Apache Airflow MCP Server

Overview

The Apache Airflow MCP Server implements the Model Context Protocol (MCP) for Apache Airflow, enabling AI assistants and other MCP clients to interact with Airflow instances. This server acts as a bridge between MCP clients and Apache Airflow's REST API, providing a standardized interface for workflow automation tasks.

Features

The server supports a comprehensive set of Airflow operations, including:

• DAG Management: List, get details, pause/unpause, update, and delete DAGs
• DAG Runs: Create, monitor, and manage DAG runs
• Tasks: View task details and manage task instances
• Variables: Create, retrieve, update, and delete Airflow variables
• Connections: Manage database and service connections
• Pools: Configure and manage worker pools
• XComs: Access cross-communication between tasks
• Datasets: Work with Airflow datasets and events
• Monitoring: Check system health and statistics
• Configuration: View Airflow configuration settings

Installation

You can install the Apache Airflow MCP Server using pip:

pip install mcp-server-apache-airflow

For development or the latest version, you can install directly from the repository:

pip install git+https://github.com/yangkyeongmo/mcp-server-apache-airflow.git

Configuration

Before using the server, you need to set up the following environment variables:

export AIRFLOW_HOST=<your-airflow-host>
export AIRFLOW_USERNAME=<your-airflow-username>
export AIRFLOW_PASSWORD=<your-airflow-password>

Running the Server

Once installed, you can run the server directly:

mcp-server-apache-airflow

If you're using uv (a faster Python package installer and resolver):

uvx mcp-server-apache-airflow

Integration with Claude Desktop

To use this MCP server with Claude Desktop, add the following configuration to your claude_desktop_config.json:

{
  "mcpServers": {
    "mcp-server-apache-airflow": {
      "command": "uvx",
      "args": ["mcp-server-apache-airflow"],
      "env": {
        "AIRFLOW_HOST": "https://your-airflow-host",
        "AIRFLOW_USERNAME": "your-username",
        "AIRFLOW_PASSWORD": "your-password"
      }
    }
  }
}

Alternatively, if you prefer using uv with a specific directory:

{
  "mcpServers": {
    "mcp-server-apache-airflow": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/mcp-server-apache-airflow",
        "run",
        "mcp-server-apache-airflow"
      ],
      "env": {
        "AIRFLOW_HOST": "https://your-airflow-host",
        "AIRFLOW_USERNAME": "your-username",
        "AIRFLOW_PASSWORD": "your-password"
      }
    }
  }
}

Docker Deployment

The repository includes a Dockerfile for containerized deployment. To build and run the Docker container:

# Build the Docker image
docker build -t mcp-server-apache-airflow .

# Run the container
docker run -e AIRFLOW_HOST=<your-airflow-host> \
           -e AIRFLOW_USERNAME=<your-airflow-username> \
           -e AIRFLOW_PASSWORD=<your-airflow-password> \
           -p 8000:8000 \
           mcp-server-apache-airflow

Usage Examples

Once the server is running and connected to your Claude Desktop or other MCP client, you can perform operations like:

• Ask Claude to list all your DAGs
• Request information about specific DAG runs
• Trigger new DAG runs with custom configurations
• Check the status of running workflows
• Manage Airflow variables and connections
• Monitor system health and performance

The MCP server handles the translation between natural language requests and the appropriate Airflow API calls, making workflow automation more accessible through conversational interfaces.