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uniprot-link

by berntpopp
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Python
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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

    A4.7/5.0

    Scored across 15 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose: find_proteins vs find_proteins_batch are differentiated by batch vs single; get_protein_* tools each target specific data aspects (sequence, features, variants, diseases, GO, cross-refs); SPARQL-related tools are separate; taxonomy and mapping tools are unique. No overlap in functionality.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern: find_*, get_*, map_*, run_*, search_*. The naming is predictable and homogeneous, making it easy for an agent to understand the action and target of each tool.

    Tool Count5/5

    With 15 tools, the server is well-scoped for querying UniProt data. It covers search, retrieval of detailed protein information, cross-references, sequence, features, variants, diseases, GO terms, taxonomy, identifier mapping, SPARQL queries, and server capabilities, without being excessive.

    Completeness5/5

    The tool surface is comprehensive for the domain: all major UniProt data types are covered (core summary, cross-references, features, sequence, variants, diseases, GO terms). The inclusion of find_proteins_batch, map_identifiers, get_taxon, and the SPARQL escape hatch ensures no major workflow dead ends. Minor gaps like subcellular localization can be obtained via GO terms or SPARQL.

    Maintenance

    ActivityActive
    ResponsivenessResponsive

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