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yangkyeongmo

MCP Server for Apache Airflow

by yangkyeongmo

patch_dags

Modify multiple Airflow DAGs simultaneously by updating their pause status, tags, or using ID patterns for batch operations.

Instructions

Update multiple DAGs

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dag_id_patternNo
is_pausedNo
tagsNo

Implementation Reference

  • The async handler function implementing the 'patch_dags' tool. It constructs an update request for DAG properties (is_paused, tags), creates a DAG object, and calls the Airflow API to patch matching DAGs based on dag_id_pattern.
    async def patch_dags(
        dag_id_pattern: Optional[str] = None,
        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)
    
        kwargs = {}
        if dag_id_pattern is not None:
            kwargs["dag_id_pattern"] = dag_id_pattern
    
        response = dag_api.patch_dags(dag_id_pattern=dag_id_pattern, dag=dag, update_mask=update_mask, **kwargs)
        return [types.TextContent(type="text", text=str(response.to_dict()))]
  • Registration of the 'patch_dags' tool within the get_all_functions() list, which provides (function, name, description, read_only) for MCP tool registration.
    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 provided, the description carries full burden but only states 'update' without clarifying behavioral traits. It doesn't mention whether this is a safe operation, what permissions are required, if changes are reversible, rate limits, or what happens to DAGs matching the pattern. 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 extremely concise with just three words, front-loading the core action. There's no wasted language, though this brevity contributes to underspecification in other dimensions.

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 a mutation tool with 3 parameters, 0% schema coverage, no annotations, and no output schema, the description is incomplete. It doesn't explain what DAGs are, what 'update' entails, parameter usage, or expected outcomes, leaving significant gaps for an AI agent to use it correctly.

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 description must compensate but provides no parameter information. It doesn't explain what 'dag_id_pattern', 'is_paused', or 'tags' mean, their formats, or how they interact to update DAGs. With 3 undocumented parameters, the description adds no value beyond the 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 'Update multiple DAGs' clearly states the verb ('update') and resource ('multiple DAGs'), making the purpose understandable. It distinguishes from the sibling 'patch_dag' (singular) by specifying 'multiple', but doesn't explain what DAGs are or what aspects are updated beyond the implied scope.

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' (for single DAG updates), 'pause_dag', or 'unpause_dag'. The description mentions 'multiple DAGs' but doesn't specify prerequisites, constraints, or typical use cases for batch updates.

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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