Airflow API
Integrates Apache Airflow API to provide comprehensive tools for cluster management, monitoring, and operations via Model Context Protocol.
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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.
Características Principales
- Comprehensive DAG management: list, monitor, trigger, pause, and unpause DAGs.
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- Provides tools for cluster health monitoring, pool management, and variable management.
- Detailed task instance management, including logs, XComs, and extra links.
- Optimized for large-scale Airflow environments with pagination and resource-efficient default limits.
- Advanced DAG analysis and monitoring with event logs, import error summaries, and duration statistics.
Casos de Uso
- Automating Airflow cluster management and operations via natural language interfaces and AI agents.
- Retrieving comprehensive Airflow operational data for analysis, reporting, or integration into other systems.
- Monitoring and troubleshooting DAGs, task instances, and overall cluster health in real-time.
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