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nikhil-ganage

MCP Server Airflow Token

get_task

Retrieve a specific Airflow task using its DAG and task IDs to monitor execution status and configuration details.

Instructions

Get a task by ID

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dag_idYes
task_idYes

Implementation Reference

  • The async handler function implementing the 'get_task' MCP tool. It fetches the task details from the Airflow DAG API using dag_id and task_id, converts the response to a dictionary, and returns it 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 get_all_functions() in the DAG module returns the list of available tools, including the registration tuple for 'get_task': (get_task, "get_task", "Get a task by ID", True). This list is used by main.py to register the 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)
    In the main entrypoint, tools from each API module (including DAG's get_all_functions) are registered by calling app.add_tool for each function in the list, mapping 'get_task' to its handler.
    for func, name, description, *_ in functions:
        app.add_tool(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 like authentication needs, rate limits, error handling, or what data is returned (e.g., task definition, status). For a tool with no annotation coverage, this leaves significant gaps in understanding how it behaves.

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 a task by ID'), front-loaded with the core action. There's no wasted text, 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 the complexity (a read operation with 2 required parameters), no annotations, 0% schema coverage, and no output schema, the description is incomplete. It doesn't explain what 'get' returns, parameter semantics, or usage context, leaving the agent with insufficient information to use the tool effectively.

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 schema provides no parameter descriptions. The description mentions 'by ID' but doesn't explain what 'dag_id' and 'task_id' represent, their formats, or relationships. It adds minimal meaning beyond the parameter names, failing to compensate for the lack of schema documentation.

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 action (get) and resource (task), but it's vague about what 'get' entails (retrieve metadata, status, details?) and doesn't differentiate from siblings like 'get_tasks' (plural) or 'get_task_instance'. It specifies the lookup method (by ID) which adds some specificity.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention siblings like 'get_tasks' for listing multiple tasks or 'get_task_instance' for instance details, nor does it specify prerequisites (e.g., needing a DAG ID). Usage is implied only by the name and parameters.

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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