get_dataset_events
Retrieve dataset events from Apache Airflow to monitor data dependencies and track pipeline triggers using filtering and pagination options.
Instructions
Get dataset events
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| offset | No | ||
| order_by | No | ||
| dataset_id | No | ||
| source_dag_id | No | ||
| source_task_id | No | ||
| source_run_id | No | ||
| source_map_index | No |
Implementation Reference
- src/airflow/dataset.py:73-103 (handler)The handler function that implements the core logic of the 'get_dataset_events' tool by collecting optional parameters into kwargs and calling the Airflow DatasetApi.get_dataset_events method, then returning the response as TextContent.async def get_dataset_events( limit: Optional[int] = None, offset: Optional[int] = None, order_by: Optional[str] = None, dataset_id: Optional[int] = None, source_dag_id: Optional[str] = None, source_task_id: Optional[str] = None, source_run_id: Optional[str] = None, source_map_index: Optional[int] = 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 dataset_id is not None: kwargs["dataset_id"] = dataset_id if source_dag_id is not None: kwargs["source_dag_id"] = source_dag_id if source_task_id is not None: kwargs["source_task_id"] = source_task_id if source_run_id is not None: kwargs["source_run_id"] = source_run_id if source_map_index is not None: kwargs["source_map_index"] = source_map_index response = dataset_api.get_dataset_events(**kwargs) return [types.TextContent(type="text", text=str(response.to_dict()))]
- src/airflow/dataset.py:11-39 (registration)The get_all_functions() defines the registration tuple for 'get_dataset_events' among other dataset tools: (get_dataset_events, "get_dataset_events", "Get dataset events", True). This list is imported and used in main.py to register the tools with 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_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:87-92 (registration)The generic registration loop in main.py that calls app.add_tool for each function from get_dataset_functions(), including get_dataset_events, effectively registering it as an MCP tool.if read_only: functions = filter_functions_for_read_only(functions) for func, name, description, *_ in functions: app.add_tool(func, name=name, description=description)