MCP as a Judge ⚖️

mcp-name: io.github.OtherVibes/mcp-as-a-judge

MCP as a Judge acts as a validation layer between AI coding assistants and LLMs, helping ensure safer and higher-quality code.

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• Treat LLM output as ground truth; skip research and use outdated information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

Vibe coding doesn’t have to be frustrating

What it enforces

• Evidence‑based research and reuse (best practices, libraries, existing code)
• Plan‑first delivery aligned to user requirements
• Human‑in‑the‑loop decisions for ambiguity and blockers
• Quality gates on code and tests (security, performance, maintainability)

Key capabilities

• Intelligent code evaluation via MCP sampling; enforces software‑engineering standards and flags security/performance/maintainability risks
• Comprehensive plan/design review: validates architecture, research depth, requirements fit, and implementation approach
• User‑driven decisions via MCP elicitation: clarifies requirements, resolves obstacles, and keeps choices transparent
• Security validation in system design and code changes

Tools and how they help

| Tool | What it solves |