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nikhil-ganage

MCP Server Airflow Token

get_upstream_dataset_events

Retrieve upstream dataset events for Apache Airflow DAG runs to monitor data dependencies and pipeline triggers within Astronomer Cloud deployments.

Instructions

Get dataset events for a DAG run

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dag_idYes
dag_run_idYes

Implementation Reference

  • The async handler function that executes the tool logic by calling the Airflow DAGRunApi's get_upstream_dataset_events method.
    async def get_upstream_dataset_events(
        dag_id: str, dag_run_id: str
    ) -> List[Union[types.TextContent, types.ImageContent, types.EmbeddedResource]]:
        response = dag_run_api.get_upstream_dataset_events(dag_id=dag_id, dag_run_id=dag_run_id)
        return [types.TextContent(type="text", text=str(response.to_dict()))]
  • The get_all_functions() returns the registration tuple for the get_upstream_dataset_events tool.
    def get_all_functions() -> list[tuple[Callable, str, str, bool]]:
        """Return list of (function, name, description, is_read_only) tuples for registration."""
        return [
            (post_dag_run, "post_dag_run", "Trigger a DAG by ID", False),
            (get_dag_runs, "get_dag_runs", "Get DAG runs by ID", True),
            (get_dag_runs_batch, "get_dag_runs_batch", "List DAG runs (batch)", True),
            (get_dag_run, "get_dag_run", "Get a DAG run by DAG ID and DAG run ID", True),
            (update_dag_run_state, "update_dag_run_state", "Update a DAG run state by DAG ID and DAG run ID", False),
            (delete_dag_run, "delete_dag_run", "Delete a DAG run by DAG ID and DAG run ID", False),
            (clear_dag_run, "clear_dag_run", "Clear a DAG run", False),
            (set_dag_run_note, "set_dag_run_note", "Update the DagRun note", False),
            (get_upstream_dataset_events, "get_upstream_dataset_events", "Get dataset events for a DAG run", True),
        ]
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 'Get' which implies a read operation, but doesn't disclose any behavioral traits like whether it's safe, what permissions are needed, if it's paginated, or what format the events are returned in. For a tool with no annotation coverage, this is insufficient.

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 that gets straight to the point without unnecessary words. It's appropriately sized for a simple tool, though it could benefit from additional context given the lack of annotations and schema descriptions.

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 description coverage, and no output schema, the description is incomplete. It doesn't explain what 'upstream dataset events' are, how they differ from other event types, what the return format is, or any behavioral constraints. For a tool in this context, more information is needed.

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 the schema provides no parameter descriptions. The tool description doesn't mention parameters at all, failing to explain what 'dag_id' and 'dag_run_id' represent or how to obtain them. With 2 required parameters and no schema descriptions, the description adds no semantic value.

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 dataset events for a DAG run' clearly states the action (get) and resource (dataset events) with a specific scope (for a DAG run). However, it doesn't differentiate from sibling tools like 'get_dataset_events' or 'get_dag_dataset_queued_events', leaving ambiguity about what makes this tool distinct.

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. With siblings like 'get_dataset_events' and 'get_dag_dataset_queued_events', there's no indication of what 'upstream' means or when this specific tool is appropriate, leaving the agent to guess.

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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