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Airflow MCP

by zamax14
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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

    B3.4/5.0

    Scored across 9 tools

    Disambiguation5/5

    Each tool targets a distinct entity or action: DAG metadata, DAG run details, task logs, listing operations, and state changes (pause, unpause, trigger). No overlapping purposes.

    Naming Consistency5/5

    All names follow a consistent verb_noun pattern with underscores (e.g., get_dag, list_dags, trigger_dag_run). Verbs are clear and nouns precisely identify the resource.

    Tool Count5/5

    9 tools is appropriate for an Airflow MCP server, covering core operations without redundancy. The count is well-scoped for the domain.

    Completeness4/5

    Covers essential DAG, DAG run, and task instance operations. Minor gaps like clearing task instances or updating DAG configuration are missing but not critical for typical monitoring and triggering workflows.

    Maintenance

    ActivityInactive
    ResponsivenessResponsive

    Resources