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MCP-Airflow-API

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    TDQS

    C2.9/5.0

    Scored across 54 tools

    Disambiguation4/5

    Most tools clearly target distinct resources and actions, such as list_dags, get_dag, and trigger_dag. However, a few pairs like list_task_instances_all vs list_task_instances_batch and list_dataset_events vs get_dataset_events have overlapping purposes that could confuse an agent.

    Naming Consistency5/5

    All 54 tools follow a consistent snake_case verb_noun pattern, such as get_user, list_plugins, trigger_dag, and pause_dag. Even longer names like get_dags_detailed_batch and list_task_instances_all maintain the same pattern, with no mixed naming conventions.

    Tool Count1/5

    With 54 tools, this is an extremely large tool set for a single server. The number far exceeds the typical well-scoped range of 3-15 tools, and while Airflow is complex, many tools are redundant or could be consolidated, making the surface overwhelming for an agent.

    Completeness3/5

    The tool set provides broad read coverage for most Airflow resources, including DAGs, task instances, connections, variables, and pools, plus core actions like triggering, pausing, and unpausing DAGs. However, there are significant gaps in mutation operations, such as creating or updating variables, pools, and users, and missing common task management like clearing or retrying task instances.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources