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BrianLondon

airflow-dev-mcp

by BrianLondon

clear_task_instances

Clear task instances for re-execution after a fix. Preview with dry run, or apply with dry_run=False.

Instructions

Clear task instances so they re-run — the fast way to re-test a task after a fix.

Defaults to a DRY RUN: it reports which task instances would be cleared without touching them. Pass dry_run=False to actually clear; with reset_dag_runs=True the affected run is put back into a running state so cleared tasks re-execute.

Args: dag_id: DAG identifier. dag_run_id: Restrict to a single run (recommended). If omitted, the API's other filters apply across runs. task_ids: Restrict to specific task_ids. If omitted, all matching tasks are cleared. only_failed: When True, only clear failed task instances. reset_dag_runs: When True (default), set affected runs back to running so cleared tasks are re-scheduled. dry_run: When True (default), preview only. Set False to actually clear.

Returns: ClearResult with dry_run (echoed) and task_instances (the affected TIs).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dag_idYes
dry_runNo
task_idsNo
dag_run_idNo
only_failedNo
reset_dag_runsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dry_runYes
task_instancesYes
Behavior5/5

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

Description fully discloses behavior: dry-run default, effect of reset_dag_runs, restriction options, and return value. No annotations were provided, so description carries full burden and exceeds expectations.

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?

Well-structured with a concise summary, behavior explanation, parameter list, and return description. Every sentence adds value, no redundancy.

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

Completeness5/5

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

Given no annotations, 6 parameters, and existing output schema, the description is fully complete: explains purpose, parameter usage, and return structure without gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

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

Input schema has 0% description coverage, but description adds detailed and actionable semantics for all 6 parameters, including recommendations (dag_run_id) and conditional behavior (only_failed).

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

Purpose5/5

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

Clearly states it clears task instances for re-running, uses specific verb and resource, and distinguishes from siblings like get_run_status or list_dags.

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

Usage Guidelines4/5

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

Provides context ('fast way to re-test after a fix') and explains default behavior (dry run) and how to actually clear. No explicit when-not-to-use, but the context is clear.

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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