get_datasets
Retrieve and filter datasets from Apache Airflow by specifying parameters like limit, offset, order, URI pattern, or DAG IDs for streamlined data management.
Instructions
List datasets
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| dag_ids | No | ||
| limit | No | ||
| offset | No | ||
| order_by | No | ||
| uri_pattern | No |
Implementation Reference
- src/airflow/dataset.py:42-64 (handler)The main asynchronous handler function for the 'get_datasets' MCP tool. It constructs a kwargs dictionary from optional input parameters and calls the Airflow DatasetApi.get_datasets method, returning the result as a TextContent object.async def get_datasets( limit: Optional[int] = None, offset: Optional[int] = None, order_by: Optional[str] = None, uri_pattern: Optional[str] = None, dag_ids: 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 uri_pattern is not None: kwargs["uri_pattern"] = uri_pattern if dag_ids is not None: kwargs["dag_ids"] = dag_ids response = dataset_api.get_datasets(**kwargs) return [types.TextContent(type="text", text=str(response.to_dict()))]
- src/airflow/dataset.py:11-39 (registration)get_all_functions() provides the registration tuple for 'get_datasets' (line 14): (get_datasets, "get_datasets", "List datasets", True), along with other dataset tools. This function is imported in main.py and invoked to obtain the list of tools to register.def get_all_functions() -> list[tuple[Callable, str, str, bool]]: """Return list of (function, name, description, is_read_only) tuples for registration.""" return [ (get_datasets, "get_datasets", "List datasets", True), (get_dataset, "get_dataset", "Get a dataset by URI", True), (get_dataset_events, "get_dataset_events", "Get dataset events", True), (create_dataset_event, "create_dataset_event", "Create dataset event", False), (get_dag_dataset_queued_event, "get_dag_dataset_queued_event", "Get a queued Dataset event for a DAG", True), (get_dag_dataset_queued_events, "get_dag_dataset_queued_events", "Get queued Dataset events for a DAG", True), ( delete_dag_dataset_queued_event, "delete_dag_dataset_queued_event", "Delete a queued Dataset event for a DAG", False, ), ( delete_dag_dataset_queued_events, "delete_dag_dataset_queued_events", "Delete queued Dataset events for a DAG", False, ), (get_dataset_queued_events, "get_dataset_queued_events", "Get queued Dataset events for a Dataset", True), ( delete_dataset_queued_events, "delete_dataset_queued_events", "Delete queued Dataset events for a Dataset", False, ), ]
- src/main.py:95-96 (registration)Generic registration loop in main.py that iterates over functions from get_dataset_functions() (aliased from dataset.py's get_all_functions), creating and adding MCP Tool instances via app.add_tool for each, including 'get_datasets'.for func, name, description, *_ in functions: app.add_tool(Tool.from_function(func, name=name, description=description))