Heap Seance

Summoning retained objects from the heap — so you can interrogate what refuses to die.

An MCP server + CLI toolkit that channels the spirits of jcmd, jmap, jstat, jfr, Eclipse MAT, and async-profiler into a structured leak investigation workflow — designed to run inside Claude Code.

2 slash commands. 8 MCP tools. Conservative by default.

How It Works • Quick Start • MCP Tools • Workflow • Prerequisites • Contributing

How It Works

Heap Seance follows a two-stage escalation model. No deep forensics unless the evidence demands it.

 /leak-scan                          /leak-deep
     |                                   |
     v                                   v
  3x class histogram               (all of scan, plus)
  + GC pressure snapshot            JFR recording
     |                              heap dump
     v                              MAT leak suspects
  monotonic growth?                 async-profiler alloc profile
  old-gen pressure?                     |
     |                                  v
     +--- both true? -----> auto-escalate to deep
     |
     +--- otherwise ------> verdict + next steps

Confidence is earned, not assumed. high requires at least two independent strong signals. A single growing class is watch. Growth plus GC pressure is suspicious. Add a MAT dominator or JFR correlation and you get probable_memory_leak.

Quick Start

Requires uv, Python 3.10+, and a JDK 17+ for tooling (the target app can run any Java version).

1. Clone

git clone https://github.com/your-org/heap-seance.git

2. Add .mcp.json to your Java project

In the project you want to investigate, create a .mcp.json:

{
  "mcpServers": {
    "heap-seance": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/heap-seance", "python", "-m", "heap_seance_mcp.server"],
      "env": {
        "JAVA_HOME": "/path/to/jdk-17",
        "MAT_BIN": "/path/to/ParseHeapDump.sh",
        "ASYNC_PROFILER_BIN": "/path/to/asprof"
      }
    }
  }
}

--directory points to where you cloned Heap Seance. uv run handles the virtual environment and dependencies automatically. ASYNC_PROFILER_BIN is optional — if missing, deep mode continues with JFR + MAT.

3. Copy the Claude Code commands

Copy the .claude/commands/ folder into your Java project so the /leak-scan and /leak-deep slash commands are available:

cp -r /path/to/heap-seance/.claude/commands/ .claude/commands/

4. Run

/leak-scan my-service        # conservative scan
/leak-deep 12345             # full forensics by PID

Heap Seance resolves the target process, collects evidence, and returns a structured verdict.

MCP Tools

Every tool returns the same unified schema:

{
  "status": "ok | warn | error",
  "evidence": ["..."],
  "metrics": {},
  "confidence": "none | low | medium | high",
  "next_recommended_action": "...",
  "raw_artifact_path": "..."
}

Investigation Workflow

1. Start your app and let it initialize fully.
2. /leak-scan <name-or-pid> — takes the first histogram snapshot.
3. Exercise the suspect behavior — the scan prompts you between each of the 3 histogram samples to perform the action you suspect is leaking (open/close views, send requests, repeat workflows). This is critical — without load between samples, leaks stay invisible.
4. Read the verdict. Focus on Confidence, Key Evidence, Suspect Types.
5. /leak-deep <name-or-pid> if the scan flags growth, or if you want full forensics regardless.
6. Fix and re-scan. Bounded caches, weak refs, listener cleanup — then /leak-scan again to confirm the signal drops.
7. Keep artifacts. .jfr, .hprof, and MAT reports are saved for team review.

What you get back

/leak-scan returns: Verdict, Confidence, Key Evidence, Suspect Types, Artifacts, Next Steps.

/leak-deep goes further: Verdict, Confidence, Root Holder Hypothesis (who retains the growing objects and via which field/chain), Supporting Evidence, Artifacts, Remediation Hypotheses (concrete fix suggestions), Verification Plan.

Confidence ladder

Prerequisites

Tooling JDK (required):

• JDK 17+ for jcmd, jmap, jstat — set via JAVA_HOME in .mcp.json
• The target application can run any Java version (including Java 8)

Deep forensics (for /leak-deep):

• Eclipse MAT CLI (ParseHeapDump.sh / .bat) — required for deep mode
• async-profiler — optional tie-breaker

Optional tools:

• jfr CLI — used for JFR summary if available, falls back to jcmd JFR.view otherwise. JFR is skipped entirely for Java 8 targets (incompatible format).

Check your setup:

./scripts/check_prereqs.sh          # macOS / Linux
scripts\check_prereqs.bat           # Windows

Environment overrides

Set these in your .mcp.json env block (recommended) or as shell variables:

See .mcp.json.example for a full config template.

Compatibility notes

• Java 8 targets: histogram + GC + MAT work fully. JFR is skipped (v0.9 format incompatible with modern tools).
• Windows: MAT works via ParseHeapDump.bat. async-profiler is optional — if missing, deep mode continues with JFR + MAT. Locale-specific decimal separators (comma vs dot) in jstat output are handled automatically.
• MAT + JAVA_HOME: MAT is launched with the JDK from JAVA_HOME, so it works even if the system default Java is too old for MAT.

CLI Usage (without Claude Code)

uv run heap-seance --mode scan --match your-app
uv run heap-seance --mode deep --pid 12345 --output json

# macOS / Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows (PowerShell)
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

python3 -m venv .venv
source .venv/bin/activate       # Windows: .\.venv\Scripts\Activate.ps1
pip install -e .

heap-seance --mode scan --match your-app

Tests

python3 -m unittest discover -s tests -p "test_*.py"

Example Java scenarios for validation live in examples/java-scenarios/ — a real leak, a bounded cache (no leak), and a burst allocator (no leak).

Contributing

Contributions welcome! See CONTRIBUTING.md for guidelines on adding tools, signals, and skills.

License

This project is dual-licensed under either of

• Apache License, Version 2.0
• MIT License

at your option.