vibe-check-mcp-server: Vibe Check is a tool that provides mentor-like feedback to AI Agents, preventing tunnel-vision

by PV-Bhat · MCP Server · ★ 502

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

How to install vibe-check-mcp-server

git clone https://github.com/PV-Bhat/vibe-check-mcp-server.git

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 68/100

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.

agentic-aiagentic-workflowai-agentschain-of-thoughtcpierror-handlingmcpmcp-servermodel-context-protocolrli

Quick Facts

Stars502
Forks67
LanguageTypeScript
CategoryMCP Server
LicenseMIT
Quality Score68/100
Last Updated2026-08-20
Created2025-03-22
Platformsmcp, node
Est. Tokens~18k

About vibe-check-mcp-server

Vibe Check MCP This project is in maintenance mode. Active feature development has ended; only maintenance patches (security and bug fixes) are published. v2.8.1 is the latest maintenance release. The server remains fully functional. Community forks and contributions are welcome under the MIT license. KISS overzealous agents goodbye. Plug & play agent oversight tool.

Compatible Skills

These tools work well together with vibe-check-mcp-server for enhanced workflows:

  • agent-rules — semantic(0.24)+complementary+same_lang+similar_pop+shared_platform (53%)
  • Mysti — semantic(0.23)+complementary+same_lang+similar_pop+shared_platform (53%)
  • agentic — semantic(0.23)+complementary+same_lang+similar_pop+shared_platform (53%)
  • deepbot — semantic(0.23)+complementary+same_lang+similar_pop+shared_platform (53%)
  • Dorothy — semantic(0.18)+complementary+same_lang+similar_pop+shared_platform (51%)

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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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