fetch_dags
Retrieve Apache Airflow DAGs with filtering options for tags, status, and patterns to manage and monitor workflow automation.
Instructions
Fetch all DAGs
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| offset | No | ||
| order_by | No | ||
| tags | No | ||
| only_active | No | ||
| paused | No | ||
| dag_id_pattern | No |
Implementation Reference
- src/airflow/dag.py:40-77 (handler)Handler function that implements the core logic of the 'fetch_dags' tool. Fetches DAGs from Airflow API with optional parameters, enhances with UI URLs, and returns as MCP TextContent.async def get_dags( limit: Optional[int] = None, offset: Optional[int] = None, order_by: Optional[str] = None, tags: Optional[List[str]] = None, only_active: Optional[bool] = None, paused: Optional[bool] = None, dag_id_pattern: Optional[str] = None, ) -> List[Union[types.TextContent, types.ImageContent, types.EmbeddedResource]]: # Build parameters dictionary kwargs: Dict[str, Any] = {} if limit is not None: kwargs["limit"] = limit if offset is not None: kwargs["offset"] = offset if order_by is not None: kwargs["order_by"] = order_by if tags is not None: kwargs["tags"] = tags if only_active is not None: kwargs["only_active"] = only_active if paused is not None: kwargs["paused"] = paused if dag_id_pattern is not None: kwargs["dag_id_pattern"] = dag_id_pattern # Use the client to fetch DAGs response = dag_api.get_dags(**kwargs) # Convert response to dictionary for easier manipulation response_dict = response.to_dict() # Add UI links to each DAG for dag in response_dict.get("dags", []): dag["ui_url"] = get_dag_url(dag["dag_id"]) return [types.TextContent(type="text", text=str(response_dict))]
- src/airflow/dag.py:15-33 (registration)Defines the list of tool functions for DAG API, including the registration tuple for 'fetch_dags' which associates the get_dags handler with the tool name, description, and read-only flag.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:92-97 (registration)The code in main.py that iterates over the functions from get_dag_functions() (among others) and registers each tool with the MCP app using app.add_tool.if read_only: functions = filter_functions_for_read_only(functions) for func, name, description, *_ in functions: app.add_tool(Tool.from_function(func, name=name, description=description))
- src/airflow/dag.py:36-38 (helper)Helper function used by get_dags to generate UI URLs for each DAG.def get_dag_url(dag_id: str) -> str: return f"{AIRFLOW_HOST}/dags/{dag_id}/grid"
- src/main.py:8-9 (registration)Import of get_all_functions from dag.py, aliased as get_dag_functions, which is used to load the tool registrations.from src.airflow.dag import get_all_functions as get_dag_functions from src.airflow.dagrun import get_all_functions as get_dagrun_functions