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  1. Home
  2. 🧠 Knowledge & Memory
  3. Heap Seance
Heap Seance logo
Health: ActiveRecent health check succeeded.Last checked 9/11/2026, 2:31:14 PM

Heap Seance

User RatingsBe the first to rate and review this MCP server!
View Repository4 GitHub StarsTotal stargazers on GitHub for the source repository (4 stars).Visit Website

Java memory leak diagnostics with a two-stage workflow using JVM tools and profilers, providing structured confidence-based verdicts.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

One-click editor setup isn’t available for this listing yet β€” we don’t have a confirmed install command, and we’d rather show nothing than point your editor at the wrong package or host. Follow the project’s own setup instructions, linked above.

Manual Client & Custom JSON ConfigExpand JSON β–Ύ
No confirmed setup config for this listing yet. We only publish a config block when the install details come from the project itself β€” its README, its docs, or a verified owner. We haven’t found those for SegfaultSorcerer/heap-seance, and we’d rather show nothing than a guess you’d paste into your client. Follow the project’s own setup instructions for the current steps.
Install Directory Badge Claim listing Alternatives🧠 More in Knowledge & Memory

Overview

Heap Seance is an MCP server and CLI toolkit for diagnosing Java memory leaks by orchestrating JVM tools like jcmd, jmap, jstat, JFR, Eclipse MAT, and async-profiler. It uses a two-stage escalation model starting with a conservative scan and escalating to deep forensic analysis only when warranted by evidence. The server returns structured verdicts with confidence levels and actionable next steps. It is designed to run inside Claude Code and supports integration with Java projects via MCP configuration.

Use cases

β€’Scan running Java processes for memory leaks conservatively
β€’Perform deep forensic analysis on suspected leaking JVMs
β€’Correlate heap dumps, JFR recordings, and allocation profiles
β€’Generate structured leak investigation reports with confidence levels
β€’Integrate memory leak diagnostics into Claude Code workflows

Key features

β€’Two-stage leak investigation: conservative scan and deep forensics
β€’Unified schema for tool outputs with confidence and next steps
β€’Supports JVM tools: jcmd, jmap, jstat, JFR, Eclipse MAT, async-profiler
β€’Slash commands for leak scanning and deep analysis
β€’Automated escalation based on evidence of leak signals
β€’Saves artifacts like heap dumps, JFR recordings, and MAT reports

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by Heap Seance.

Extracted Tool Capabilities
Two-stage leak investigation: conservative scan and deep forensics
Unified schema for tool outputs with confidence and next steps
Supports JVM tools: jcmd, jmap, jstat, JFR, Eclipse MAT, async-profiler
Slash commands for leak scanning and deep analysis
Automated escalation based on evidence of leak signals
Saves artifacts like heap dumps, JFR recordings, and MAT reports

Documentation Overview

Heap Seance

Heap Seance

Summoning retained objects from the heap β€” 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 β€” designed to run inside Claude Code.

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


How It Works β€’ Quick Start β€’ MCP Tools β€’ Workflow β€’ Prerequisites β€’ Contributing


How It Works

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

Code
 /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

bash
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:

config.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 β€” 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:

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

4. Run

bash
/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:

config.json
{
  "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> β€” takes the first histogram snapshot.
  3. Exercise the suspect behavior β€” 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 β€” 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 β€” 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 evidenceβ€”
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 β€” 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) β€” required for deep mode
  • async-profiler β€” optional tie-breaker

Optional tools:

  • jfr CLI β€” 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:

bash
./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 β€” $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 β€” 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 β€” 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)

bash
uv run heap-seance --mode scan --match your-app
uv run heap-seance --mode deep --pid 12345 --output json
Installing uv
bash
# 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)
bash
python3 -m venv .venv
source .venv/bin/activate       # Windows: .\.venv\Scripts\Activate.ps1
pip install -e .

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

Tests

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

Example Java scenarios for validation live in examples/java-scenarios/ β€” 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.

Read the full README β†’View source on GitHub β†’

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks β€” not a rating.

GitHub stars
4
Stargazers on the source repository.
Last commit
6mo ago
Most recent push to the default branch.
Directory activity
1 views
Config copies, upvotes, and views on AllMCPs.

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Frequently Asked Questions about Heap Seance

Requires JDK 17+ for the diagnostic tooling; the target Java app can run any Java version.

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Technical Specs & Signals

Category🧠Knowledge & Memory
More technical detailsExpand β–Ύ
Last updatedAug 7, 2026
5/6 checks healthy over the last 33d
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GitHub stars4
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Last commit6mo ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Mar 15, 2026
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Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
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Adoption & activity1/15
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