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CRC-LNM Medical Agent

by lisazhanggg0810-source
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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

    A3.5/5.0

    Scored across 6 tools

    Disambiguation5/5

    Each tool serves a distinct pipeline stage: model info retrieval, case data QC, CT feature preparation, pathology feature preparation, multimodal prediction, and report generation. No two tools appear to handle the same responsibility, so an agent can unambiguously select the right tool for each step.

    Naming Consistency4/5

    All tools share the consistent 'crc_lnm_' prefix and use snake_case. Most follow a verb_noun pattern (get_model_info, prepare_ct_features, generate_report), though 'case_data_qc' is more noun-like and 'predict_multimodal' uses an adjective, creating minor deviations. Overall the naming is predictable and readable.

    Tool Count5/5

    Six tools cover the full end-to-end workflow of a specialized medical AI pipeline without redundancy. The count is appropriately scoped for the server's purpose, neither sparse nor bloated.

    Completeness5/5

    The tool set covers the complete workflow from model inspection and data QC through feature preparation, prediction, and report generation. There are no obvious gaps for the intended use case, as each step in the pipeline is represented.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues

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