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Plotnine MCP Server

by Fervoyush
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Python
Hybrid

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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

    A3.9/5.0

    Scored across 11 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose with no ambiguity: batch_create_plots handles multiple plots, create_plot builds custom visualizations, create_plot_from_template uses predefined templates, export/import_plot_config manage configurations, list_color_palettes/geom_types/templates/themes provide discovery, preview_data inspects data, and suggest_plot_templates offers recommendations. The tools cover different aspects of the plotting workflow without overlap.

    Naming Consistency5/5

    All tool names follow a consistent snake_case verb_noun pattern (e.g., create_plot, list_color_palettes, preview_data). The naming is predictable and readable throughout, with verbs like create, list, export, import, preview, and suggest clearly indicating actions.

    Tool Count5/5

    With 11 tools, the server is well-scoped for a plotting library, covering creation (custom, batch, template-based), configuration management, data inspection, and discovery of palettes, geometries, templates, and themes. Each tool earns its place by addressing a specific need in the visualization workflow.

    Completeness5/5

    The tool surface is complete for the plotnine domain, offering full lifecycle coverage: data inspection (preview_data), creation (create_plot, batch_create_plots, create_plot_from_template), customization (list tools for discovery), configuration management (export/import_plot_config), and intelligent assistance (suggest_plot_templates). There are no obvious gaps that would hinder agent workflows.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources