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
- 01Escalate from conservative scans to deep forensics
- 02Collect class histograms and GC data
- 03Create JFR recordings and heap dumps
- 04Analyze leak suspects with Eclipse MAT
- 05Return structured evidence and confidence
Use cases
- 01Check object growth after repeated service actions
- 02Assess growth alongside GC pressure
- 03Investigate retained objects in heap dumps
- 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.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
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
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
| 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
- Start your app and let it initialize fully.
/leak-scan <name-or-pid>— takes the first histogram snapshot.- 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.
- Read the verdict. Focus on
Confidence,Key Evidence,Suspect Types. /leak-deep <name-or-pid>if the scan flags growth, or if you want full forensics regardless.- Fix and re-scan. Bounded caches, weak refs, listener cleanup — then
/leak-scanagain to confirm the signal drops. - 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 viaJAVA_HOMEin.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:
jfrCLI — used for JFR summary if available, falls back tojcmd JFR.viewotherwise. 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) injstatoutput 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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