stats-compass-mcp
PyPI
v0.3.22
Published by an unidentified publisher — no publish provenance and no public repository, so the publisher could not be verified and the source cannot be independently located.
The grade answers one question — how safe is this server for you to adopt — so it is computed in two auditable stages. Nothing below is an opinion or an LLM's guess; every line is a real term the deterministic engine applied, and the same input always yields the same number.
1. Threat score — 100 − 2.1 = 98. What the published surface and source actually contain:
| Points | What was found | Category |
|---|---|---|
| −2.1 | Package has no source repository MTC-SUP-011 | supply-chain |
2. Client adoption risk — 98 − 8 = 90. Three small, subtract-only factors that reflect your risk in adopting it — a clean scan proves less on a powerful, unverified or barely-inspectable package, so the grade says so plainly:
| Points | Adoption-risk factor |
|---|---|
| −3 | capability blast radius (moderate) — client exposure if the model is manipulated |
| −5 | publisher verification (unlocatable) — no provenance and no public repository to inspect |
| ✓ | inspection depth (source) — how much of the target the scan could see |
Capability observations and info notes are shown under Findings but never scored.
Open any row's finding below for the file, line and evidence behind a deduction.
Tool "save_model" can write, overwrite or delete files (keyword "save_file"). Verify it is scoped to a safe directory.
Fix: Constrain file operations to an explicit, non-sensitive root; reject path traversal.
Location: tool save_model
Tool "save_model" can mutate/egress but declares no destructiveHint. Clients that don't default to spec-safe behavior may not prompt before running it.
Fix: Declare accurate annotations, and gate destructive tools on user confirmation regardless.
Location: tool save_model
"stats-compass-mcp" declares no repository URL, so its published artifact cannot be compared against reviewable source.
Fix: Prefer packages that link to public, reviewable source.
Location: package stats-compass-mcp
Each tool and what it can reach — statically extracted from the published source.
describe_data_toolsingests untrusted inputreads sensitive datasave_modelingests untrusted inputwrites fileslist_filesreads sensitive datadelete_sessionno sensitive capabilitydescribe_cleaning_toolsno sensitive capabilitydescribe_eda_toolsno sensitive capabilitydescribe_ml_toolsno sensitive capabilitydescribe_plot_toolsno sensitive capabilitydescribe_transform_toolsno sensitive capabilityexecute_cleaning_toolno sensitive capabilityexecute_data_toolno sensitive capabilityexecute_eda_toolno sensitive capabilityexecute_ml_toolno sensitive capabilityexecute_plot_toolno sensitive capabilityexecute_transform_toolno sensitive capabilityget_sampleno sensitive capabilityget_schemano sensitive capabilityget_upload_urlno sensitive capabilityget_usage_guideno sensitive capabilitylist_dataframesno sensitive capabilityload_csvno sensitive capabilityload_datasetno sensitive capabilityload_excelno sensitive capabilitypingno sensitive capabilityregister_uploaded_fileno sensitive capabilityrun_classification_workflowno sensitive capabilityrun_eda_report_workflowno sensitive capabilityrun_preprocessing_workflowno sensitive capabilityrun_regression_workflowno sensitive capabilityrun_timeseries_workflowno sensitive capabilitysave_csvno sensitive capabilityserver_statsno sensitive capabilitysession_infono sensitive capabilityScan history per published version. The engine is deterministic — the same version always yields the same score, so a changed score means the package itself changed.
| Version | Score | Findings | Engine | Scanned |
|---|---|---|---|---|
v0.3.22 latest |
A 90/100 | 3 | 1.8.0 | 2026-07-23 |
Show this server's live Trust Score in your README, docs or website. The badge is served straight from the registry and updates automatically after every rescan — no API key needed. It links back to this page, so anyone who sees the grade can also read the findings behind it instead of taking a number on faith.
The score above is reproducible: the same package version always yields the same result. Run it locally or over the free API — no account, no LLM, fully deterministic.
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