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MCP Server for Apache Airflow

by yangkyeongmo

delete_dag_dataset_queued_events

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

Instructions

Delete queued Dataset events for a DAG

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dag_idYes
beforeNo

Implementation Reference

  • The main handler function that executes the tool logic. It takes a dag_id and optional 'before' timestamp, calls the dataset_api to delete queued events, and returns the response.
    async def delete_dag_dataset_queued_events(
        dag_id: 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_dag_dataset_queued_events(dag_id=dag_id, **kwargs)
        return [types.TextContent(type="text", text=str(response.to_dict()))]
  • The registration tuple in get_all_functions() that registers the tool with its handler function, name, description, and indicates it is not read-only (mutates data).
    (
        delete_dag_dataset_queued_events,
        "delete_dag_dataset_queued_events",
        "Delete queued Dataset events for a DAG",
        False,
    ),
Behavior2/5

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

No annotations are provided, so the description carries full burden. It states 'Delete' which implies a destructive mutation, but doesn't disclose behavioral traits like permissions needed, whether deletion is reversible, rate limits, or what 'queued' means operationally. This is inadequate for a mutation tool with zero 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 with the core action and target, 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?

For a destructive mutation tool with 2 parameters (0% schema coverage), no annotations, and no output schema, the description is incomplete. It lacks details on behavior, parameters, error conditions, and doesn't compensate for the missing structured data, leaving significant gaps for an AI agent.

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 parameters are undocumented in the schema. The description mentions 'for a DAG', which hints at 'dag_id', but doesn't explain 'before' parameter or provide any semantic context for either parameter. It adds minimal value beyond the schema.

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 ('queued Dataset events for a DAG'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'delete_dag_dataset_queued_event' (singular) or 'delete_dataset_queued_events' (without DAG context), leaving some ambiguity about 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 multiple deletion-related siblings (e.g., 'delete_dag_dataset_queued_event', 'delete_dataset_queued_events'), the description lacks context about scope, prerequisites, or comparative use cases.

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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