Rakesh-infosrc/mcp-airflow-openwebui

AirTrack

A Model Context Protocol (MCP) server for Apache Airflow that enables standardized access to DAG metadata, run status, and task insights, allowing seamless integration with MCP clients for monitoring and automation.

About

This project implements a Model Context Protocol server that wraps Apache Airflow's REST API, allowing MCP clients to interact with Airflow in a standardized way. It uses the official Apache Airflow client library to ensure compatibility and maintainability.

Project Structure

combined_project/
├── airflow/           # Airflow project files
│   ├── dags/         # Airflow DAG definitions
│   ├── logs/         # Airflow logs
│   ├── plugins/      # Airflow plugins
│   └── Docker-compose.yaml  # Docker compose file for Airflow
│
└── mpc/              # MPC application files
    ├── utils/        # Utility functions
    ├── server.py     # Main server file
    └── main.py       # Entry point

Running the Projects

Requirements

• Docker and Docker Compose for Airflow
• Python 3.8+ for MPC application
• Virtual environment for MPC application

Airflow

1. Navigate to the airflow directory:

   cd airflow
   

2. Start Airflow using Docker Compose:

   docker-compose up 
   

3. Access the Airflow web interface at http://localhost:8181

   Username: admin Password: airflow

MPC Application

1. Navigate to the mpc directory:

   cd mpc
   

2. Create and activate a virtual environment:

   python -m venv .venv
   .venv\Scripts\activate  # On Windows
   source .venv/bin/activate  # On Unix/MacOS
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Run the MPC server:

   python server.py
   

Usage with Claude Desktop

{
  "mcpServers": {
    "FlowPredictor": {
   "command": "D:\\Apps\\conda\\Scripts\\uv.EXE",
   "args": [
     "run",
     "--with",
     "mcp[cli]",
     "mcp",
     "run",
     "<---PATH OF YOUR SERVER FILE eg(C:\\Users\\..\\..\\..\\server.py) --->"
   ]
 }
   }
}

Integration

The Airflow DAGs can interact with the MPC application through API calls. Make sure both services are running when executing workflows that require MPC functionality.

Future Development

• 🔄 Live Updates – Stream DAG/task status via WebSocket or SSE.

• 🔐 Security – Add OAuth2, API keys, and role-based access.

• ⚡ Event Triggers – Auto-trigger agents on DAG events.

• A📊 Analytics – Dashboard for DAG performance and trends.

• 🤖 AI Troubleshooting – Use LLMs for issue analysis and fixes.

• Integrate with OpenWebUi

• 1. install MCPO

  pip install mcpo
  

2.create config.js in mcp folder

{
  "mcpServers": {
   "airflow-mcp-server": {
      "command": "C:\\Users\\RakeshReddyBijjam\\pipx\\venvs\\meltano\\Scripts\\uv.EXE",
      "args": [
        "run",
        "--with",
        "mcp[cli]",
        "mcp",
        "run",
        "C:\\Users\\RakeshReddyBijjam\\Desktop\\claude_sam\\AirTrack\\mcp\\server.py"
      ]
    }
  }
}

3. Run the server

   uvx mcpo --config config.json --port 8001