Java Profiler

npm version

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. Pass pid, duration (seconds). Optional: preset (default effective: profile), settingsFile (path to .jfc, mutually exclusive with preset), memorysize, stackdepth (default 128).
profile_jfr_network	Socket I/O summary from .jfr (jdk.SocketRead, jdk.SocketWrite). Optional filepath (default new_profile), topN.
profile_jfr_file_io	File read/write summary (jdk.FileRead, jdk.FileWrite). Optional filepath, topN.
profile_jfr_locks	Monitor contention (JavaMonitorBlocked) and j.u.c parking (ThreadPark). Optional filepath, topN. Live waits: analyze_threads structured=true.
profile_jfr_native	Native-method CPU hotspots (jdk.NativeMethodSample). Optional filepath, topN.
native_memory_summary	jcmd VM.native_memory summary — requires JVM with -XX:NativeMemoryTracking=summary or detail. Pass pid.
gc_class_stats	jcmd GC.class_stats when available (often JDK 21+). Pass pid.
gc_finalizer_info	jcmd GC.finalizer_info. Pass pid.
compiler_codecache	jcmd Compiler.codecache. Pass pid.
compiler_queue	jcmd Compiler.queue. Pass pid.
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), structured (JSON lock-wait chains). Live snapshot; historical locks: profile_jfr_locks.
heap_histogram	Class histogram (GC.class_histogram). Pass pid, optional topN (20), all (triggers full GC — may pause app). Static snapshot; use heap_live_histogram_diff for growth.
heap_live_histogram_diff	Two histograms spaced by intervalSeconds (default 5). Top classes by instance/byte growth. First step in memory-leak workflow. Pass pid, optional topN, all, minInstanceDelta.
heap_dump	Create .hprof for MAT/VisualVM. After heap_live_histogram_diff, use MAT Path to GC Roots. 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 by bytes/count, allocation stacks, OldObjectSample by class. Optional: filepath, topN, sortBy (bytes/count). Pair with gc_efficiency, heap_live_histogram_diff.
gc_efficiency	GC efficiency from .jfr: pause vs freed bytes per collector. Optional: filepath, topN. After stop_profiling.
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 → 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