The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the MCP Benchmark Hygiene listing page.
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?
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.:
...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.
When a workspace is flagged CORRUPTED, the tool returns the corrected command:
-o addopts= strips inherited coverage/abort gates. (Or run the grader from
outside the repo root.)
| tool | purpose |
|---|---|
inspect_workspace(path) | full analysis: ini chain, effective addopts, CLEAN/CORRUPTED/UNKNOWN verdict + corrected command |
check_addopts(path) | thin boolean: corrupted + reasons |
summarize(analysis) | one-line actionable summary string |
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:
--cov, --cov-fail-under, --cov-report, --cov-config--maxfail, -x, --strict, --strict-markers, --pdb, --ffOnly gates that change exit codes / abort grading are flagged. A harmless
addopts is reported CLEAN with the exact string.
One command (recommended) — installs from the repo, no PyPI token needed:
Or with pipx: pipx install git+https://github.com/sudo-ai-git/mcp-benchmark-hygiene
Direct from source (fallback):
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.
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.
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 wireThis 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.
MIT. Independently derived from the documented VulcanBench #79 finding; no endorsement by or affiliation with morganlinton/VulcanBench implied.
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.