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multivon-mcp

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    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A3.9/5.0

    Scored across 23 tools

    Disambiguation3/5

    Most eval tools target distinct metrics, but several are easy to conflate: eval_faithfulness/eval_hallucination are inverse measures of the same construct, eval_vqa_faithfulness/eval_document_grounding both do vision-grounded checking, and eval_relevance/eval_answer_accuracy both score response quality. The detailed descriptions help, but the sheer number of similar score/pass/reason evaluators still creates real selection ambiguity.

    Naming Consistency4/5

    All tools use snake_case and nearly all share the eval_ prefix followed by a metric noun (eval_toxicity, eval_context_precision), with a few verb-style exceptions (eval_discover, eval_ingest_trace, eval_generate_cases). The two pdfhell_* tools form a coherent sub-namespace rather than a violation, so overall naming is consistent but not perfectly uniform.

    Tool Count3/5

    With 23 tools, this is on the heavy end for an MCP server and an agent must navigate a large surface. The breadth is defensible for a full LLM evaluation platform covering text, vision, RAG, safety, and PDF benchmarks, but some tools are closely related and could plausibly be consolidated.

    Completeness4/5

    The set covers a full eval lifecycle: case generation, trace ingestion, diverse text/vision/RAG/safety evaluators, report comparison, acceptance policies, and audit packaging. Minor gaps remain—such as no dedicated summarization or code-quality evaluator and no explicit tool for assembling arbitrary eval results into a saved report—but generic G-Eval and custom-rubric tools close most holes.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources