axiom-perception-mcp
PyPI
v2.1.0
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.
Empirical memory for AI agents — patterns learned from real executions, not hand-written rules. Workflow checkpointing, failure knowledge base, multi-agent coordination. Zero API keys required.
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 − 0 = 100. What the published surface and source actually contain:
The deterministic scan raised no scored threat in the surface it inspected — the threat score stayed at 100. Capability observations and advisory notes are recorded but never lower it.
2. Client adoption risk — 100 − 5 = 95. 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 |
| −2 | publisher verification (unlinked) — no provenance/repo link, but the shipped source was fully read |
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.
Each tool and what it can reach — statically extracted from the published source.
fetch_community_patternsingests untrusted inputabandon_checkpointno sensitive capabilitycheck_accessibility_permissionsno sensitive capabilityclear_agent_progressno sensitive capabilityclick_elementno sensitive capabilitycomplete_checkpointno sensitive capabilitydelete_noteno sensitive capabilityexport_patternno sensitive capabilityfind_elementno sensitive capabilityget_agent_progressno sensitive capabilityget_app_ui_treeno sensitive capabilityget_focused_elementno sensitive capabilitylist_checkpointsno sensitive capabilitylist_notesno sensitive capabilitylist_patternsno sensitive capabilitylist_running_appsno sensitive capabilityread_noteno sensitive capabilityrecall_patternno sensitive capabilityrecord_outcomeno sensitive capabilityreport_stepno sensitive capabilityresume_checkpointno sensitive capabilitysave_checkpointno sensitive capabilitysave_patternno sensitive capabilitysearch_patternsno sensitive capabilityshare_noteno sensitive capabilitytype_in_elementno sensitive capabilityupdate_patternno 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 |
|---|---|---|---|---|
v2.1.0 latest |
A 95/100 | 0 | 1.13.0 | 2026-08-25 |
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.
npx mcptrustchecker scan axiom-perception-mcp --online --registry pypi
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