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nikhil-ganage

MCP Server Airflow Token

get_dag_tasks

Retrieve tasks for a specific Airflow DAG to monitor workflow components and dependencies.

Instructions

Get tasks for DAG

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dag_idYes

Implementation Reference

  • The async handler function implementing the 'get_dag_tasks' tool. It calls the Airflow API to get tasks for the specified DAG ID and returns the result as formatted text content.
    async def get_dag_tasks(dag_id: str) -> List[Union[types.TextContent, types.ImageContent, types.EmbeddedResource]]:
        response = dag_api.get_tasks(dag_id=dag_id)
        return [types.TextContent(type="text", text=str(response.to_dict()))]
  • The get_all_functions() in dag.py returns a list of tuples including the registration for 'get_dag_tasks': (get_dag_tasks, "get_dag_tasks", "Get tasks for DAG", True). This list is used to register tools.
    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:90-92 (registration)
    The main registration loop in src/main.py that calls app.add_tool() for each function from the get_all_functions() lists of various modules, including dag.py's get_dag_tasks.
    for func, name, description, *_ in functions:
        app.add_tool(func, name=name, description=description)
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. It only states 'Get tasks for DAG', offering no behavioral details such as read-only vs. destructive nature, authentication needs, rate limits, error handling, or output format. This is inadequate for a tool with unknown behavior.

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 a single sentence 'Get tasks for DAG', which is front-loaded and wastes no words. However, this conciseness comes at the cost of under-specification, but structurally it is efficient.

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 no annotations, 0% schema coverage, no output schema, and a simple but unclear purpose, the description is incomplete. It fails to provide necessary context for a tool that interacts with DAG tasks, lacking details on behavior, parameters, and usage relative to siblings.

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%, with one parameter 'dag_id' undocumented in the schema. The description adds no parameter information, failing to explain what 'dag_id' is, its format, or examples. This leaves the parameter's meaning and usage unclear.

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 'Get tasks for DAG' restates the tool name 'get_dag_tasks' almost verbatim, making it tautological. It specifies the resource ('tasks for DAG') but lacks a clear verb beyond 'Get', which is generic. It does not distinguish from siblings like 'get_tasks' or 'get_task', leaving ambiguity about scope.

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?

No guidance is provided on when to use this tool versus alternatives. Sibling tools include 'get_tasks' and 'get_task', but the description offers no context on differences, prerequisites, or exclusions. This leaves the agent without direction for selection.

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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