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codequality-mcp

by c-daly
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    • LLM Evaluation Harness MCP

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      A
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      quality
      D
      maintenance
      Evaluates 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.
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    • llmlint

      la2278647-arch
      A
      license
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      quality
      A
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      Enables 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.
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    Deploy Server
    Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

    TDQS

    A3.6/5.0

    Scored across 3 tools

    Disambiguation5/5

    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.

    Naming Consistency4/5

    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.

    Tool Count5/5

    Three tools are well-scoped for a focused code-quality analysis server; each tool (evaluate, explain_rule, list_analyzers) earns its place without redundancy.

    Completeness4/5

    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.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

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