fetch_dags
Retrieve and list all Directed Acyclic Graphs (DAGs) from Apache Airflow with filtering options for tags, status, and patterns to manage workflow orchestration.
Instructions
Fetch all DAGs
Input 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 `get_dags` that implements the core logic of the `fetch_dags` tool: accepts optional query parameters, calls Airflow's DAGApi.get_dags, enhances the response with UI URLs, and returns it 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)Module-level `get_all_functions` that registers the `fetch_dags` tool by including its tuple (get_dags, "fetch_dags", "Fetch all DAGs", True). This is imported and used in src/main.py to add the tool to the MCP server.
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:90-92 (registration)Global tool registration loop in main.py that calls `app.add_tool` for each function from imported module get_all_functions, including `fetch_dags` from dag.py (imported at line 7).
for func, name, description, *_ in functions: app.add_tool(func, name=name, description=description) - src/airflow/dag.py:36-38 (helper)Helper function `get_dag_url` used by `get_dags` to add UI links to each DAG in the response.
def get_dag_url(dag_id: str) -> str: return f"{AIRFLOW_HOST}/dags/{dag_id}/grid"