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mcp-data-analysis-agent

by hassanvfx
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    TDQS

    D1.8/5.0

    Scored across 20 tools

    Disambiguation3/5

    Many tools have clear distinct responsibilities, but some pairs overlap in intent (get_schema vs schema_state, validate_sql vs validate_and_execute) and task lifecycle tools (begin/complete/evaluate) could be confused without descriptions. The boundaries are mostly inferable but not consistently crisp.

    Naming Consistency2/5

    Tool names mix verb-first actions (verify_observability, get_schema, run_metric) with noun-only states (schema_state, task_timeline) and one-word commands (preflight, welcome). The pattern is inconsistent, though most names are readable.

    Tool Count3/5

    20 tools is on the higher side but not unreasonable for a data-analysis agent covering tasks, schema, SQL, metrics, and recipes. Several tools could be consolidated (e.g., schema_state into get_schema, validate_and_execute with validate_sql), which would make the count more focused.

    Completeness4/5

    The set covers core data-analysis workflows: task lifecycle, schema introspection, SQL validation, metric execution, comparisons, and change detection. It lacks explicit data fetching/result retrieval endpoints, but the metric and SQL tools likely fill that role; onboarding tools are extra rather than essential.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources