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MCP Server for Apache Airflow

by yangkyeongmo

unpause_dag

Resume a paused Apache Airflow DAG to restart its scheduled tasks and workflows using the DAG ID.

Instructions

Unpause a DAG by ID

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dag_idYes

Implementation Reference

  • The handler function that implements the unpause_dag tool. It creates a DAG object with is_paused=False and patches the DAG using the Airflow API.
    async def unpause_dag(dag_id: str) -> List[Union[types.TextContent, types.ImageContent, types.EmbeddedResource]]:
        dag = DAG(is_paused=False)
        response = dag_api.patch_dag(dag_id=dag_id, dag=dag, update_mask=["is_paused"])
        return [types.TextContent(type="text", text=str(response.to_dict()))]
  • The get_all_functions function registers the unpause_dag tool (and others) by returning a tuple (unpause_dag, "unpause_dag", "Unpause a DAG by ID", False).
    def get_all_functions() -> list[tuple[Callable, str, str, bool]]:
        """Return list of (function, name, description, is_read_only) tuples for registration."""
        return [
            (get_dags, "fetch_dags", "Fetch all DAGs", True),
            (get_dag, "get_dag", "Get a DAG by ID", True),
            (get_dag_details, "get_dag_details", "Get a simplified representation of DAG", True),
            (get_dag_source, "get_dag_source", "Get a source code", True),
            (pause_dag, "pause_dag", "Pause a DAG by ID", False),
            (unpause_dag, "unpause_dag", "Unpause a DAG by ID", False),
            (get_dag_tasks, "get_dag_tasks", "Get tasks for DAG", True),
            (get_task, "get_task", "Get a task by ID", True),
            (get_tasks, "get_tasks", "Get tasks for DAG", True),
            (patch_dag, "patch_dag", "Update a DAG", False),
            (patch_dags, "patch_dags", "Update multiple DAGs", False),
            (delete_dag, "delete_dag", "Delete a DAG", False),
            (clear_task_instances, "clear_task_instances", "Clear a set of task instances", False),
            (set_task_instances_state, "set_task_instances_state", "Set a state of task instances", False),
            (reparse_dag_file, "reparse_dag_file", "Request re-parsing of a DAG file", False),
        ]
Behavior2/5

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

With no annotations, the description carries full burden but only states the action without behavioral details. It doesn't mention permissions required, whether it's idempotent, what happens if the DAG isn't paused, or the response format. For a mutation tool with zero annotation coverage, this is inadequate.

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 a single, efficient sentence with zero wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly.

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?

For a mutation tool with no annotations, no output schema, and 0% schema coverage, the description is incomplete. It lacks behavioral context, parameter details, usage guidance, and expected outcomes, leaving significant gaps for an AI agent to operate effectively.

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

Parameters3/5

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

Schema description coverage is 0%, but the description adds minimal context by specifying 'by ID' for the single parameter 'dag_id'. However, it doesn't explain what a DAG ID is, its format, or where to find it. With one parameter and low coverage, this provides basic but insufficient compensation.

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 the verb ('Unpause') and resource ('a DAG by ID'), making the purpose immediately understandable. It doesn't differentiate from its sibling 'pause_dag' beyond the opposite action, but the purpose is unambiguous. A 5 would require explicit distinction from siblings.

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 like 'patch_dag' or 'update_dag_run_state', nor prerequisites such as whether the DAG must be paused first. The description only states what it does, not when it's appropriate.

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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