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MCP Server for Apache Airflow

by yangkyeongmo

get_dataset_queued_events

Retrieve queued events for a specific dataset in Apache Airflow to monitor data pipeline dependencies and trigger status.

Instructions

Get queued Dataset events for a Dataset

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
uriYes

Implementation Reference

  • The async handler function for the 'get_dataset_queued_events' MCP tool. It accepts a 'uri' parameter, fetches queued events via the Airflow DatasetApi, converts the response to a dictionary string, and returns it wrapped in TextContent.
    async def get_dataset_queued_events(
        uri: str,
    ) -> List[Union[types.TextContent, types.ImageContent, types.EmbeddedResource]]:
        response = dataset_api.get_dataset_queued_events(uri=uri)
        return [types.TextContent(type="text", text=str(response.to_dict()))]
  • The get_all_functions() function returns a list of (callable, name, description, is_read_only) tuples used for registering MCP tools, including the entry for 'get_dataset_queued_events' on line 32.
    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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states 'Get queued Dataset events', which implies a read-only operation, but doesn't cover critical aspects like authentication needs, rate limits, pagination, response format, or error handling. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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

Conciseness4/5

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

The description is a single, efficient sentence with no wasted words, making it appropriately concise. However, it's front-loaded with basic information but lacks depth, which slightly limits its effectiveness despite the brevity.

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 (1 parameter with no schema description, no annotations, no output schema), the description is incomplete. It doesn't explain what 'queued' means, how results are returned, or provide any operational context, making it inadequate for a tool that likely interacts with event systems in a dataset context.

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 1 parameter with 0% description coverage, and the tool description doesn't explain the 'uri' parameter at all. No details are provided on what the URI represents, its format, or examples. Since schema coverage is low, the description fails to compensate, leaving the parameter's meaning unclear.

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 Dataset' clearly states the action (get) and resource (queued Dataset events), but it's vague about scope and doesn't distinguish from siblings like 'get_dataset_events' or 'get_dag_dataset_queued_events'. It specifies the target is 'for a Dataset', which provides some context but lacks detail on what 'queued' means operationally.

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 such as 'get_dataset_events' or 'get_dag_dataset_queued_events'. The description implies it's for retrieving queued events, but it doesn't specify prerequisites, exclusions, or comparison to sibling tools, leaving usage context unclear.

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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