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Apache Airflow MCP Server

by madamak
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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

    A4/5.0

    Scored across 16 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose, targeting specific Airflow entities (DAGs, runs, tasks, instances, datasets) and actions (list, get, clear, trigger, pause/unpause, describe, resolve). No two tools perform the same operation on the same entity, and descriptions make boundaries clear.

    Naming Consistency5/5

    All tools follow a consistent 'airflow_verb_noun' pattern (e.g., airflow_list_dags, airflow_trigger_dag). No mixing of conventions or unusual naming styles, making it easy for agents to predict tool names.

    Tool Count4/5

    With 16 tools, the count is slightly above the high end of 'well-scoped' (3-15), but it is reasonable given the complexity of Apache Airflow. The tools cover a comprehensive set of operations without feeling bloated or redundant.

    Completeness4/5

    The tool surface covers all core Airflow interactions: listing, getting, clearing, triggering, pausing/unpausing DAGs, runs, and task instances, plus logs, dataset events, and instance management. Minor gaps exist (e.g., no DAG update or deletion), but those are less common operations and the set still enables effective agent workflows.

    Maintenance

    ActivityStale
    ResponsivenessNo issues

    Resources