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nikhil-ganage

MCP Server Airflow Token

get_tasks

Retrieve tasks from an Apache Airflow DAG to monitor workflow execution and manage dependencies in data pipelines.

Instructions

Get tasks for DAG

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dag_idYes
order_byNo

Implementation Reference

  • The main handler function implementing the 'get_tasks' tool. It calls the Airflow DAG API's get_tasks method with dag_id and optional order_by, returning the response as text content.
    async def get_tasks(
        dag_id: str, order_by: Optional[str] = None
    ) -> List[Union[types.TextContent, types.ImageContent, types.EmbeddedResource]]:
        kwargs = {}
        if order_by is not None:
            kwargs["order_by"] = order_by
    
        response = dag_api.get_tasks(dag_id=dag_id, **kwargs)
        return [types.TextContent(type="text", text=str(response.to_dict()))]
  • Registration of the 'get_tasks' tool within the get_all_functions list used 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 offers minimal behavioral insight. It implies a read operation ('Get') but doesn't disclose permissions needed, rate limits, pagination, return format, or error conditions. For a tool with no 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 at 3 words, front-loaded with the core action. There's no wasted language, though this brevity contributes to underspecification rather than clarity.

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?

For a tool with 2 parameters, 0% schema coverage, no annotations, and no output schema, the description is incomplete. It doesn't provide enough context for an agent to understand what the tool returns, how to use parameters effectively, or how it differs from similar tools in the sibling list.

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 adds no parameter information. It doesn't explain what 'dag_id' refers to, what 'order_by' options exist, or how parameters affect results. With 2 parameters (1 required) and no schema descriptions, this leaves significant gaps.

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 tasks for DAG' states the basic purpose (retrieving tasks) and resource (DAG), but it's vague about scope and doesn't differentiate from sibling tools like 'get_dag_tasks' or 'get_task'. It doesn't specify whether it returns all tasks, specific tasks, or tasks with certain statuses.

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_tasks' or 'get_task'. The description doesn't mention prerequisites, context, or exclusions, leaving the agent to infer usage from the tool name 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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