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airflow-dev-mcp

by BrianLondon
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Python
Remote

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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

    A4.6/5.0

    Scored across 10 tools

    Disambiguation5/5

    Each tool targets a distinct resource or action with no overlap: clearing tasks, listing import errors, checking run status, fetching logs, listing connections/dag runs/DAGs/variables, toggling pause, and triggering runs. An agent can easily distinguish between them.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with lowercase underscores (e.g., clear_task_instances, get_import_errors, list_dags). Although verbs vary (clear, get, list, set, trigger), the pattern is uniform and predictable.

    Tool Count5/5

    10 tools is well-scoped for an Airflow dev MCP server. It covers essential operations without being bloated or insufficient, providing a focused set for common development tasks.

    Completeness4/5

    The tool surface covers the main workflows: triggering, monitoring, logging, pausing, and basic asset listing. A minor gap is the lack of a dedicated get_dag endpoint, but list_dags with filtering partially compensates. No CRUD on connections/variables is acceptable for a read-only dev environment.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues

    Resources