io.github.sudo-ai-git/mcp-benchmark-hygiene
Detects pytest configuration leakage in a workspace, such as inherited addopts coverage or abort gates that can silently corrupt grading, and provides corrected pytest commands to prevent false failures.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@io.github.sudo-ai-git/mcp-benchmark-hygienecheck this repo for pytest addopts that would corrupt grading"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
mcp-benchmark-hygiene
mcp-name: io.github.sudo-ai-git/mcp-benchmark-hygiene
Deterministic detection of pytest config-leakage that silently corrupts agent-benchmark / function grading.
No LLM. No network. One question, answered reliably:
If I run
python -m pytest <tests>inside this workspace, will it inherit a host coverage/abort gate that mis-scores passing code as failed?
The bug this catches
Automated agent-evaluation harnesses often run python -m pytest <hidden_tests>
inside the target's workspace. If that workspace nests under a repo root carrying
pytest addopts — e.g.:
[tool.pytest.ini_options]
addopts = "--cov=harness --cov-report=term-missing:skip-covered --cov-fail-under=80"...pytest resolves that host pyproject.toml as its rootdir, inherits the
addopts, and fails on the host's own coverage gate (harness collected at
0% → below threshold → non-zero exit). The harness then records functionally
PASSING code as FAILED.
This is exactly the bug documented in
sudo-ai-git/vulcanbench-findings:
VulcanBench's declarative grader mis-scored every functional task as 0.0 for
this reason; with -o addopts= neutralizing the leak, the same workspaces passed
10/10.
Related MCP server: forge-repo-mcp
The fix it hands you
When a workspace is flagged CORRUPTED, the tool returns the corrected command:
python -m pytest -o addopts= <tests>-o addopts= strips inherited coverage/abort gates. (Or run the grader from
outside the repo root.)
Tools
tool | purpose |
| full analysis: ini chain, effective addopts, CLEAN/CORRUPTED/UNKNOWN verdict + corrected command |
| thin boolean: |
| one-line actionable summary string |
Deterministic core (no deps)
The analysis walks the workspace directory up to filesystem root, reading
pyproject.toml / pytest.ini / tox.ini / setup.cfg in pytest's
first-found order, and extracts addopts. Flags:
coverage gates —
--cov,--cov-fail-under,--cov-report,--cov-configabort/strict gates —
--maxfail,-x,--strict,--strict-markers,--pdb,--ff
Only gates that change exit codes / abort grading are flagged. A harmless
addopts is reported CLEAN with the exact string.
Install & run (MCP stdio)
One command (recommended) — installs from the repo, no PyPI token needed:
uv tool install git+https://github.com/sudo-ai-git/mcp-benchmark-hygiene
mcp-benchmark-hygiene # run stdio server
mcp-benchmark-hygiene --http --port 8137 # or Streamable HTTPOr with pipx: pipx install git+https://github.com/sudo-ai-git/mcp-benchmark-hygiene
Direct from source (fallback):
{ "mcpServers": {
"benchmark-hygiene": { "command": "python3", "args": ["/abs/path/to/mcp_server.py"] }
}}Requires the official mcp python package (pip install mcp). The deterministic
core (inspect_workspace / check_addopts / summarize) imports and runs with
zero dependencies — the mcp package is only needed for the stdio server.
Streamable HTTP (remote/Smithery-publishable)
python3 mcp_server.py --http --port 8137 # serves on http://<host>:8137/mcp/Run with --http to serve over Streamable HTTP (a remote MCP endpoint) instead of
stdio. This is the transport smithery mcp publish <url> expects for URL-based
publishing — so once a Smithery service token exists, the server deploys as-is.
Example
inspect_workspace(path="/home/runner/vulcanbench/workspace/task-1")
→ {
"ok": true,
"workspace": "/home/runner/vulcanbench/workspace/task-1",
"ini_chain": [{"file": "/home/runner/vulcanbench/pyproject.toml",
"addopts": "--cov=harness ... --cov-fail-under=80"}],
"effective_addopts": "--cov=harness ... --cov-fail-under=80",
"will_corrupt_grading": true,
"verdict": "CORRUPTED",
"fixed_command": ["python3", "-m", "pytest", "-o", "addopts=", "<tests>"],
"reasons": ["coverage gate(s) present: ['--cov', '--cov-fail-under']"]
}Verification
python3 test_detector.py— 5/5 core detection checks (root gate, nested inheritance, clean, abort gate, pyproject-no-pytest)python3 test_e2e.py— drives the real MCP stdio transport (initialize → tools/call) and asserts CORRUPTED / CLEAN thread through the wire
Part of a family
This is one of three deterministic, no-LLM agent-trust MCP servers by sudo-ai-git:
mcp-skill-sec— pre-install skill/security auditmcp-verify-claim— evidence-gated, honestly-tiered claim reportingmcp-benchmark-hygiene— pytest config-leakage / eval-honesty detection (this repo)
Sibling product: mcp-token-saver — token-cost proxy + analyzer for agent conversations (dedupes redundant tokens before they're billed; live-proven 74% cut). Discussion
Also in the family (a free CLI, not an MCP server): harness-audit — deterministic agent-eval / benchmark-grading hygiene audit that catches the same silent config-leakage mis-scoring class. Free lead-magnet; the same verification discipline, zero dependencies, auditable line-by-line.
License & provenance
MIT. Independently derived from the documented VulcanBench #79 finding; no endorsement by or affiliation with morganlinton/VulcanBench implied.
Hire a custom integration
Need this connected to your internal system (auth, logging, security-scan pass, hosted)? Open a custom-build request. MIT reference assets are free to use either way.
This server cannot be deployed
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