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MCP Server for Apache Airflow

by yangkyeongmo

get_dataset_events

Retrieve dataset events from Apache Airflow to monitor data dependencies and track dataset updates for workflow orchestration.

Instructions

Get dataset events

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
offsetNo
order_byNo
dataset_idNo
source_dag_idNo
source_task_idNo
source_run_idNo
source_map_indexNo

Implementation Reference

  • The main handler function for the 'get_dataset_events' tool. It accepts optional parameters, builds a kwargs dict, calls dataset_api.get_dataset_events, and returns 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()))]
  • The get_all_functions() returns a list of tuples for tool registration, including the one for 'get_dataset_events' at line 16.
    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,
            ),
        ]
Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. The description 'Get dataset events' implies a read operation but reveals nothing about permissions required, rate limits, pagination behavior (despite limit/offset parameters), whether it returns historical or real-time data, or what format the events are in. This is a complete lack of behavioral transparency for an 8-parameter tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

While technically concise with only three words, this is under-specification rather than effective conciseness. The description doesn't earn its place by providing necessary information. It's front-loaded only in the trivial sense that there's nothing to load beyond the initial phrase.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an 8-parameter tool with no annotations, 0% schema description coverage, and no output schema, the description is completely inadequate. It doesn't explain what dataset events are, how they're structured, what filtering options exist, or what the tool returns. The agent would struggle to use this tool correctly without significant trial and error.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage and 8 parameters, the description provides zero information about any parameters. It doesn't mention dataset_id, source filtering parameters (dag_id, task_id, run_id, map_index), or pagination controls (limit, offset, order_by). The description fails completely to compensate for the schema's lack of documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Get dataset events' is a tautology that restates the tool name without adding meaningful clarification. It specifies the verb 'get' and resource 'dataset events' but provides no additional context about what dataset events are, their format, or scope. This is slightly better than a pure tautology but remains vague and minimally informative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides absolutely no guidance on when to use this tool versus alternatives. There are multiple sibling tools related to datasets (e.g., get_dataset, get_datasets, get_dataset_queued_events, get_upstream_dataset_events) but no indication of how this tool differs or when it should be selected. The agent receives no usage context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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