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MarkIvor

DataSearcher MCP

by MarkIvor
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Python
Hybrid

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    TDQS

    C2.4/5.0

    Scored across 46 tools

    Disambiguation2/5

    Many tools occupy adjacent analytical niches—smart_summary, auto_insights, data_story, build_dashboard, and create_public_dashboard all produce summary/insight outputs, while profile_data and data_quality_report overlap and export_data/export_xlsx differ mostly by format. Although descriptions are detailed, an agent would frequently struggle to pick between near-equivalent options.

    Naming Consistency4/5

    The vast majority of tools follow a verb_noun snake_case pattern like get_schema, transform_data, and build_dashboard. A few noun-based names such as sql_query, query_explain, correlation_analysis, and data_story are minor deviations, but the overall convention is predictable.

    Tool Count2/5

    With 46 tools, the surface is heavily overloaded for a single MCP server. Many tools could be consolidated, such as the export pair, dashboard/story/summary family, or the many statistical analysis tools, and the large count increases selection cost and maintenance burden.

    Completeness3/5

    Core data connection, query, analysis, visualization, and metadata workflows are broadly covered, so most agent tasks can be completed. However, the knowledge-base lifecycle lacks delete operations, and connection management has attach/test/refresh but no list/detach, creating some dead ends.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues

    Resources