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nmdc-mcp

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by microbiomedata
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Python
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    TDQS

    A3.5/5.0

    Scored across 16 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose: fetching GFF by PFAM domains, retrieving collection IDs, biosamples by study, collection names/stats, data objects by PFAM, entities by ID with/without projection, samples by annotation/ecosystem/elevation/lat-lon, study DOI details, study for biosample, and searching studies by DOI. Even similar functions like entity retrieval are differentiated by batch vs single and projection vs full.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern using snake_case, primarily starting with 'get_' or 'fetch_', with clear and descriptive nouns. The only variation is 'search_studies_by_doi_criteria', but it still maintains the pattern. No mixing of camelCase or inconsistent verb styles.

    Tool Count4/5

    16 tools is slightly above the typical 3-15 range, but it is well-scoped for the NMDC database query domain. Each tool serves a specific purpose without redundancy, and the count is reasonable given the variety of query needs (by ID, by annotation, by location, etc.).

    Completeness4/5

    The tool set covers a comprehensive range of query operations for the NMDC database: listing collections, retrieving entities by ID, searching biosamples by multiple criteria (study, ecosystem, elevation, location, PFAM domains), and accessing study DOIs. Minor gaps include lack of direct query for data objects by other attributes, but for a read-only query interface, this is nearly complete.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

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