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Genomics MCP

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by rewire-bio
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Python
Hybrid

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    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

    B3.4/5.0

    Scored across 23 tools

    Disambiguation5/5

    Each tool targets a distinct data type or action: sequence, features, signal, reads, coverage, pileup, variants, samples, metadata, transfers, locus inspection, sample comparison, identifier resolution, variant normalization, variant/gene/protein lookups, source listing, dataset search/description, and file listing. Overlaps like get_reads/get_coverage/get_pileup are differentiated by output, and resolve_identifier vs lookup_* by scope.

    Naming Consistency5/5

    All tool names follow a consistent snake_case verb_noun pattern (e.g., get_sequence, list_samples, fetch_file, normalize_variant, lookup_protein). No mixing of conventions or vague verbs; the pattern is predictable across all 23 tools.

    Tool Count4/5

    23 tools is slightly above the ideal 3-15 range but reasonable given the breadth of genomics data formats (FASTA, BED, GFF, bigWig, BAM, CRAM, VCF) and operations (access, lookup, transfer, inspection). Each tool appears to earn its place, though some consolidation of similar BAM/CRAM tools could reduce count.

    Completeness4/5

    The surface covers core genomics workflows: sequences, features, signal, reads, coverage, pileup, variants, samples, datasets, files, transfers, identifier resolution, variant normalization, and lookups for variants/genes/proteins. Minor gaps exist, such as searching genes by keyword or listing variants by gene without a locus, but agents can work around these.

    Maintenance

    ActivityMaintained
    ResponsivenessUnresponsive

    Resources