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MCP Server Airflow Token

get_dag_source

Retrieve DAG source code from Apache Airflow deployments using file tokens for programmatic access and analysis.

Instructions

Get a source code

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_tokenYes

Implementation Reference

  • The async handler function that executes the get_dag_source tool logic by calling the Airflow DAG API to retrieve the DAG source code and returning it as TextContent.
    async def get_dag_source(file_token: str) -> List[Union[types.TextContent, types.ImageContent, types.EmbeddedResource]]:
        response = dag_api.get_dag_source(file_token=file_token)
        return [types.TextContent(type="text", text=str(response.to_dict()))]
  • Registration of all DAG-related tools, including the tuple for get_dag_source: (get_dag_source, "get_dag_source", "Get a source code", True).
    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),
        ]
Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Get a source code' gives no information about whether this is a read-only operation, whether it requires specific permissions, what format the source code is returned in, potential rate limits, or error conditions. For a tool with no annotation coverage, this description provides essentially zero behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

While technically concise with just three words, this is an example of under-specification rather than effective conciseness. The description doesn't earn its place by providing necessary information. A proper concise description would still include essential context about what's being retrieved and for what purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given a tool with 1 required parameter, no annotations, no output schema, and 0% schema description coverage, the description 'Get a source code' is completely inadequate. It provides no information about what the tool returns, how to use it correctly, what the parameter means, or how this differs from similar sibling tools. This leaves the AI 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.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description provides no information about the single required parameter 'file_token'. With 0% schema description coverage (the schema has no descriptions for the parameter), the description fails completely to compensate. It doesn't explain what a file_token is, how to obtain it, what format it should be in, or what it represents in the context of getting source code.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Get a source code' is a tautology that essentially restates the tool name 'get_dag_source' without adding meaningful specificity. It doesn't clarify what type of source code (DAG definition, Python file, configuration) or from what system (Airflow DAG repository, version control, etc.). Compared to sibling tools like 'get_dag' or 'get_dag_details', it fails to distinguish its specific purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides absolutely no guidance on when to use this tool versus alternatives. With many sibling tools available (like get_dag, get_dag_details, get_dag_tasks), there's no indication whether this retrieves raw DAG definition files, Python source code for tasks, or something else. No context about prerequisites, dependencies, or appropriate scenarios is mentioned.

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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