Just configure it as described in Integration section below

For Developers

1. Clone the repository:

git clone https://github.com/theSharque/mcp-jperf.git
cd mcp-jperf

2. Install dependencies:

npm install

3. Build the project:

npm run build

Usage

Development Mode

npm run dev

Production Mode

npm start

MCP Inspector

Debug and test with MCP Inspector:

npx @modelcontextprotocol/inspector node dist/index.js

Integration

Cursor IDE

1. Open Cursor Settings → Features → Model Context Protocol
2. Click "Edit Config" button
3. Add one of the configurations below

Option 1: Via npm (Recommended)

Installs from npm registry automatically:

{
  "mcpServers": {
    "javaperf": {
      "command": "npx",
      "args": ["-y", "javaperf"]
    }
  }
}

Option 2: Via npm link (Development)

For local development with live changes:

{
  "mcpServers": {
    "javaperf": {
      "command": "javaperf"
    }
  }
}

Requires: cd /path/to/mcp-jperf && npm link -g

Option 3: Direct path

{
  "mcpServers": {
    "javaperf": {
      "command": "node",
      "args": ["dist/index.js"],
      "cwd": "${workspaceFolder}",
      "env": {
        "JAVA_HOME": "/path/to/your/jdk"
      }
    }
  }
}

If list_java_processes fails with "jps not found", the MCP server may not inherit your shell's JAVA_HOME. Add the env block above with your JDK root path (e.g. /usr/lib/jvm/java-17 or ~/.sdkman/candidates/java/current).

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "javaperf": {
      "command": "npx",
      "args": ["-y", "javaperf"]
    }
  }
}

Continue.dev

Edit .continue/config.json:

{
  "mcpServers": {
    "javaperf": {
      "command": "npx",
      "args": ["-y", "javaperf"]
    }
  }
}

Tools

Tool	Description
list_java_processes	List running Java processes (pid, mainClass, args). Use topN (default 10) to limit.
start_profiling	Start JFR recording with settings=profile. Pass pid, duration (seconds). Optional: memorysize (e.g. "20M"), stackdepth (default 128).
list_jfr_recordings	List active JFR recordings for a process. Use before stop_profiling to get recordingId.
stop_profiling	Stop recording and save to recordings/new_profile.jfr. Requires pid and recordingId.
check_deadlock	Check for Java-level deadlocks. Returns structured JSON with threads, locks, and cycle.
analyze_threads	Thread dump (jstack) with deadlock summary. Pass pid, optional topN (default 10).
heap_histogram	Class histogram (GC.class_histogram). Pass pid, optional topN (20), all (triggers full GC — may pause app).
heap_dump	Create .hprof heap dump for MAT/VisualVM. Pass pid. Saved to recordings/heap_dump.hprof.
heap_info	Brief heap summary. Pass pid.
vm_info	JVM info: uptime, version, flags. Pass pid.
trace_method	Build call tree for a method from .jfr. Pass className, methodName. Optional: filepath (default new_profile), topN.
parse_jfr_summary	Parse .jfr into summary: top methods, GC stats, anomalies. Optional: filepath (default new_profile), events, topN.
profile_memory	Memory profile: top allocators, GC, potential leaks. Optional: filepath (default new_profile), topN.
profile_time	CPU bottleneck profile (bottom-up). Optional: filepath (default new_profile), topN.
profile_frequency	Call frequency profile (leaf frames). Optional: filepath (default new_profile), topN.

Example Workflow

1. List processes → list_java_processes
2. Start recording → start_profiling with pid and duration (e.g. 60)
3. Wait for duration seconds (or let it run)
4. Check recordings (optional) → list_jfr_recordings to get recordingId
5. Stop and save → stop_profiling with pid and recordingId
6. Analyze → Use parse_jfr_summary, profile_memory, profile_time, profile_frequency, or trace_method (filepath defaults to new_profile)

Limitations

• Sampling: JFR samples ~10ms; fast methods may not appear in ExecutionSample
• Local only: Runs on the machine where MCP is started
• Permissions: Must run as same user as target JVM for jcmd access