mnemex
PyPI
v0.6.0
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.
Mnemex: Temporal memory management for AI assistants with human-like dynamics
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 − 1.2 = 99. What the published surface and source actually contain:
| Points | What was found | Category |
|---|---|---|
| −1.2 | Hardcoded JSON Web Token in test/example/packaging MTC-SRC-008 | exfiltration |
2. Client adoption risk — 99 − 2 = 97. 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 |
|---|---|
| −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.
A hardcoded JSON Web Token (an embedded JSON Web Token (often an example or expired token)) appears in `tests/test_security_secrets.py:306`. Verify whether this is a real credential; if so, remove and rotate it.
Evidence: JSON Web Token: eyJh…(redacted)
Fix: Remove the secret, rotate it, and load credentials from the environment or a secret store.
Location: server tests/test_security_secrets.py
Each tool and what it can reach — statically extracted from the published source.
cluster_memoriesno sensitive capabilityconsolidate_memoriesno sensitive capabilitycreate_relationno sensitive capabilitygcno sensitive capabilityget_performance_metricsno sensitive capabilityobserve_memory_usageno sensitive capabilityopen_memoriesno sensitive capabilitypromote_memoryno sensitive capabilityread_graphno sensitive capabilityreset_performance_metricsno sensitive capabilitytouch_memoryno 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 |
|---|---|---|---|---|
v0.6.0 latest |
A 97/100 | 1 | 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 mnemex --online --registry pypi
Independent packages implementing the same tool, scanned with the same engine. Compare all 2 side by side →
FDA device & vehicle recall risk for AI agents: recall history, MAUDE trend, risk score.
Open-source MCP server exposing Agent402.Tools' catalog — 500+ strong: 400+ self-hostable tools + 100 multi-tool skill packs (security-audit, trend-analysis, structured-scrape, decode-blob, forecasting-bake-off) for AI agents — browser, web search & answe
Zero-dependency MCP server that gives AI agents a self-updating project memory in AGENTS.md. Returns merge instructions instead of mutating state, so every change is a reviewable diff.
MCP Apps UI resources and server helpers for n8n
MCP server providing comprehensive access to BookStack knowledge management system
MCP server for tracking achievements with STAR methodology