codequality-mcp
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- AlicenseNot gradedqualityCmaintenanceEvaluates RAG outputs on faithfulness, answer relevancy, and context precision using an LLM-as-a-Judge backend. Exposes tools for running evaluations, scoring individual samples, and checking thresholds, enabling CI gating and on-demand assessment via MCP.MIT
- AlicenseAqualityDmaintenanceProvides 28 MCP tools across 16 analysis engines for comprehensive Python code quality assessment, including complexity scoring, security scanning, dead code detection, dependency auditing, and test quality analysis.28MIT
- AlicenseNot gradedqualityBmaintenanceEnables MCP clients to run security and code review on pull requests and diffs, exposing review_pr and review_diff capabilities with local-first analyzers and LLM explanations.2MIT
- AlicenseBqualityAmaintenanceEnables auditing codebases for production readiness, including linting, testing, CI/CD, security, branch conventions, architecture, and an A–F quality scorecard via MCP tools.1501MIT
- AlicenseNot gradedqualityCmaintenanceEnables AI coding agents to enforce architecture standards by checking repositories against overridable rules and adapter results over MCP.144 npmMIT

Patronus MCP Serverofficial
AlicenseNot gradedqualityDmaintenanceEnables running LLM evaluations, experiments, and custom evaluators through a standardized MCP interface.16Apache 2.0
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose: evaluate runs the quality analysis, explain_rule provides details on a specific rule, and list_analyzers checks analyzer health. No overlap or ambiguity in selection.
Two tools follow a verb_noun pattern (explain_rule, list_analyzers), while evaluate is a bare verb. This minor deviation is still readable and consistent in snake_case, but prevents a perfect score.
Three tools are well-scoped for a focused code-quality analysis server; each tool (evaluate, explain_rule, list_analyzers) earns its place without redundancy.
The surface covers running evaluations, explaining rules, and checking analyzer availability, but lacks a way to list all rules or dimensions directly. This minor gap is workable via evaluate findings, but agents cannot discover rules upfront.