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nikhil-ganage

MCP Server Airflow Token

fetch_dags

Retrieve and list all Directed Acyclic Graphs (DAGs) from Apache Airflow with filtering options for tags, status, and patterns to manage workflow orchestration.

Instructions

Fetch all DAGs

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
offsetNo
order_byNo
tagsNo
only_activeNo
pausedNo
dag_id_patternNo

Implementation Reference

  • Handler function `get_dags` that implements the core logic of the `fetch_dags` tool: accepts optional query parameters, calls Airflow's DAGApi.get_dags, enhances the response with UI URLs, and returns it as MCP TextContent.
    async def get_dags(
        limit: Optional[int] = None,
        offset: Optional[int] = None,
        order_by: Optional[str] = None,
        tags: Optional[List[str]] = None,
        only_active: Optional[bool] = None,
        paused: Optional[bool] = None,
        dag_id_pattern: Optional[str] = None,
    ) -> List[Union[types.TextContent, types.ImageContent, types.EmbeddedResource]]:
        # Build parameters dictionary
        kwargs: Dict[str, Any] = {}
        if limit is not None:
            kwargs["limit"] = limit
        if offset is not None:
            kwargs["offset"] = offset
        if order_by is not None:
            kwargs["order_by"] = order_by
        if tags is not None:
            kwargs["tags"] = tags
        if only_active is not None:
            kwargs["only_active"] = only_active
        if paused is not None:
            kwargs["paused"] = paused
        if dag_id_pattern is not None:
            kwargs["dag_id_pattern"] = dag_id_pattern
    
        # Use the client to fetch DAGs
        response = dag_api.get_dags(**kwargs)
    
        # Convert response to dictionary for easier manipulation
        response_dict = response.to_dict()
    
        # Add UI links to each DAG
        for dag in response_dict.get("dags", []):
            dag["ui_url"] = get_dag_url(dag["dag_id"])
    
        return [types.TextContent(type="text", text=str(response_dict))]
  • Module-level `get_all_functions` that registers the `fetch_dags` tool by including its tuple (get_dags, "fetch_dags", "Fetch all DAGs", True). This is imported and used in src/main.py to add the tool to 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)
    Global tool registration loop in main.py that calls `app.add_tool` for each function from imported module get_all_functions, including `fetch_dags` from dag.py (imported at line 7).
    for func, name, description, *_ in functions:
        app.add_tool(func, name=name, description=description)
  • Helper function `get_dag_url` used by `get_dags` to add UI links to each DAG in the response.
    def get_dag_url(dag_id: str) -> str:
        return f"{AIRFLOW_HOST}/dags/{dag_id}/grid"
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. 'Fetch' implies a read operation, but it doesn't disclose behavioral traits like whether this requires authentication, rate limits, pagination behavior (implied by limit/offset but not explained), or what format the returned DAGs are in. The description is too minimal to provide adequate transparency for a tool with 7 parameters.

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 just three words, with zero wasted language. It's front-loaded with the core action, though this brevity comes at the cost of completeness.

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 7 parameters with 0% schema coverage, no annotations, no output schema, and multiple sibling tools, the description is incomplete. It doesn't explain what DAGs are (Airflow Directed Acyclic Graphs), how results are structured, or how parameters interact. For a list/fetch tool with rich filtering options, this is inadequate.

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%, so the schema provides only parameter names and types without explanations. The description 'Fetch all DAGs' adds no meaning about any of the 7 parameters—it doesn't mention filtering by tags, active status, dag_id_pattern, ordering, or pagination. This fails to compensate for the complete lack of schema descriptions.

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 'Fetch all DAGs' states a clear verb ('fetch') and resource ('DAGs'), but it's vague about scope and doesn't distinguish from siblings like 'get_dag' (singular) or 'get_dag_details'. It doesn't specify what 'all' means in context of the filtering parameters available.

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 on when to use this tool versus alternatives like 'get_dag' (for single DAG) or 'get_dag_details'. The description doesn't mention any prerequisites, context, or exclusions for usage.

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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