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astro-airflow-mcp

Official
by astronomer

trigger_dag

Trigger a new DAG run to manually execute a workflow in Apache Airflow. Optionally pass configuration parameters that become available to the DAG during execution.

Instructions

Trigger a new DAG run (start a workflow execution manually).

Use this tool when the user asks to:

  • "Run DAG X" or "Start DAG Y" or "Execute DAG Z"

  • "Trigger a run of DAG X" or "Kick off DAG Y"

  • "Run this workflow" or "Start this pipeline"

  • "Execute DAG X with config Y" or "Trigger DAG with parameters"

  • "Start a manual run" or "Manually execute this DAG"

This creates a new DAG run that will be picked up by the scheduler and executed. You can optionally pass configuration parameters that will be available to the DAG during execution via the conf context variable.

IMPORTANT: This is a write operation that modifies Airflow state by creating a new DAG run. Use with caution.

Returns information about the newly triggered DAG run including:

  • dag_run_id: Unique identifier for the new execution

  • dag_id: Which DAG was triggered

  • state: Initial state (typically 'queued')

  • execution_date: When this run is scheduled to execute

  • start_date: When execution started (may be null if queued)

  • run_type: Type of run (will be 'manual')

  • conf: Configuration passed to the run

  • external_trigger: Set to true for manual triggers

Args: dag_id: The ID of the DAG to trigger (e.g., "example_dag") conf: Optional configuration dictionary to pass to the DAG run. This will be available in the DAG via context['dag_run'].conf

Returns: JSON with details about the newly triggered DAG run

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dag_idYes
confNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations provided, so the description carries full burden. It explicitly states this is a write operation that modifies Airflow state by creating a new DAG run, and lists return fields. It does not detail permissions or side effects beyond creation, but is transparent enough for basic use.

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 well-structured with a clear action statement, example queries, notes, and return field list. It is slightly verbose but every section adds value. Front-loaded with core purpose.

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

Completeness5/5

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

The description is complete: it explains purpose, usage context, parameter meaning, return value fields, and behavioral notes. Despite the output schema existing but not provided here, the description's own return field list is thorough. For a moderately complex tool with 2 parameters and a clear action, this description fully covers the agent's needs.

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

Parameters4/5

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

Schema coverage is 0%, but the description compensates by explaining both parameters: dag_id as the DAG ID string and conf as an optional config dictionary with context explanation. This adds meaning beyond the raw 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 it triggers a new DAG run and provides many example user queries. However, it does not differentiate from the sibling tool trigger_dag_and_wait, which slightly reduces clarity.

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

Usage Guidelines4/5

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

The description lists explicit when-to-use scenarios with example user requests. It could be improved by noting when not to use this tool (e.g., if waiting for completion is desired, use trigger_dag_and_wait instead).

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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