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mcp-data-service

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    TDQS

    B3.2/5.0

    Scored across 11 tools

    Disambiguation3/5

    Most tools are distinct, but list_columns and get_schema clearly overlap, and query_video_stats, aggregate_stats, and data_insights have fuzzy boundaries around statistics and insights. Descriptions help somewhat, but an agent could easily select the wrong tool for a given analytical request.

    Naming Consistency3/5

    All names are snake_case, but conventions are mixed: verb-noun names like list_columns and preview_data coexist with noun-phrase names like data_insights and correlation_analysis. query_video_stats also stands out as a domain-specific exception to the otherwise general-purpose naming pattern.

    Tool Count5/5

    Eleven tools is an appropriate size for a data service covering source management, schema inspection, preview, aggregation, correlation, SQL querying, and health checks. The count is neither bloated nor too thin for the stated purpose.

    Completeness4/5

    The tool surface covers the core read-only data workflow well: discovering sources, inspecting schema, previewing, aggregating, running SQL, checking correlations, and getting automated insights. Minor gaps exist—such as no explicit dataset-comparison or export tool—but they are workable through sql_query and preview_data.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues

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