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nikhil-ganage

MCP Server Airflow Token

get_dag_dataset_queued_events

Retrieve queued Dataset events for a specific DAG in Apache Airflow to monitor data dependencies and trigger conditions.

Instructions

Get queued Dataset events for a DAG

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dag_idYes

Implementation Reference

  • The asynchronous handler function that executes the tool 'get_dag_dataset_queued_events'. It calls the Airflow DatasetApi with the provided dag_id and returns the queued events as a text content response.
    async def get_dag_dataset_queued_events(
        dag_id: str,
    ) -> List[Union[types.TextContent, types.ImageContent, types.EmbeddedResource]]:
        response = dataset_api.get_dag_dataset_queued_events(dag_id=dag_id)
        return [types.TextContent(type="text", text=str(response.to_dict()))]
  • The registration of the tool via the get_all_functions() which returns a list of tuples including (get_dag_dataset_queued_events, "get_dag_dataset_queued_events", "Get queued Dataset events for a DAG", True). This is likely used to register the tool 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,
            ),
        ]
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It states 'Get queued Dataset events for a DAG', implying a read-only operation, but doesn't disclose behavioral traits such as permissions required, rate limits, pagination, or what 'queued' means in practice. This leaves significant gaps for a tool with no annotation coverage.

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

Conciseness5/5

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

The description is a single, efficient sentence with no wasted words. It's front-loaded and appropriately sized for the tool's purpose, making it easy to parse quickly.

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

Completeness2/5

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

Given no annotations, no output schema, and low schema description coverage (0%), the description is incomplete. It lacks details on behavior, return values, and parameter semantics, making it inadequate for a tool that likely returns event data. More context is needed to fully understand its use.

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

Parameters3/5

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

The input schema has 1 parameter (dag_id) with 0% description coverage. The description doesn't add any meaning beyond the schema, as it doesn't explain what 'dag_id' represents or its format. With low schema coverage, the description fails to compensate, but the single parameter is straightforward, so a baseline 3 is appropriate given minimal complexity.

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

Purpose3/5

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

The description 'Get queued Dataset events for a DAG' clearly states the action (get) and resource (queued Dataset events for a DAG), but it's somewhat vague about what 'queued Dataset events' specifically entails. It doesn't differentiate from sibling tools like 'get_dag_dataset_queued_event' (singular) or 'get_dataset_queued_events' (general), leaving ambiguity in scope.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives. With siblings like 'get_dag_dataset_queued_event' (singular) and 'get_dataset_queued_events' (general), the description lacks explicit context or exclusions, offering only basic usage without distinguishing from related tools.

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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