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astronomer

astro-airflow-mcp

Official
by astronomer

get_airflow_config

Retrieve complete Airflow configuration settings organized by sections such as core, database, and webserver, with key, value, and source for each parameter.

Instructions

Get Airflow instance configuration and settings.

Use this tool when the user asks about:

  • "What's the Airflow configuration?" or "Show me Airflow settings"

  • "What's the executor type?" or "How is Airflow configured?"

  • "What's the parallelism setting?"

  • Database connection, logging, or scheduler settings

  • Finding specific configuration values

Returns all Airflow configuration organized by sections:

  • [core]: Basic Airflow settings (executor, dags_folder, parallelism)

  • [database]: Database connection and settings

  • [webserver]: Web UI configuration (port, workers, auth)

  • [scheduler]: Scheduler behavior and intervals

  • [logging]: Log locations and formatting

  • [api]: REST API configuration

  • [operators]: Default operator settings

  • And many more sections...

Each setting includes:

  • key: Configuration parameter name

  • value: Current value

  • source: Where the value came from (default, env var, config file)

Returns: JSON with complete Airflow configuration organized by sections

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations provided, so description carries full burden. It describes the output structure and that it returns JSON, but does not mention authentication, permissions, or side effects. For a getter, it's adequate but not thorough.

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?

Well-structured with bullet points and sections, front-loaded with purpose. However, the list of example queries is slightly verbose and could be shortened.

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 parameters and presence of output schema, the description fully explains the output sections and per-setting details, making it complete for the tool's purpose.

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

Parameters4/5

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

No parameters in input schema, so baseline is 4. Description adds value by detailing the output sections and structure, exceeding the baseline.

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 the tool gets Airflow configuration and settings, listing specific user queries and sections returned. It distinguishes from sibling tools (e.g., get_airflow_version) by focusing on config retrieval.

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?

Explicitly states when to use with example queries, providing clear context. Does not mention when not to use, but given the tool's read-only nature, it's acceptable.

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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