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astronomer

astro-airflow-mcp

Official
by astronomer
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Python
Hybrid

get_dag_run

Retrieve detailed information about a specific Airflow DAG run, including its state, start and end times, duration, run type, configuration, and execution metadata.

Instructions

Get detailed information about a specific DAG run execution.

Use this tool when the user asks about:

  • "Show me details for DAG run X" or "What's the status of run Y?"

  • "When did this run start/finish?" or "How long did run Z take?"

  • "Why did this run fail?" or "Get execution details for run X"

  • "What was the configuration for this run?" or "Show me run metadata"

  • "What's the state of DAG run X?" or "Did run Y succeed?"

Returns detailed information about a specific DAG run execution including:

  • dag_run_id: Unique identifier for this execution

  • dag_id: Which DAG this run belongs to

  • state: Current state (running, success, failed, queued, etc.)

  • execution_date: When this run was scheduled to execute

  • start_date: When execution actually started

  • end_date: When execution completed (if finished)

  • duration: How long the run took (in seconds)

  • run_type: Type of run (manual, scheduled, backfill, etc.)

  • conf: Configuration parameters passed to this run

  • external_trigger: Whether this was triggered externally

  • data_interval_start: Start of the data interval

  • data_interval_end: End of the data interval

  • last_scheduling_decision: Last scheduling decision timestamp

  • note: Optional note attached to the run

Args: dag_id: The ID of the DAG (e.g., "example_dag") dag_run_id: The ID of the DAG run (e.g., "manual__2024-01-01T00:00:00+00:00")

Returns: JSON with complete details about the specified DAG run

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dag_idYes
dag_run_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.5/5.0
Behavior4/5

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

No annotations provided, so description bears full burden. It comprehensively lists all returned fields and states output is JSON. It does not mention error handling, authorization, or read-only nature, but for a read operation this is adequate.

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 structured with sections and bullet points, front-loading the purpose. It is thorough but slightly verbose; could be trimmed without losing meaning.

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 the tool's complexity, lack of annotations, and presence of output schema (implied by detailed field list), the description is complete. It covers usage guidance, parameter semantics, and return value details, enabling correct selection among 28 siblings.

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 coverage is 0%, but the description provides clear meaning and examples for both parameters (dag_id and dag_run_id). It fully compensates for the missing schema descriptions.

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?

Description clearly states it gets detailed information about a specific DAG run execution. Use cases and examples distinguish it from siblings like list_dag_runs (list all runs) and diagnose_dag_run (diagnose issues).

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 provides explicit when-to-use scenarios with example user queries. It implicitly differentiates from alternative tools but does not explicitly state when not to use it or name alternatives directly.

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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