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MCP Server Airflow Token

create_dataset_event

Generate dataset events in Apache Airflow to trigger DAGs based on data availability, enabling automated workflow execution when datasets are ready.

Instructions

Create dataset event

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataset_uriYes
extraNo

Implementation Reference

  • The async handler function implementing the create_dataset_event tool. It constructs an event request from dataset_uri and optional extra, calls the Airflow DatasetApi to create the event, and returns the response as text content.
    async def create_dataset_event(
        dataset_uri: str,
        extra: Optional[Dict[str, Any]] = None,
    ) -> List[Union[types.TextContent, types.ImageContent, types.EmbeddedResource]]:
        event_request = {
            "dataset_uri": dataset_uri,
        }
        if extra is not None:
            event_request["extra"] = extra
    
        response = dataset_api.create_dataset_event(create_dataset_event=event_request)
        return [types.TextContent(type="text", text=str(response.to_dict()))]
  • The registration tuple in get_all_functions() that specifies the create_dataset_event function, its name, description, and that it is not read-only. This list is imported and used in src/main.py to register the tool with the MCP server.
    (create_dataset_event, "create_dataset_event", "Create dataset event", False),
Behavior1/5

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. 'Create dataset event' implies a write/mutation operation but reveals nothing about permissions required, whether the operation is idempotent, what happens on failure, rate limits, or what constitutes a successful creation. This leaves critical behavioral aspects completely undocumented.

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 maximally concise at just three words. While this represents severe under-specification rather than ideal conciseness, from a pure structural perspective there is zero wasted verbiage. Every word directly relates to the tool's purpose, though that purpose is inadequately explained.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with 2 parameters (one required), 0% schema description coverage, no annotations, and no output schema, the description is completely inadequate. It provides no information about what the tool actually does, how to use it properly, what the parameters mean, or what to expect as a result. The agent would be operating almost blindly when attempting to use this tool.

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

Parameters1/5

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

Schema description coverage is 0%, meaning neither parameter ('dataset_uri' or 'extra') has any documentation in the schema. The description provides zero information about these parameters - no explanation of what 'dataset_uri' should contain, what format it expects, what 'extra' data might be appropriate, or how these parameters affect the creation operation. The description fails completely to compensate for the schema's lack of documentation.

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

Purpose2/5

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

The description 'Create dataset event' is essentially a tautology that restates the tool name. It provides no additional specificity about what 'dataset event' means, what type of event is being created, or what this operation accomplishes. While it includes a verb ('Create') and resource ('dataset event'), it lacks meaningful differentiation from sibling tools like 'get_dataset_events' or 'delete_dataset_queued_event'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides absolutely no guidance on when to use this tool versus alternatives. There are no contextual cues, prerequisites, or exclusions mentioned. Given the sibling tools include operations like 'get_dataset_events' and 'delete_dataset_queued_event', the agent has no information about when creation is appropriate versus retrieval or deletion.

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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