MCP as a Judge: a behavioral MCP that strengthens AI coding assistants via explicit LLM evaluations
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# MCP as a Judge ⚖️ mcp-name: io.github.OtherVibes/mcp-as-a-judge <div align="left"> <img src="assets/mcp-as-a-judge.png" alt="MCP as a Judge Logo" width="200"> </div> > MCP as a Judge acts as a validation layer between AI coding assistants and LLMs, helping ensure safer and higher-quality code. [](https://opensource.org/license/mit/) [](https://www.python.org/downloads/) [](https://modelcontextprotocol.io/) [](https://github.com/OtherVibes/mcp-as-a-judge/actions/workflows/ci.yml) [](https://github.com/OtherVibes/mcp-as-a-judge/actions/workflows/release.yml) [](https://pypi.org/project/mcp-as-a-judge/) **MCP as a Judge** is a **behavioral MCP** that strengthens AI coding assistants by requiring explicit LLM evaluations for: - Research, system design, and planning - Code changes, testing, and task-completion verification It enforces evidence-based research, reuse over reinvention, and human-in-the-loop decisions. > If your IDE has rules/agents (Copilot, Cursor, Claude Code), keep using them—this Judge adds enforceable approval gates on plan, code diffs, and tests. ## Key problems with AI coding assistants and LLMs - Treat LLM output as ground truth; skip research and use outdated information - Reinvent the wheel instead of reusing libraries and existing code - Cut corners: code below engineering standards and weak tests - Make unilateral decisions when requirements are ambiguous or plans change - Security blind spots: missing input validation, injection risks/attack vectors, least‑privilege violations, and weak defensive programming …
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