patch_dags
Update multiple DAGs in Apache Airflow by modifying pause status, tags, or filtering by DAG ID pattern to manage workflow configurations.
Instructions
Update multiple DAGs
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| dag_id_pattern | No | ||
| is_paused | No | ||
| tags | No |
Implementation Reference
- src/airflow/dag.py:142-164 (handler)The async handler function implementing the 'patch_dags' tool logic. It builds an update request for DAG properties (is_paused, tags) and calls the Airflow API to patch all matching DAGs based on dag_id_pattern.async def patch_dags( dag_id_pattern: Optional[str] = None, is_paused: Optional[bool] = None, tags: Optional[List[str]] = None, ) -> List[Union[types.TextContent, types.ImageContent, types.EmbeddedResource]]: update_request = {} update_mask = [] if is_paused is not None: update_request["is_paused"] = is_paused update_mask.append("is_paused") if tags is not None: update_request["tags"] = tags update_mask.append("tags") dag = DAG(**update_request) kwargs = {} if dag_id_pattern is not None: kwargs["dag_id_pattern"] = dag_id_pattern response = dag_api.patch_dags(dag_id_pattern=dag_id_pattern, dag=dag, update_mask=update_mask, **kwargs) return [types.TextContent(type="text", text=str(response.to_dict()))]
- src/airflow/dag.py:15-33 (registration)The get_all_functions() that includes the registration tuple for 'patch_dags': (patch_dags, "patch_dags", "Update multiple DAGs", False). This is imported and used in main.py to register the tool.def get_all_functions() -> list[tuple[Callable, str, str, bool]]: """Return list of (function, name, description, is_read_only) tuples for registration.""" return [ (get_dags, "fetch_dags", "Fetch all DAGs", True), (get_dag, "get_dag", "Get a DAG by ID", True), (get_dag_details, "get_dag_details", "Get a simplified representation of DAG", True), (get_dag_source, "get_dag_source", "Get a source code", True), (pause_dag, "pause_dag", "Pause a DAG by ID", False), (unpause_dag, "unpause_dag", "Unpause a DAG by ID", False), (get_dag_tasks, "get_dag_tasks", "Get tasks for DAG", True), (get_task, "get_task", "Get a task by ID", True), (get_tasks, "get_tasks", "Get tasks for DAG", True), (patch_dag, "patch_dag", "Update a DAG", False), (patch_dags, "patch_dags", "Update multiple DAGs", False), (delete_dag, "delete_dag", "Delete a DAG", False), (clear_task_instances, "clear_task_instances", "Clear a set of task instances", False), (set_task_instances_state, "set_task_instances_state", "Set a state of task instances", False), (reparse_dag_file, "reparse_dag_file", "Request re-parsing of a DAG file", False), ]
- src/main.py:78-98 (registration)The main registration loop in main.py that calls get_dag_functions() (which includes patch_dags) and registers each tool with app.add_tool(func, name, description).for api in apis: logging.debug(f"Adding API: {api}") get_function = APITYPE_TO_FUNCTIONS[APIType(api)] try: functions = get_function() except NotImplementedError: continue # Filter functions for read-only mode if requested if read_only: functions = filter_functions_for_read_only(functions) for func, name, description, *_ in functions: app.add_tool(func, name=name, description=description) if transport == "sse": logging.debug("Starting MCP server for Apache Airflow with SSE transport") app.run(transport="sse") else: logging.debug("Starting MCP server for Apache Airflow with stdio transport") app.run(transport="stdio")
- src/main.py:7-7 (registration)Import of get_all_functions from dag.py, aliased as get_dag_functions for use in tool registration.from src.airflow.dag import get_all_functions as get_dag_functions