javaperf

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

1. Install dependencies:

npm install

1. 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

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 → parse_jfr_summary, profile_memory, gc_efficiency, profile_time, profile_frequency, trace_method, profile_jfr_network, profile_jfr_file_io, profile_jfr_locks, or profile_jfr_native (events must exist in the recording — use start_profiling with a suitable preset or .jfc via settingsFile)

Example Workflow: Memory leak hypothesis

1. List processes → list_java_processes
2. Find growing classes → heap_live_histogram_diff with pid, intervalSeconds: 5
3. Record under load → start_profiling → wait → stop_profiling
4. Allocation profile → profile_memory on new_profile (check oldObjectSamplesByClass for suspect class)
5. GC pressure → gc_efficiency on the same .jfr
6. Confirm retention → heap_dump → Eclipse MAT → Path to GC Roots (exclude weak/soft references)
7. AI builds a coherent leak hypothesis from the combined results (no dedicated tool)

Remote JVM (stdio MCP)

javaperf uses stdio MCP and attaches to JVMs via local jps/jcmd. That only works on the OS account and host where the MCP process runs.

To diagnose a JVM on another machine:

• Run the MCP server (your IDE connector, Cursor, or Claude Desktop) on that machine, for example SSH remote workspace, Codespaces, CI runner checkout on the server, or a shell session on the same host as the process.
• Do not rely on piping jcmd over plain SSH from another host unless you deliberately run MCP there; attaching across hosts is outside this server’s scope.

Requirements (same user, local attach) listed under Limitations still apply.

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