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nikhil-ganage

MCP Server Airflow Token

get_dag_details

Retrieve simplified DAG representations from Apache Airflow deployments to analyze workflow structures and configurations.

Instructions

Get a simplified representation of DAG

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dag_idYes
fieldsNo

Implementation Reference

  • The main handler function implementing the get_dag_details tool. It fetches simplified DAG details from the Airflow API based on dag_id and optional fields, returning the result as text content.
    async def get_dag_details(
        dag_id: str, fields: Optional[List[str]] = None
    ) -> List[Union[types.TextContent, types.ImageContent, types.EmbeddedResource]]:
        # Build parameters dictionary
        kwargs: Dict[str, Any] = {}
        if fields is not None:
            kwargs["fields"] = fields
    
        response = dag_api.get_dag_details(dag_id=dag_id, **kwargs)
        return [types.TextContent(type="text", text=str(response.to_dict()))]
  • The get_all_functions() in dag.py provides the registration tuple for the get_dag_details tool (function reference, name, description, read-only flag), which is imported and used in src/main.py to register all tools with 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:90-92 (registration)
    The generic registration loop in main.py that calls app.add_tool for each tool from get_all_functions(), including get_dag_details from dag.py.
    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 full burden. It states this is a 'Get' operation, implying read-only behavior, but doesn't disclose any behavioral traits like authentication requirements, rate limits, error conditions, or what 'simplified' entails in practice. 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 that directly states the tool's function. There's no wasted language or unnecessary elaboration, making it front-loaded and easy to parse. Every word earns its place.

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 of DAG operations, 2 parameters with 0% schema coverage, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what 'simplified' means, how it differs from other DAG tools, what the 'fields' parameter controls, or what the return format looks like. For a tool in this context, more detail is needed.

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 for undocumented parameters. It mentions 'DAG' which relates to 'dag_id', but doesn't explain what 'fields' parameter does or what 'simplified representation' means in terms of output. With 2 parameters and no schema descriptions, the description adds minimal semantic value beyond the parameter names themselves.

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 states the tool 'Get a simplified representation of DAG' which provides a basic verb+resource combination ('Get' + 'DAG representation'). However, it's vague about what 'simplified' means compared to other DAG-related tools like 'get_dag' or 'get_dag_tasks', and doesn't clearly distinguish from siblings. It avoids tautology but lacks 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. With many sibling tools like 'get_dag', 'get_dag_tasks', and 'get_dag_stats', there's no indication of what makes this 'simplified representation' unique or when it's preferred over other DAG retrieval tools. Usage is implied at best.

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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