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MCP Server Airflow Token

reparse_dag_file

Trigger re-parsing of Airflow DAG files to update workflows when changes occur, using file tokens for identification.

Instructions

Request re-parsing of a DAG file

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_tokenYes

Implementation Reference

  • The main handler function for the 'reparse_dag_file' tool. It takes a file_token parameter, calls the Airflow DAG API's reparse_dag_file method, and returns the response as formatted text content.
    async def reparse_dag_file(
        file_token: str,
    ) -> List[Union[types.TextContent, types.ImageContent, types.EmbeddedResource]]:
        response = dag_api.reparse_dag_file(file_token=file_token)
        return [types.TextContent(type="text", text=str(response.to_dict()))]
  • The registration function get_all_functions() that lists all DAG-related tools, including reparse_dag_file with its name, description, and read-only status (False). This is used to register the tool 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),
        ]
Behavior2/5

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

With no annotations, the description carries full burden but only states the action without behavioral details. It doesn't disclose if this is a read/write operation, requires permissions, has side effects (e.g., triggering DAG refreshes), rate limits, or response format. 'Request' implies a mutation, but specifics are missing, leaving significant gaps.

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 a single, efficient sentence with zero waste—it directly states the tool's action. It's appropriately sized for a simple tool and front-loaded with the core purpose, though 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 no annotations, 0% schema coverage, no output schema, and a mutation-like tool ('request re-parsing'), the description is incomplete. It lacks details on behavior, parameters, outcomes, and how it fits with siblings (e.g., vs. 'get_dag_source'). For a tool that likely triggers system changes, more context 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%, and the description adds no parameter information. It doesn't explain what 'file_token' represents (e.g., a file path, identifier, or token), its format, or how to obtain it. With 1 undocumented parameter, the description fails to compensate for the schema gap.

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 'Request re-parsing of a DAG file' states a clear action ('request re-parsing') and target ('DAG file'), but is vague about what re-parsing entails and doesn't distinguish from siblings like 'get_dag_source' or 'patch_dag'. It provides basic purpose but lacks specificity about the operation's scope or outcome.

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. The description doesn't mention prerequisites (e.g., after file changes), exclusions, or related tools like 'get_dag_source' for reading DAG files or 'patch_dag' for updates. Usage context is implied but not explicit.

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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