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MCP Server for Apache Airflow

by yangkyeongmo

clear_dag_run

Clear a specific DAG run in Apache Airflow to remove task instances and reset execution status for rerunning workflows.

Instructions

Clear a DAG run

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dag_idYes
dag_run_idYes
dry_runNo

Implementation Reference

  • The main handler function for the 'clear_dag_run' tool. It constructs a ClearDagRun model and calls the Airflow API to clear the specified DAG run.
    async def clear_dag_run(
        dag_id: str, dag_run_id: str, dry_run: Optional[bool] = None
    ) -> List[Union[types.TextContent, types.ImageContent, types.EmbeddedResource]]:
        clear_dag_run = ClearDagRun(dry_run=dry_run)
        response = dag_run_api.clear_dag_run(dag_id=dag_id, dag_run_id=dag_run_id, clear_dag_run=clear_dag_run)
        return [types.TextContent(type="text", text=str(response.to_dict()))]
  • Registration of all DAG run tools, including the 'clear_dag_run' tool with its handler, name, description, and read-only flag.
    def get_all_functions() -> list[tuple[Callable, str, str, bool]]:
        """Return list of (function, name, description, is_read_only) tuples for registration."""
        return [
            (post_dag_run, "post_dag_run", "Trigger a DAG by ID", False),
            (get_dag_runs, "get_dag_runs", "Get DAG runs by ID", True),
            (get_dag_runs_batch, "get_dag_runs_batch", "List DAG runs (batch)", True),
            (get_dag_run, "get_dag_run", "Get a DAG run by DAG ID and DAG run ID", True),
            (update_dag_run_state, "update_dag_run_state", "Update a DAG run state by DAG ID and DAG run ID", False),
            (delete_dag_run, "delete_dag_run", "Delete a DAG run by DAG ID and DAG run ID", False),
            (clear_dag_run, "clear_dag_run", "Clear a DAG run", False),
            (set_dag_run_note, "set_dag_run_note", "Update the DagRun note", False),
            (get_upstream_dataset_events, "get_upstream_dataset_events", "Get dataset events for a DAG run", True),
        ]
  • Import of the ClearDagRun model class, which defines the input schema for the clear_dag_run API call.
    from airflow_client.client.model.clear_dag_run import ClearDagRun
Behavior1/5

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

No annotations are provided, so the description carries full burden for behavioral disclosure. 'Clear a DAG run' implies a destructive mutation but doesn't specify what 'clear' entails (e.g., deletion, state reset, data removal), whether it's reversible, what permissions are needed, or any side effects. This is inadequate for a mutation tool with zero annotation coverage.

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 no wasted language. It's front-loaded with the core action, though this brevity comes at the cost of clarity and completeness.

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?

For a mutation tool with 3 parameters, 0% schema coverage, no annotations, and no output schema, the description is completely inadequate. It doesn't explain what 'clear' means, when to use it, what the parameters do, or what to expect in return, leaving critical gaps for agent understanding.

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%, meaning none of the 3 parameters (dag_id, dag_run_id, dry_run) are documented in the schema. The description adds no information about these parameters—not their purposes, formats, or examples—failing to compensate for the complete lack of schema documentation.

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 'Clear a DAG run' is essentially a tautology that restates the tool name. It specifies the verb 'clear' and resource 'DAG run', but lacks specificity about what 'clear' means operationally (e.g., delete, reset, remove data). It doesn't distinguish from sibling tools like 'delete_dag_run', leaving ambiguity about their differences.

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?

No guidance is provided on when to use this tool versus alternatives. With sibling tools like 'delete_dag_run', 'clear_task_instances', and 'set_task_instances_state', the description offers no context on appropriate use cases, prerequisites, or exclusions, leaving the agent to guess based on naming alone.

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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