codequality-mcp
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- AlicenseNot gradedqualityDmaintenanceEvaluates 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
- AlicenseNot gradedqualityAmaintenanceEnables MCP clients to evaluate text or local files for LLM-output quality issues, returning 0-100 scores, grades, and precise line/column findings. It also lets clients list all available lint rules and severities.MIT
- AlicenseNot gradedqualityBmaintenanceEnables pixi-aware quality gates for linting, type checking, security scanning, complexity analysis, baselines, trend analysis, automated fixes, and comprehensive reporting through a small three-meta-tool MCP interface.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.2815 PyPIMIT
- 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
- AlicenseAqualityAmaintenanceEnables MCP clients to score retrieval quality with recall, precision, nDCG, hit rate and MRR, compare 2-3 configurations with seeded paired-bootstrap confidence intervals, audit agent tool calls against expected traces, and detect prompt regressions against golden sets. It also generates Markdown, JSON or JUnit reports with pass/fail gates, all deterministically and offline without paid API keys.6308 npm1MIT
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.