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MCP Server for Apache Airflow

by yangkyeongmo

get_dag_details

Retrieve simplified DAG details from Apache Airflow to understand workflow structure and monitor pipeline execution status.

Instructions

Get a simplified representation of DAG

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dag_idYes
fieldsNo

Implementation Reference

  • The async handler function that executes the tool logic: accepts dag_id and optional fields, calls the Airflow DAG API's get_dag_details method, and returns the response as formatted text content.
    async def get_dag_details(
        dag_id: str, fields: Optional[List[str]] = None
    ) -> List[Union[types.TextContent, types.ImageContent, types.EmbeddedResource]]:
        # Build parameters dictionary
        kwargs: Dict[str, Any] = {}
        if fields is not None:
            kwargs["fields"] = fields
    
        response = dag_api.get_dag_details(dag_id=dag_id, **kwargs)
        return [types.TextContent(type="text", text=str(response.to_dict()))]
  • The get_all_functions() returns the list of all MCP tools including the tuple for get_dag_details (function, name, description, read_only) used for 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 the full burden of behavioral disclosure. It only states it 'gets' a representation, implying a read operation, but doesn't cover critical aspects like authentication requirements, rate limits, error conditions, or what the output format looks like. For a tool with zero annotation coverage, this leaves significant gaps.

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 one sentence and no wasted words. It's front-loaded with the core purpose, though this brevity comes at the cost of completeness. Every word earns its place in conveying the basic intent.

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 the complexity (2 parameters, no output schema, no annotations), the description is insufficient. It doesn't explain what 'simplified representation' means, doesn't document parameters, and provides no behavioral context. For a tool that likely returns structured DAG data, this leaves too many unanswered questions for effective agent use.

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?

The schema has 0% description coverage, so parameters 'dag_id' and 'fields' are completely undocumented in the schema. The description doesn't mention either parameter or explain their purpose, format, or constraints. It fails to compensate for the schema's lack of documentation, leaving agents guessing about required inputs.

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 a simplified representation of DAG' clearly states the action (get) and resource (DAG), but it's vague about what 'simplified representation' means compared to siblings like 'get_dag' or 'get_dag_tasks'. It doesn't specify what aspects are simplified or how it differs from other DAG retrieval tools.

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 'get_dag', 'get_dag_tasks', or 'fetch_dags'. The description doesn't mention any prerequisites, constraints, or specific use cases that would help an agent choose this tool over its siblings.

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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