frame-trace-mcp: Best AI QA Evidence Recorder MCP Server for Automated Testing 2026

by yashsindya2014-hash · MCP Server · ★ 68

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 59/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

computer-visionmcpmodel-context-protocolqarobloxscreen-recordingtesting

Quick Facts

Stars68
Forks0
LanguageHTML
CategoryMCP Server
Quality Score59/100
Last Updated2026-10-22
Created2026-09-22
Platformsmcp
Est. Tokens~19k

About frame-trace-mcp

🎞️ FrameHarbor — Temporal Evidence Vault for AI Quality Assurance FrameHarbor is an ambient, always-on visual journal for software quality workflows. Where traditional QA tools capture a single screenshot at a moment of failure, FrameHarbor continuously weaves a rolling tapestry of frames from your desktop or device viewport, then exposes that tapestry to AI assistants through the Model Context...

Compatible Skills

These tools work well together with frame-trace-mcp for enhanced workflows:

  • game-slop-purge — semantic(0.15)+complementary+rare_topics+same_lang+similar_pop (55%)
  • lua-script-bridge — semantic(0.38)+rare_topics+same_lang+similar_pop+shared_platform (53%)
  • mcp-roblox-profile-lens — semantic(0.38)+rare_topics+same_lang+similar_pop+shared_platform (53%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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