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SAS MCP Server

by raedaldweik
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Python
Remote

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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 28 tools

    Disambiguation4/5

    Most tools are clearly separated by resource and action, but list_models_and_decisions and list_registered_models both list models from different repositories, and execute_sas_code vs submit_batch_job are similar (sync vs async), creating minor ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent lowercase verb_noun snake_case pattern (e.g., list_ml_projects, score_data, cancel_job), with no mixed conventions or vague verbs.

    Tool Count3/5

    28 tools is on the heavy side, and the set covers many domains (AutoML, data, files, jobs, CAS, charts), but the count is justified by the platform's breadth; still, several tools could be merged (e.g., the two model listing tools).

    Completeness3/5

    The tool set covers core workflows for AutoML, data exploration, and job execution, but lacks update operations for ML projects and delete operations for tables/files, leaving some lifecycle gaps.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues

    Resources