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MCP Server Airflow Token

get_dataset_queued_events

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

Instructions

Get queued Dataset events for a Dataset

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
uriYes

Implementation Reference

  • Handler function implementing the core logic of the 'get_dataset_queued_events' tool. It retrieves queued events for the given dataset URI using the DatasetApi and returns the result as MCP-formatted 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()))]
  • Module-level registration function listing all dataset-related tools, including the registration tuple for 'get_dataset_queued_events'.
    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,
            ),
        ]
  • src/main.py:87-92 (registration)
    MCP server registration loop that adds tools (including get_dataset_queued_events from dataset functions) to the app using app.add_tool().
    if read_only:
        functions = filter_functions_for_read_only(functions)
    
    for func, name, description, *_ in functions:
        app.add_tool(func, name=name, description=description)
  • Initialization of the DatasetApi client instance used by the get_dataset_queued_events handler.
    dataset_api = DatasetApi(api_client)
Behavior2/5

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

With no annotations provided, the description carries full burden but only states the action without behavioral details. It doesn't disclose whether this is a read-only operation, what permissions are needed, how results are returned (e.g., pagination), or any rate limits. This leaves significant gaps for a tool that likely interacts with event queues.

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. It's front-loaded with the core action, though it could be more informative without sacrificing brevity. The structure is clear but minimal.

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, 0% schema coverage, no output schema, and a parameter-heavy sibling list, the description is incomplete. It doesn't address key aspects like return format, error conditions, or how 'queued' differs from other event types, making it inadequate for safe and effective use by an 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%, and the description adds no parameter information beyond implying a Dataset is involved. It doesn't explain what 'uri' represents, its format, or how it relates to queued events. With one undocumented parameter, the description fails to compensate for the schema's lack of details.

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 verb ('Get') and resource ('queued Dataset events'), but it's vague about what 'queued' means and doesn't differentiate from siblings like 'get_dataset_events' or 'get_dag_dataset_queued_events'. It provides basic purpose but lacks specificity.

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. The description doesn't mention prerequisites, context, or exclusions, leaving the agent with no hints about appropriate usage scenarios compared to related tools in the list.

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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