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MCP Server Airflow Token

fetch_dags

Retrieve all DAGs from an Apache Airflow deployment with filtering options for tags, status, and ID patterns to manage workflow orchestration.

Instructions

Fetch all DAGs

Input Schema

NameRequiredDescriptionDefault
dag_id_patternNo
limitNo
offsetNo
only_activeNo
order_byNo
pausedNo
tagsNo

Input Schema (JSON Schema)

{ "properties": { "dag_id_pattern": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "title": "Dag Id Pattern" }, "limit": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "default": null, "title": "Limit" }, "offset": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "default": null, "title": "Offset" }, "only_active": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "default": null, "title": "Only Active" }, "order_by": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "title": "Order By" }, "paused": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "default": null, "title": "Paused" }, "tags": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "default": null, "title": "Tags" } }, "type": "object" }

Implementation Reference

  • The main handler function for the 'fetch_dags' tool. It accepts optional parameters for filtering DAGs, calls the Airflow DAG API, enhances the response with UI URLs, and returns it as text content.
    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))]
  • The registration function that includes the 'fetch_dags' tool, mapping the get_dags handler to the tool name with 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), ]
  • 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"

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