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MCP-Airflow-API

MCP-Airflow-API

by call518
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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

    C2.7/5.0

    Scored across 53 tools

    Disambiguation3/5

    Most tools map to a distinct resource+action, but several clusters overlap: list_task_instances_all vs list_task_instances_batch vs get_task_instance_details, list_event_logs/get_event_log/all_dag_event_summary, and list_datasets/get_dataset_events/list_dataset_events (the last two read as near-duplicates). list_dags vs get_dags_detailed_batch vs get_dag is also a subtle distinction an agent could easily misfire on.

    Naming Consistency3/5

    The dominant pattern is snake_case verb_noun (list_pools, get_pool, create_connection), which is good. However, a sizeable minority break the verb-first rule: running_dags, failed_dags, dag_graph, dag_code, dag_calendar, all_dag_event_summary, all_dag_import_summary. The mixed conventions are still readable but not predictable.

    Tool Count2/5

    53 tools is far past the comfortable range for an agent to select from reliably, and Airflow's breadth doesn't fully excuse it. Many families (config, event logs, import errors, dataset events) could be collapsed into fewer parameterized tools, e.g. one list_task_instances with filters instead of two plus a batch variant.

    Completeness2/5

    Coverage is lopsided: connections get full CRUD, but variables, pools, users, roles, and datasets are read-only, and there is no way to clear/delete a DAG run, mark a task instance success/failed, or create/delete a DAG. Common operational recovery workflows (clear and rerun, set variable) are dead ends for an agent.

    Maintenance

    ActivityActive
    ResponsivenessLimited data

    Resources