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MCP Server for Apache Airflow

by yangkyeongmo

delete_dataset_queued_events

Remove queued Dataset events in Apache Airflow to manage event backlog and maintain system performance.

Instructions

Delete queued Dataset events for a Dataset

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
uriYes
beforeNo

Implementation Reference

  • The main handler function for the 'delete_dataset_queued_events' tool. It accepts a dataset URI and optional 'before' timestamp, constructs kwargs, calls the underlying DatasetApi to delete queued events, and returns the response as text content.
    async def delete_dataset_queued_events(
        uri: str,
        before: Optional[str] = None,
    ) -> List[Union[types.TextContent, types.ImageContent, types.EmbeddedResource]]:
        kwargs: Dict[str, Any] = {}
        if before is not None:
            kwargs["before"] = before
    
        response = dataset_api.delete_dataset_queued_events(uri=uri, **kwargs)
        return [types.TextContent(type="text", text=str(response.to_dict()))]
  • Registration of the 'delete_dataset_queued_events' tool within the get_all_functions() list, including the function reference, name, description, and mutability flag.
        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 of behavioral disclosure. While 'Delete' implies a destructive mutation, the description doesn't specify whether this action is reversible, what permissions are required, or what happens if the dataset or events don't exist. It also doesn't describe the return value or error conditions, leaving the agent with incomplete operational context.

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 with the core action and resource, making it easy to scan. Every word contributes directly to stating the tool's purpose, achieving optimal conciseness.

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 the complexity of a destructive operation with two parameters, no annotations, and no output schema, the description is inadequate. It lacks details on parameter usage, behavioral traits like idempotency or error handling, and expected outcomes. For a tool that modifies system state, this leaves too many unknowns for reliable agent invocation.

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?

The input schema has 0% description coverage, with two parameters (uri and before) undocumented. The description mentions 'for a Dataset', which hints that 'uri' might refer to a dataset identifier, but provides no format or examples. It doesn't explain 'before' at all, leaving its purpose (e.g., timestamp filter) ambiguous. This fails to compensate for the low schema coverage.

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 resource ('queued Dataset events for a Dataset'), making the purpose understandable. However, it doesn't distinguish this tool from its sibling 'delete_dag_dataset_queued_event' or 'delete_dag_dataset_queued_events', which appear to handle similar operations but for DAGs rather than Datasets specifically.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, such as needing a valid dataset URI, or clarify whether this deletes all queued events or only those before a certain time. With many sibling tools for dataset and DAG operations, the lack of differentiation is a significant gap.

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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