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MCP DS Toolkit Server

by Yasserelhaddar
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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

    B3.1/5.0

    Scored across 30 tools

    Disambiguation4/5

    Most tools have clearly distinct purposes, but 'compare_runs' is vague compared to more specific comparisons like 'compare_models' and 'compare_datasets', and 'preprocess_dataset' could overlap slightly with 'clean_dataset' and 'validate_dataset' though descriptions help differentiate.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with snake_case (e.g., clean_dataset, train_model, log_metrics), making the set predictable and easy to navigate.

    Tool Count3/5

    With 30 tools, the set is on the heavy side but each tool addresses a specific aspect of data science workflows (data ops, modeling, experiment tracking). While some tools could be consolidated, the count is still reasonable for a comprehensive toolkit.

    Completeness3/5

    The tool set covers major lifecycle steps from data loading to model evaluation and experiment tracking, but lacks a delete_model tool and explicit feature engineering or data merging capabilities, leaving minor but notable gaps.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources