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yangkyeongmo

MCP Server for Apache Airflow

by yangkyeongmo

get_task

Retrieve a specific task from Apache Airflow using its DAG and task identifiers to access execution details and status.

Instructions

Get a task by ID

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dag_idYes
task_idYes

Implementation Reference

  • The main handler function for the 'get_task' MCP tool. It calls the Airflow DAG API to retrieve task details by dag_id and task_id, and returns the response as TextContent.
    async def get_task(
        dag_id: str, task_id: str
    ) -> List[Union[types.TextContent, types.ImageContent, types.EmbeddedResource]]:
        response = dag_api.get_task(dag_id=dag_id, task_id=task_id)
        return [types.TextContent(type="text", text=str(response.to_dict()))]
  • The registration entry for the 'get_task' tool within the get_all_functions() list returned by dag.py, which is imported and used in src/main.py to add tools to the MCP server.
    (get_task, "get_task", "Get a task by ID", True),
  • Helper function get_all_functions() that provides the list of tool tuples, including get_task, for registration in the MCP server.
    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:95-96 (registration)
    The generic loop in main.py that registers all tools from modules like dag.py by calling app.add_tool for each function in get_all_functions().
    for func, name, description, *_ in functions:
        app.add_tool(Tool.from_function(func, name=name, description=description))
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. 'Get a task by ID' implies a read-only operation, but it doesn't disclose behavioral traits such as error handling (e.g., what happens if the ID is invalid), authentication needs, rate limits, or return format. This is a significant gap 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—a single sentence with no wasted words. It's front-loaded and efficiently states the core action, 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 the complexity (2 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what a 'task' is, how parameters relate, what data is returned, or error conditions. For a tool in a server with many similar siblings, more context is needed to guide proper usage.

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 2 parameters with 0% description coverage, so the schema provides no semantic information. The description adds minimal value by implying parameters are IDs, but doesn't explain what 'dag_id' and 'task_id' represent, their formats, or relationships. It fails 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 task by ID' clearly states the verb ('Get') and resource ('task'), but it's vague about what 'get' entails (e.g., retrieve metadata, fetch details) and doesn't distinguish it from siblings like 'get_tasks' (plural) or 'get_task_instance'. It specifies 'by ID', which helps, but lacks precision on what a 'task' is in this context.

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. It doesn't mention prerequisites, context (e.g., after fetching a DAG), or exclusions. With many sibling tools like 'get_tasks', 'get_task_instance', and 'get_dag_tasks', the agent has no help in choosing correctly.

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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