airflow-mcp-server

An MCP server that lets Claude inspect and operate Apache Airflow over its REST API. It exposes safe, curated tools (read + a few guarded writes) rather than mirroring the whole API.

Targets Airflow 2 (stable REST API /api/v1). Airflow 3 is intentionally out of scope.

Runs as a local stdio server: each user runs it on their own machine with their own Airflow credentials, which keeps Airflow RBAC intact.

Status

Working read and write tools against Airflow 2; not yet published.

Read: get_airflow_version, get_airflow_health, list_pools, list_dags, get_dag, list_dag_runs, get_dag_run, list_task_instances, get_task_instance, get_task_logs, list_import_errors.

Write (refused when AIRFLOW_MCP_READ_ONLY=true): trigger_dag_run, set_dag_paused, clear_task_instances (supports dry_run to preview).

Related MCP server: MCP Server for Apache Airflow

Configuration

All settings come from AIRFLOW_MCP_* environment variables (prefixed to avoid clashing with Airflow's own env). See .env.example.

Use with Claude

Until published to PyPI, point your MCP client at the local checkout:

{
  "mcpServers": {
    "airflow": {
      "command": "uv",
      "args": ["run", "--directory", "C:\\path\\to\\airflow-mcp-server", "airflow-mcp-server"],
      "env": {
        "AIRFLOW_MCP_BASE_URL": "http://localhost:8080",
        "AIRFLOW_MCP_USERNAME": "airflow",
        "AIRFLOW_MCP_PASSWORD": "airflow"
      }
    }
  }
}

Once published, this simplifies to "command": "uvx", "args": ["airflow-mcp-server"].

Development

uv sync                # install deps
uv run pytest          # unit tests (mocked, no network)
uv run pytest -m e2e   # opt-in live test; needs a .env pointing at a real Airflow 2
uv run airflow-mcp-server   # run the server (expects an MCP client on stdio)

Roadmap

1. Foundation + connectivity (version, health). done

2. Read tools: DAGs, DAG runs, task instances, logs, import errors, pools. done

3. Safe writes: trigger DAG, pause/unpause, clear/retry tasks (gated by read-only). done

4. Packaging metadata and LICENSE. done

5. Publish to PyPI. pending

License

MIT - see LICENSE.