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data-quality-cde-mcp-server

by frothkoetter
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Python
Remote

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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 13 tools

    Disambiguation3/5

    Most tools are distinct, but there is notable overlap among run-monitoring tools (get_cde_job_run_status, get_airflow_dag_run_status, list_cde_job_runs) and between generate_dag_from_contract and deploy_contract_dq_job, which both generate DAGs. This creates moderate ambiguity for an agent.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern in lowercase snake_case (e.g., list_cde_jobs, trigger_cde_job, delete_cde_job). The verbs are distinct and the pattern is highly predictable.

    Tool Count5/5

    13 tools is well-scoped for a CDE/CDW data-quality server, covering configuration, connectivity checks, DAG generation/deployment, job lifecycle, and monitoring without unnecessary bloat.

    Completeness5/5

    The tool surface covers the full workflow: configure and diagnose connectivity, generate/preview/deploy data contract DAGs, manage jobs, trigger runs, monitor status, and delete jobs. No critical operations are missing for the stated domain.

    Maintenance

    ActivityStale
    ResponsivenessNo issues

    Resources