Airflow MCP Server
Model Context Protocol server for Apache Airflow workflow management and orchestration.
Problem Statement
AI applications needed a way to interact with Apache Airflow workflows through a standardized protocol, enabling automated workflow management, monitoring, and orchestration.
Solution Approach
We developed an MCP server that provides comprehensive Airflow functionality:
- DAG (Directed Acyclic Graph) management
- Task execution and monitoring
- Workflow scheduling and triggering
- Log access and debugging
- Connection and variable management
Technologies Used
- Python 3.11+ - Core language
- FastAPI - MCP server framework
- Apache Airflow - Workflow orchestration platform
- Airflow REST API - Official Airflow API client
- MCP Protocol - Model Context Protocol
Key Achievements
- ✅ Full Airflow API integration
- ✅ DAG lifecycle management
- ✅ Real-time workflow monitoring
- ✅ Secure authentication handling
- ✅ Production-ready deployment
Impact
Enables AI applications to:
- Automate workflow management
- Monitor and debug Airflow DAGs
- Trigger workflows on demand
- Manage Airflow connections and variables
Lessons Learned
- Workflow orchestration APIs require careful state management
- Real-time monitoring adds significant value
- Authentication and security are critical for production use
- Clear abstractions simplify complex workflow operations