Airflow + BeeAI Framework: Complete MCP Integration
Airflow is a MCP server that A MCP Server that connects to Apache Airflow using official python client..
When integrated with BeeAI Framework, you can:
- List all available DAGs in Airflow
- Manually trigger a DAG run
- Get DAG run history and status
This guide provides step-by-step instructions to set up Airflow in BeeAI Framework, including configuration, examples, and troubleshooting.
What You'll Achieve
After completing this setup:
- Airflow will be fully integrated and operational
- You can use Airflow tools directly in BeeAI Framework
- All Airflow capabilities will be available for your workflows
- Access to 5 different tools
Prerequisites
Before starting, ensure you have:
- Airflow webserver host URL
- Airflow authentication username
- Airflow authentication password
- BeeAI Framework installed and configured
- Compatible operating system (Node.js, Cross-platform)
Installation
Step 1: Install Airflow
Configuration
Step 2: Configure BeeAI Framework
- Open BeeAI Framework settings
- Navigate to MCP server configuration
- Add Airflow server with appropriate settings
- Save and restart if needed
Examples
Once configured, you can use Airflow in BeeAI Framework:
Pipeline Monitoring
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Ask BeeAI Framework: "Check status of all DAGs and recent failures"
Expected Result: undefined
Manual DAG Execution
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Ask BeeAI Framework: "Trigger monthly_report DAG with date parameter"
Expected Result: undefined
Troubleshooting Failed Tasks
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Ask BeeAI Framework: "Show failed tasks in data_pipeline and their error logs"
Expected Result: undefined
Testing Your Setup
- Launch BeeAI Framework
- Verify Airflow is available in the tools list
- Test basic Airflow functionality
Troubleshooting
Common Issues
Framework Installation Fails
Symptoms: npm install errors, Dependency conflicts
Cause: Node.js version or dependency issues
Solution:
- Verify Node.js version compatibility
- Clear npm cache and node_modules
- Use npm ci for clean installation
- Check for conflicting global packages
Configuration Not Loaded
Symptoms: Config file ignored, Default settings used
Cause: Configuration file syntax or location issues
Solution:
- Verify beeai.config.js syntax is correct
- Check configuration file location in project root
- Use absolute paths for server executables
- Validate JSON/JavaScript configuration format
Workflow Execution Fails
Symptoms: Workflow errors, MCP tools not available
Cause: MCP server connection or tool execution issues
Solution:
- Check MCP server installation and PATH
- Verify server is responding to connections
- Test server independently before framework integration
- Review workflow logs for specific error messages
Airflow not appearing in BeeAI Framework
Symptoms: Server not listed, Tools not available
Cause: Configuration or installation issue
Solution:
- Verify configuration syntax
- Check Airflow installation
- Restart BeeAI Framework
- Check logs for error messages
Next Steps
Now that Airflow is integrated with BeeAI Framework:
- Explore all Airflow capabilities through BeeAI Framework
- Check out other MCP servers that work with BeeAI Framework
- Join the MCP community for tips and support
- Consider contributing to Airflow development
Need Help?
- Search for Airflow documentation
- Check the BeeAI Framework MCP guide
- Join the MCP community discussions