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MCP Server Airflow Token

patch_dag

Modify Airflow DAG configurations by updating pause status, tags, or other parameters to manage workflow execution.

Instructions

Update a DAG

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dag_idYes
is_pausedNo
tagsNo

Implementation Reference

  • The main handler function that implements the 'patch_dag' tool. It constructs an update request for optional fields 'is_paused' and 'tags', creates a DAG model instance, and calls the Airflow DAG API to patch the specified DAG.
    async def patch_dag(
        dag_id: str, is_paused: Optional[bool] = None, tags: Optional[List[str]] = None
    ) -> List[Union[types.TextContent, types.ImageContent, types.EmbeddedResource]]:
        update_request = {}
        update_mask = []
    
        if is_paused is not None:
            update_request["is_paused"] = is_paused
            update_mask.append("is_paused")
        if tags is not None:
            update_request["tags"] = tags
            update_mask.append("tags")
    
        dag = DAG(**update_request)
    
        response = dag_api.patch_dag(dag_id=dag_id, dag=dag, update_mask=update_mask)
        return [types.TextContent(type="text", text=str(response.to_dict()))]
  • The get_all_functions() returns a list of tuples for registering all DAG-related tools, including the 'patch_dag' tool with name 'patch_dag', description 'Update a DAG', and is_read_only=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),
        ]
Behavior1/5

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

No annotations are provided, so the description carries full burden for behavioral disclosure. 'Update a DAG' provides minimal information - it implies a mutation operation but doesn't specify what gets modified, whether changes are reversible, what permissions are required, or what the response looks like. For a mutation tool with zero annotation coverage, this is inadequate behavioral transparency.

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 extremely concise at just three words, which could be appropriate if it were more informative. However, this brevity results in under-specification rather than efficient communication. While front-loaded, it lacks the substance needed for the tool's complexity.

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?

Given a mutation tool with 3 parameters, 0% schema description coverage, no annotations, and no output schema, the description 'Update a DAG' is completely inadequate. It doesn't explain what the tool does beyond the obvious, provides no parameter guidance, offers no behavioral context, and fails to distinguish it from numerous sibling DAG manipulation tools. This leaves the agent with insufficient information to use the tool correctly.

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 none of the three parameters (dag_id, is_paused, tags) have descriptions in the schema. The tool description provides no information about any parameters - it doesn't mention what dag_id refers to, what is_paused controls, or what tags represent. With zero parameter information in either schema or description, this fails to provide necessary semantic context.

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 'Update a DAG' is a tautology that restates the tool name 'patch_dag' without adding specificity. It doesn't clarify what aspects of a DAG are updated or distinguish this tool from sibling tools like 'pause_dag', 'unpause_dag', or 'patch_dags'. The verb 'Update' is generic and the resource 'DAG' is too broad given the context.

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 no guidance on when to use this tool versus alternatives. With sibling tools like 'pause_dag', 'unpause_dag', 'patch_dags', and 'update_dag_run_state', there's no indication of when this specific patch operation is appropriate versus those other DAG modification tools. No prerequisites, constraints, or comparison context is mentioned.

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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