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MCP Server Airflow Token

by nikhil-ganage
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Python

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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

    C2.5/5.0

    Scored across 66 tools

    Disambiguation4/5

    Most tools have distinct purposes targeting specific resources and actions, such as clear_dag_run vs. delete_dag_run. However, some overlap exists, like get_dag_details and get_dag, which could cause confusion about which to use for DAG information, and get_tasks and get_dag_tasks appear redundant.

    Naming Consistency5/5

    Tool names follow a highly consistent verb_noun pattern throughout, using snake_case uniformly. Examples include create_connection, delete_dag, get_config, and update_variable, making them predictable and easy to parse.

    Tool Count2/5

    With 66 tools, the count is excessive for typical MCP server use, likely overwhelming for agents and indicating poor scoping. While Airflow is complex, this many tools suggests fragmentation rather than a streamlined interface.

    Completeness5/5

    The tool set provides comprehensive CRUD and lifecycle coverage for Airflow's domain, including DAGs, tasks, connections, variables, datasets, and more. No obvious gaps are present; it supports full management workflows from creation to deletion and monitoring.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

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