What key Airflow operations can I perform with this tool?

You can manage DAGs (list, monitor, trigger, pause/unpause), monitor cluster health, manage pools and variables, get detailed task instance info (including logs and XComs), and perform advanced DAG analysis.

How does it support AI or LLM integrations?

It's designed with minimal, LLM-friendly output, enabling easy integration with AI agents, OpenWebUI, and Smithery for natural language interaction and control over your Airflow workflows.

Is this tool suitable for large Apache Airflow deployments?

Yes, it's highly optimized for large-scale environments, featuring resource-efficient default limits, comprehensive pagination, and automated helpers to manage thousands of DAGs efficiently.

What is the Airflow API (MCP) tool?

The Airflow API (MCP) tool provides natural language, Model Context Protocol (MCP)-driven tools for comprehensive cluster management, monitoring, and operations within Apache Airflow environments.

Airflow API: LLM-Friendly Cluster Management & DevOps

This tool serves as a Model Context Protocol (MCP) server, offering a robust natural language interface for managing Apache Airflow clusters. It provides comprehensive tools for essential operations, including DAG management, service operations, configuration oversight, real-time status monitoring, and request tracking. Optimized for large-scale enterprise environments, it ensures efficient resource usage with advanced pagination and provides minimal, LLM-friendly output for seamless integration with AI agents and tools like OpenWebUI and Smithery.

主な機能

01Comprehensive DAG management: list, monitor, trigger, pause, and unpause DAGs.

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03Provides tools for cluster health monitoring, pool management, and variable management.

04Detailed task instance management, including logs, XComs, and extra links.

05Optimized for large-scale Airflow environments with pagination and resource-efficient default limits.

06Advanced DAG analysis and monitoring with event logs, import error summaries, and duration statistics.

ユースケース

01Automating Airflow cluster management and operations via natural language interfaces and AI agents.

02Retrieving comprehensive Airflow operational data for analysis, reporting, or integration into other systems.

03Monitoring and troubleshooting DAGs, task instances, and overall cluster health in real-time.

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Airflow API

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