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Performance analysis > Heap Seance

Memory leak diagnostics that orchestrates jcmd, jmap, jstat, JFR, Eclipse MAT, and async-profiler into a structured investigation workflow with confidence-based verdicts.

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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 WorksQuick StartMCP ToolsWorkflowPrerequisitesContributing


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

Tool What 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

Confidence What it means Signals required
none No leak evidence
low Weak growth, no GC pressure histogram only
medium Growth + GC is losing histogram + GC pressure
high Probable leak, corroborated histogram + 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:

Variable Required Description
JAVA_HOME recommended JDK 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_BIN for deep mode Path to ParseHeapDump.sh (macOS/Linux) or .bat (Windows)
ASYNC_PROFILER_BIN optional Path to async-profiler binary — tie-breaker evidence, deep mode works without it
HEAP_SEANCE_ARTIFACT_DIR optional Where .jfr, .hprof, and reports are saved (default: system temp dir)
MCP_TRANSPORT optional Transport protocol: stdio (default), sse, or streamable-http
MCP_HOST optional Bind address for SSE/HTTP transport (default: 0.0.0.0)
MCP_PORT optional Port 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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