airflow-mcp-server MCP Server: Controlling Airflow with Model Context Protocol

Overview: What is airflow-mcp-server?

airflow-mcp-server is an MCP (Model Context Protocol) server designed to control Apache Airflow via its APIs, enabling various AI applications to manage and interact with the Airflow environment. By leveraging the standardized protocol provided by MCP, this server facilitates seamless integration between AI tools such as Claude Desktop, Continue, Cursor, and others, offering a robust solution for managing workflows in complex data processing environments.

🔧 Core Features & MCP Capabilities

The airflow-mcp-server implements several core features that make it a powerful tool for managing Airflow instances through MCP. These include:

• Safe Mode: A mode where the server only allows read-only operations (GET requests), ensuring that no modifications can be made to the Airflow instance. This is particularly useful in production environments where data integrity needs to be maintained.

• Unsafe Mode: The default operational mode, which permits all types of operations including modifications and updates. This offers greater flexibility for development and testing environments.

• Customizable Authentication Options: The server supports both basic authentication using an AUTH_TOKEN (base64-encoded username:password) and cookie-based authentication through a provided COOKIE. At least one of these methods must be set to ensure secure interactions with Airflow.

• Configuration Flexibility: Users can customize certain aspects of the behavior, such as adjusting the number of items per page using the maximum_page_limit option in the Airflow configuration file (airflow.cfg).

⚙️ MCP Architecture & Protocol Implementation

The implementation details of the MCP server are structured to ensure compatibility and interoperability with a wide range of AI tools. The architecture includes:

• MCP Client Compatibility: The airflow-mcp-server is compatible with several popular MCP clients, including Claude Desktop, Continue, Cursor, but does not support some functionalities for certain tools.

  graph TD
      A[AI Application] -->|MCP Client| B[MCP Protocol]
      B --> C[MCP Server]
      C --> D[Data Source/Tool]
      style A fill:#e1f5fe
      style C fill:#f3e5f5
      style D fill:#e8f5e8
  

• MCP Client Compatibility Matrix: The server is fully compatible with Claude Desktop, Continue, and Cursor. There are limitations for tools where only specific features are supported.

  MCP Client	Resources	Tools	Prompts	Status
  Claude Desktop	✅	✅	✅	Full Support
  Continue	✅	✅	✅	Full Support
  Cursor	❌	✅	❌	Tools Only

🚀 Getting Started with Installation

To install and set up the airflow-mcp-server, follow these steps:

1. Environment Setup: Ensure that you have all necessary dependencies installed.

2. Configuration: Set environment variables such as AIRFLOW_BASE_URL, AUTH_TOKEN, or COOKIE based on your authentication requirements.

3. Running the Server: Use the provided command to start the server in either safe or unsafe mode.

   airflow-mcp-server --safe  # For read-only operations only
   

   Or:

   airflow-mcp-server         # Default, fully functional mode
   

💡 Key Use Cases in AI Workflows

The airflow-mcp-server can be integrated into various AI workflows to enhance the manageability and automation of Airflow tasks. Two notable use cases include:

1. Automated Data Processing Pipelines: By integrating with Claude Desktop or Continue, developers can automate the setup, execution, and monitoring of complex data processing pipelines within Airflow.
2. Dynamic Task Scheduling: Use Cursor to dynamically schedule and manage tasks based on real-time conditions, ensuring that your Airflow environment remains up-to-date and responsive to changing requirements.

🔌 Integration with MCP Clients

To integrate this server with an AI client such as Claude Desktop or Continue:

1. Setup Authentication: Ensure that the correct authentication method (either AUTH_TOKEN or COOKIE) is provided.

2. Define Server Parameters: Use the correct command and parameters to point the client towards your airflow-mcp-server.

   {
     "mcpServers": {
       "airflow-mcp-server": {
         "command": "uvx",
         "args": ["airflow-mcp-server"],
         "env": {
           "AIRFLOW_BASE_URL": "http://<host:port>/api/v1"
           // Authentication is either via AUTH_TOKEN or COOKIE
           "AUTH_TOKEN": "<base64_encoded_username_password>",
           "COOKIE": "<session_cookie>"
          }
       }
     }
   }
   

📊 Performance & Compatibility Matrix

The performance and compatibility of the server have been rigorously tested with multiple AI clients. The following matrix highlights its capabilities:

Feature	Status
API Support: Full support for all Airflow API tasks.<br>Environment Variables: Optional, but recommended for airgflow plugins.<br>Page Limit Customization: Adjustable via maximum_page_limit.	✔️

🛠️ Advanced Configuration & Security

For advanced users looking to fine-tune their setup:

1. Authentication and Authorization: Carefully configure your authentication settings ensuring that only authorized clients can interact with the Airflow instance.
2. Security Best Practices: Regularly update dependencies, monitor server logs, and follow best practices for securing access points.

❓ Frequently Asked Questions (FAQ)

Here are some common questions developers may have regarding MCP integration:

1. How do I set up authentication?
   • You can use either AUTH_TOKEN or COOKIE, but at least one of these must be provided.
2. Which AI clients support this server?
   • Claude Desktop, Continue, and Cursor are fully supported.
3. Can I modify the default page limit?
   • Yes, you can adjust it via the configuration file (airflow.cfg).
4. Is there a way to test compatibility before use?
   • You can run a quick test by starting the server in safe mode and then using an MCP client to ensure smooth integration.
5. What should I do if I encounter issues with authentication?
   • Double-check that your environment variables are correctly set, especially for AUTH_TOKEN or COOKIE.

👨‍💻 Development & Contribution Guidelines

For developers interested in contributing:

1. Fork the Repository: Start by forking the official repository.
2. Contribute Code: Submit PRs with bug fixes, new features, and improvements to documentation.
3. Join Community: Engage with the community through issues or pull requests.

🌐 MCP Ecosystem & Resources

Stay connected with the broader MCP ecosystem:

• Documentation: Explore detailed guides on integrating systems using Model Context Protocol.
• Community: Join forums and platforms where developers share experiences and best practices.
• Resources: Access a wealth of resources, including tutorials and case studies, to deepen your understanding.

By following these guidelines and leveraging the powerful features of airflow-mcp-server, you can significantly enhance your AI application's capabilities in managing Airflow environments.