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data-quality-cde-mcp-server

by frothkoetter
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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

    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

    ActivitySlowing
    ResponsivenessNo issues

    Resources