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Zaturn

by kdqed
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Python
Hybrid

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    TDQS

    A3.7/5.0

    Scored across 12 tools

    Disambiguation5/5

    Every tool has a clearly distinct purpose: 10 visualization tools each create different plot types (bar, box, scatter, etc.), 'run_query' returns raw data, 'list_data_sources' enumerates sources, and 'describe_table' provides schema information. There is no functional overlap between tools - an agent can easily select the right tool for each task.

    Naming Consistency5/5

    All tools follow a consistent snake_case naming convention with clear verb_noun patterns: visualization tools use plot_type names (bar_plot, scatter_plot), while utility tools use action_object patterns (run_query, list_data_sources, describe_table). The naming is perfectly predictable and follows a single convention throughout.

    Tool Count5/5

    12 tools is well-scoped for a data visualization and query server. The count includes comprehensive visualization coverage (10 plot types), core data operations (run_query), and metadata utilities (list_data_sources, describe_table). Each tool earns its place without redundancy or bloat.

    Completeness4/5

    The toolset provides excellent coverage for data exploration and visualization: query execution, schema inspection, source enumeration, and multiple plot types. Minor gaps include lack of data modification tools (update/delete) and advanced analytics functions, but these may be outside the server's intended scope as a visualization-focused tool.

    Maintenance

    ActivityInactive
    ResponsivenessUnresponsive

    Resources