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scigantic-mcp

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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

    A3.7/5.0

    Scored across 5 tools

    Disambiguation4/5

    Most tools are clearly distinct: search returns matches, get_archive fetches full metadata, get_schema_card explains structure, list_archive_files samples files, get_data_access provides load code. However, get_archive and get_schema_card overlap somewhat since both retrieve metadata about an archive, and list_archive_files could be confused with get_schema_card's file/column info. Descriptions mostly clarify boundaries.

    Naming Consistency3/5

    All use snake_case with verb_noun structure, but verbs are mixed in style: 'search_archives' and 'get_archive' share the archives noun base, while 'get_schema_card', 'list_archive_files', and 'get_data_access' diverge to different nouns. Each tool combines get/search/list with a distinct noun, but the noun targeting is inconsistent (archives vs schema_card vs archive_files vs data_access).

    Tool Count5/5

    Five tools is well-scoped for a scientific data catalog server. Each tool has a clear purpose: discovery (search), metadata (get_archive), structure (get_schema_card), file listing (list_archive_files), and access guidance (get_data_access). Together they cover a complete discovery-to-access workflow without redundancy.

    Completeness4/5

    The workflow is well covered: search to find datasets, get_archive for metadata, get_schema_card for structure, list_archive_files for contents, and get_data_access for loading. Minor gaps exist, such as no tool to filter/sort search results more granularly, no preview/download of actual data contents, and get_archive is somewhat redundant given get_schema_card. But core discovery and access needs are met.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources