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MCP Server for Apache Airflow

by yangkyeongmo

get_dag

Retrieve a specific Airflow DAG using its unique identifier to access workflow details and configuration.

Instructions

Get a DAG by ID

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dag_idYes

Implementation Reference

  • The main handler function for the 'get_dag' tool. Fetches DAG details from Airflow API using dag_id, adds UI URL, and returns formatted TextContent.
    async def get_dag(dag_id: str) -> List[Union[types.TextContent, types.ImageContent, types.EmbeddedResource]]:
        response = dag_api.get_dag(dag_id=dag_id)
    
        # Convert response to dictionary for easier manipulation
        response_dict = response.to_dict()
    
        # Add UI link to DAG
        response_dict["ui_url"] = get_dag_url(dag_id)
    
        return [types.TextContent(type="text", text=str(response_dict))]
  • Local registration of DAG-related tools, including 'get_dag' as (get_dag, "get_dag", "Get a DAG by ID", True). This list is returned for use in main.py.
    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),
        ]
  • src/main.py:95-97 (registration)
    Generic MCP tool registration loop where tools from get_dag_functions() (including get_dag) are added to the app using Tool.from_function.
    for func, name, description, *_ in functions:
        app.add_tool(Tool.from_function(func, name=name, description=description))
  • Helper function to generate UI URL for a DAG, used in get_dag handler.
    def get_dag_url(dag_id: str) -> str:
        return f"{AIRFLOW_HOST}/dags/{dag_id}/grid"
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It states 'Get a DAG by ID', implying a read operation, but doesn't disclose behavioral traits such as authentication requirements, rate limits, error handling, or what the return value includes (e.g., JSON structure). This leaves significant gaps for a tool with no annotation coverage.

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 four words, front-loaded with the core action. There's no wasted language, making it easy to parse quickly, though this brevity contributes to gaps 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 no annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't explain what 'get' returns, how to interpret results, or handle errors, making it inadequate for a tool that likely returns complex DAG data in a server with many related operations.

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 input schema has 1 parameter with 0% description coverage, and the description doesn't add any meaning beyond the parameter name 'dag_id'. It doesn't explain what a DAG ID is, its format, or where to find it, failing to compensate for the low schema coverage.

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 DAG by ID' clearly states the action (get) and resource (DAG), but it's vague about what 'get' entails—whether it retrieves metadata, configuration, or status. It doesn't differentiate from siblings like 'get_dag_details' or 'get_dag_source', which might provide more specific information about the same DAG.

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. With many sibling tools like 'get_dag_details', 'get_dag_source', and 'fetch_dags', the description lacks any indication of context, prerequisites, or exclusions, leaving the agent to guess based on tool names alone.

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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