mcp-server-apache-airflow MCP Server: Seamless Integration with Apache Airflow

Overview: What is mcp-server-apache-airflow MCP Server?

The mcp-server-apache-airflow is a Model Context Protocol (MCP) server implementation designed to enhance and standardize the interaction of AI applications, such as Claude Desktop, Continue, Cursor, and others, with Apache Airflow. By wrapping Apache Airflow's REST API within the MCP framework, this project facilitates seamless, standardized interactions between MCP clients and Apache Airflow. This integration ensures that AI applications can efficiently manage workflows, access data, and leverage the powerful features of Apache Airflow through a unified protocol.

🔧 Core Features & MCP Capabilities

mcp-server-apache-airflow supports comprehensive DAG management, including creating, updating, and deleting Directed Acyclic Graphs (DAGs). It also handles various aspects related to tasks, variables, connections, pools, XComs, datasets, monitoring APIs, config, plugins, providers, event logs, import errors, and system health checks. The server's API paths align with the official Apache Airflow REST API, ensuring compatibility and maintaining data consistency.

DAG Management

• List and manage Directed Acyclic Graphs (DAGs) through endpoints like /api/v1/dags, /api/v1/dags/{dag_id}, etc.
• Support for updating DAGs via /api/v1/dags/{dag_id}
• Ability to pause, unpause, delete, and update DAGs
• Managing DAG runs, tasks, and task instances with endpoints such as /api/v1/dagRuns, /api/v1/tasks
• Status: All features are fully implemented.

Variable Management

• List, create, get, update, and delete variables using /api/v1/variables

Connection Management

• Create, manage, and test connections through /api/v1/connections

Event Logging & Health Checks

• Provide health status, version information, and event logs via /api/v1/events and /api/v1/version.

⚙️ MCP Architecture & Protocol Implementation

This MCP server is designed to adopt the Model Context Protocol (MCP) architecture, enabling seamless integration with AI applications. It utilizes the official Apache Airflow client library to interact with the underlying Airflow instance, ensuring optimal performance and maintainability. The server's API design follows the MCP principles, allowing for consistent and predictable interactions between clients and the backend system.

🚀 Getting Started with Installation

To start using mcp-server-apache-airflow, follow these steps:

1. Install Dependencies: Ensure that apache-airflow-client is installed in your environment.
2. Set Environment Variables: Define AIRFLOW_HOST, AIRFLOW_USERNAME, and AIRFLOW_PASSWORD.
3. Installation via Smithery: Use the @smithery/cli tool to automatically install and configure the server.

Installation via Command Line

To install the MCP server using Smithery, run:

npx -y @smithery/cli install @yangkyeongmo/mcp-server-apache-airflow --client claude

For alternative configurations, use the uvx or uv commands as described in the README.

💡 Key Use Cases in AI Workflows

Data Transformation & Processing

One key use case involves integrating mcp-server-apache-airflow with Claude Desktop for automated data processing. By configuring the server to manage DAGs and tasks, users can create workflows that process data from various sources using Apache Airflow's robust scheduling capabilities.

Real-Time Monitoring

Another use case involves deploying this MCP server in a complex AI system where real-time monitoring is critical. For instance, consider a scenario where Continue tracks and manages multiple DAG runs to ensure that all tasks are completed as expected. The mcp-server-apache-airflow can be used to implement efficient monitoring and alerting mechanisms.

🔌 Integration with MCP Clients

mcp-server-apache-airflow supports the following MCP clients:

• Claude Desktop: ✅ Full Support
• Continue: ✅ Full Support
• Cursor: ❌ Tools Only

To integrate mcp-server-apache-airflow into these applications, ensure that necessary environment variables are properly set and the server is running on the correct configuration.

📊 Performance & Compatibility Matrix

The following table outlines compatibility and performance metrics for various MCP clients:

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

This matrix helps in understanding the extent to which each client can leverage mcp-server-apache-airflow's features.

🛠️ Advanced Configuration & Security

mcp-server-apache-airflow offers advanced configuration options and security measures. To manually start the server, use:

make run

To specify parameters like port or transport type, append relevant arguments as follows:

make run --port 8080 --transport sse

The provided Makefile commands allow for flexibility in both standalone and enterprise-level setups.

❓ Frequently Asked Questions (FAQ)

Q1: How does mcp-server-apache-airflow integrate with existing AI applications?

A1: The server integrates with MCP clients like Claude Desktop through standard API interactions, enabling seamless workflow management and data processing within Apache Airflow.

Q2: Are there any specific security measures for interacting with the server?

A2: Yes, ensure to use secure authentication methods when setting up environment variables such as API_KEY.

Q3: Can this server handle real-time monitoring requirements effectively?

A3: Absolutely. The server supports various endpoints for monitoring DAG runs and task execution in real time.

Q4: What are the steps to contribute to mcp-server-apache-airflow?

A4: Contributions are welcome! Submit a Pull Request via the GitHub repository associated with this project.

Q5: How can I troubleshoot common issues related to MCP client compatibility?

A5: Review the compatibility matrix and ensure that all required environment variables are set correctly when running the server.

👨‍💻 Development & Contribution Guidelines

Contributions to mcp-server-apache-airflow are encouraged. Developers can contribute by submitting pull requests or engaging in discussions on GitHub issues. Follow best practices for coding, testing, and documentation as outlined in the repository's contributing guidelines.

🌐 MCP Ecosystem & Resources

Explore additional resources related to MCP and its ecosystem:

• Model Context Protocol Website - For detailed documentation and updates.
• Smithery Tools - For seamless installation and management of MCP servers.

By integrating mcp-server-apache-airflow into your AI workflows, you can streamline operations and enhance the functionality of AI applications using a standardized protocol.

MCP Protocol Flow Diagram

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

Real-Time Workflow Diagram

graph TD
    subgraph AI Application
        A[AI Application] --> B[MPC Client]
    end
    E[MCP Server] --> F[Data Source/Tool]
    G[Real-Time Monitoring] --> H[DAG Runs] --> I[Task Execution & Data Processing]
    J[Event Logging] --> K[Health Checks]

    style AI Application fill:#fdd8e2
    style MPC Client fill:#baffc9
    style E fill:#dedef8
    style F fill:#fff6cc
    style G fill:#e0ecff
    style H fill:#ffffff
    style I fill:#fefeae
    style J fill:#cde7ee

Real-Time Monitoring Use Case

Scenario: Real-time Data Processing and Notification System for AI Applications

In a high-traffic analytics environment, users can configure mcp-server-apache-airflow to monitor real-time data processing tasks. The server sends notifications via WebSockets or SSE (Server-Sent Events) whenever a DAG run completes successfully or fails.

1. Configuration: Set up the server with correct environment variables for WebSocket/SSE transport type.
2. Monitoring Setup: Use the provided make run-sse command to start the server and set up monitoring endpoints.
3. Notification System: Implement WebSockets/Webhooks from the server to notify AI applications about any changes or issues.

Technology Stack Used

• Apache Airflow for DAG management
• WebSocket/SSE for real-time communication
• MongoDB/PostgreSQL for logging and persistence

Real-Time Event Logging Use Case

Scenario: Continuous Monitoring of Task Execution in Large-scale AI Applications

For large-scale AI applications, continuous monitoring is crucial. By configuring mcp-server-apache-airflow to log events and system health checks, developers can ensure that all tasks execute as expected.

1. Setup: Configure the event logging endpoint using /api/v1/events.
2. Health Checks: Implement regular health check intervals via the /api/v1/version route.
3. Notification System: Set up notification services to alert on any issues detected in real-time.

Configuration Code Sample

{
  "mcpServers": {
    "mcp-apache-airflow-server": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-apacheairflow"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

Frequently Asked Questions (FAQ)

Q1: How can I ensure compatibility for my MCP clients?

A1: Refer to the compatibility matrix and set up required environment variables properly.

Q2: Can this server handle real-time monitoring effectively?

A2: Yes, by integrating WebSocket or SSE communication channels, the server supports seamless real-time monitoring.

Q3: How can I troubleshoot common issues related to MCP client compatibility?

A3: Review the compatibility matrix and ensure all necessary configuration settings are applied correctly.

Q4: What is the process for contributing to this project?

A4: Submit pull requests or engage in discussions on GitHub issues.

Q5: Can you provide more details about the real-time monitoring use case?

A5: Detailed steps include setting up event logging and health check intervals, and configuring notification services.

By following these comprehensive guidelines and leveraging the capabilities of mcp-server-apache-airflow, developers can create robust AI workflows that are both efficient and scalable.