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mlflow-mcp-server

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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

    B3.2/5.0

    Scored across 82 tools

    Disambiguation5/5

    Every tool targets a distinct resource-action pair (e.g., create-experiment vs create-run vs create-logged-model). Even similar functions like search-runs and search-runs-by-tags are clearly differentiated by the 'by-tags' suffix. There is no ambiguity between any two tools.

    Naming Consistency5/5

    All tool names follow a consistent verb-noun pattern using lowercase and hyphens (e.g., delete-run-tag, set-experiment-tag, search-registered-models). The pattern is uniform across all 82 tools, with no mixing of conventions or unexpected formats.

    Tool Count2/5

    With 82 tools, the count far exceeds the recommended 3-15 range for a typical server. While MLflow is a broad platform, the fine-grained separation into individual operations (e.g., separate log-metric, log-param, log-batch) results in an overwhelming surface that could hinder agent efficiency, despite the presence of a search-tools helper.

    Completeness5/5

    The tool set covers the entire MLflow lifecycle: experiments, runs, model registry (including aliases and versions), logged models, prompt optimization, traces with assessments, webhooks, and compound aggregate tools like summarize-experiment. There are no obvious dead ends or missing CRUD operations for the domain.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

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