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BrianLondon

airflow-dev-mcp

by BrianLondon

get_run_status

Retrieve the current state of a DAG run and its task instances. Check if the run succeeded or failed and get per-task status details.

Instructions

Get the state of a DAG run and (optionally) its task instances.

Args: dag_id: DAG identifier. run_id: DAG run identifier returned by trigger_dag (e.g. manual__2026-07-02T14:23:11+00:00). include_tasks: When True (default), also fetch per-task states.

Returns: RunStatus with run (a DagRunSummary) and, if requested, tasks (a list of TaskInstanceSummary: task_id, state, try_number, operator, start/end dates, duration, map_index). tasks is null when include_tasks is False.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dag_idYes
run_idYes
include_tasksNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
runYes
tasksNo
Behavior4/5

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

With no annotations, the description fully carries behavioral disclosure. It details return structure (RunStatus, TaskInstanceSummary fields) and the conditional null of tasks when include_tasks=False. No contradictions.

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

Conciseness4/5

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

The description is well-structured with Args and Returns sections, though slightly verbose due to detailed type info. It is clear and front-loaded with the main purpose.

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

Completeness4/5

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

Given an output schema exists, the description's return details are supplementary but helpful. It covers purpose, parameters, and return structure sufficiently for a medium-complexity tool.

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?

Schema description coverage is 0%, but the description provides clear, meaningful descriptions for all three parameters, including an example for run_id, which adds significant value beyond bare schema titles.

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?

The description clearly states it gets the state of a DAG run and optionally its task instances. It distinguishes from sibling tools like list_dag_runs (which lists runs) by focusing on a single run's detailed status.

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?

The description explains parameters and when to use include_tasks, but does not explicitly contrast with alternatives like list_dag_runs or clear_task_instances, though the purpose is clear enough.

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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