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MCP Server Airflow Token

delete_dag_dataset_queued_event

Remove a queued Dataset event from a DAG to manage Airflow data dependencies and prevent unnecessary task triggers.

Instructions

Delete a queued Dataset event for a DAG

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dag_idYes
uriYes

Implementation Reference

  • The handler function implementing the tool logic. It calls the underlying dataset_api to delete the queued event and returns the response as text.
    async def delete_dag_dataset_queued_event(
        dag_id: str,
        uri: str,
    ) -> List[Union[types.TextContent, types.ImageContent, types.EmbeddedResource]]:
        response = dataset_api.delete_dag_dataset_queued_event(dag_id=dag_id, uri=uri)
        return [types.TextContent(type="text", text=str(response.to_dict()))]
  • Registration of the tool in the get_all_functions list, including the function reference, name, description, and read-only flag.
        delete_dag_dataset_queued_event,
        "delete_dag_dataset_queued_event",
        "Delete a queued Dataset event for a DAG",
        False,
    ),
Behavior2/5

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

With no annotations, the description carries full burden but only states the action without behavioral details. It does not disclose if deletion is permanent, requires specific permissions, has side effects (e.g., on DAG runs), or error conditions. 'Delete' implies mutation, but critical context like safety or impact is missing, leaving significant gaps.

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, direct sentence with zero wasted words. It is front-loaded with the core action and target, making it efficient and easy to parse. No unnecessary details or redundancy are present.

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?

For a deletion tool with no annotations, 2 parameters (0% schema coverage), and no output schema, the description is incomplete. It lacks parameter explanations, behavioral context (e.g., idempotency, errors), and output expectations. The agent cannot reliably use this tool without guessing at critical details.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate but adds no parameter information. It does not explain what 'dag_id' or 'uri' represent, their formats, or how they identify the event. With 2 undocumented parameters, the description fails to provide meaningful semantics beyond the schema's basic structure.

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

Purpose4/5

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

The description clearly states the action ('Delete') and target ('a queued Dataset event for a DAG'), making the purpose evident. It distinguishes from siblings like 'delete_dag_dataset_queued_events' (plural) and 'delete_dataset_queued_events' (no DAG context), though not explicitly. However, it lacks specificity about what 'queued' means or the event's nature, preventing a perfect score.

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. It does not mention prerequisites (e.g., event must exist), exclusions, or compare to siblings like 'delete_dag_dataset_queued_events' for bulk operations. The agent must infer usage from the name alone, which is insufficient.

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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