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Heap Seance

Heap Seance

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github.com/SegfaultSorcererDeveloper Tools4Updated 33w ago

About Heap Seance

Heap Seance is an MCP server and CLI toolkit for developers investigating Java memory leaks, designed to run in Claude Code. It escalates from conservative scans to deeper forensics and assigns confidence based on multiple evidence signals.

Key features

  1. 01Escalate from conservative scans to deep forensics
  2. 02Collect class histograms and GC data
  3. 03Create JFR recordings and heap dumps
  4. 04Analyze leak suspects with Eclipse MAT
  5. 05Return structured evidence and confidence

Use cases

  1. 01Check object growth after repeated service actions
  2. 02Assess growth alongside GC pressure
  3. 03Investigate retained objects in heap dumps
  4. 04Rescan after fixes to verify changes

MCP support

The MCP server provides 8 tools to discover JVMs, collect runtime data, create recordings and heap dumps, and analyze them with MAT or async-profiler. It also includes the /leak-scan and /leak-deep commands for Claude Code.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Heap Seance

Heap Seance

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

MIT Apache 2.0 Python 3.10+ JDK 17+ MCP Server Claude Code Plugin

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

ToolWhat it does
java_list_processes()Discover running JVMs via jcmd -l
java_class_histogram(pid)Snapshot live object counts per class
java_gc_snapshot(pid)Sample jstat -gcutil over time
java_jfr_start(pid)Capture a JFR recording
java_jfr_summary(jfr_file)Summarize JFR event types and counts
java_heap_dump(pid)Full heap dump (.hprof)
java_mat_suspects(heap_dump)Run MAT leak suspects analysis
java_async_alloc_profile(pid)Allocation flame graph via async-profiler

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
ConfidenceWhat it meansSignals required
noneNo leak evidence—
lowWeak growth, no GC pressurehistogram only
mediumGrowth + GC is losinghistogram + GC pressure
highProbable leak, corroboratedhistogram + GC + MAT/JFR

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):

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:

VariableRequiredDescription
JAVA_HOMErecommendedJDK 17+ installation path — $JAVA_HOME/bin is searched first for jcmd, jmap, jstat, jfr. Also used to launch MAT with the correct Java version.
MAT_BINfor deep modePath to ParseHeapDump.sh (macOS/Linux) or .bat (Windows)
ASYNC_PROFILER_BINoptionalPath to async-profiler binary — tie-breaker evidence, deep mode works without it
HEAP_SEANCE_ARTIFACT_DIRoptionalWhere .jfr, .hprof, and reports are saved (default: system temp dir)
MCP_TRANSPORToptionalTransport protocol: stdio (default), sse, or streamable-http
MCP_HOSToptionalBind address for SSE/HTTP transport (default: 0.0.0.0)
MCP_PORToptionalPort for SSE/HTTP transport (default: 8000)

CLI flags --sse and --streamable-http can be used instead of MCP_TRANSPORT.

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
Installing uv
# 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"
Manual setup (without uv)
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

at your option.

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