Airflow MCP

This project implements an MCP server for Apache Airflow, enabling users to interact with their orchestration platform using natural language.

With a few minutes of setup, you should be able to use Claude Desktop or any MCP-enabled LLM to ask questions like:

• "What DAGs do we have in our Airflow cluster?"

• "What is our latest failed DAG?"

• "Show me all versions of the transform_sales_aggregator DAG"

• "Which assets are produced by our load DAGs?"

About MCP and Airflow MCP

The Model Context Protocol (MCP) is an open standard creating secure connections between data sources and AI applications. This repository provides a custom MCP server for Apache Airflow that transforms how teams interact with their orchestration platform through natural language.

Supports Airflow 2 and Airflow 3. Set airflow_version=3 in your environment to enable the Airflow 3 API (/api/v2) and JWT authentication automatically.

Related MCP server: Airflow MCP

🚀 Features

• Query pipeline statuses through natural language

• Troubleshoot DAG failures efficiently

• Retrieve comprehensive DAG information

• Trigger DAGs based on their status

• Monitor execution results

• Analyze DAG components and configurations

• Browse Airflow variables, pools, and connections

• Inspect task instances for any DAG run

• Airflow 3: DAG version history, Asset (Dataset) catalog

🛠️ Getting Started

Prerequisites

If you already have an Airflow instance and want to use our prebuilt Docker image, you only need:

• Docker

• Access to your Apache Airflow instance (Airflow 2 or Airflow 3)

• LLM access (Claude, ChatGPT, or AWS Bedrock)

This repository also provides a local setup for Apache Airflow, which you can use for demo purposes.

Quick Start - Using the Prebuilt Docker Image

Configure Claude Desktop to connect to your Airflow instance. If you haven't configured Claude Desktop for use with MCP before, follow the Claude Desktop documentation.

Airflow 2:

{
  "mcpServers": {
    "airflow_mcp": {
      "command": "docker",
      "args": ["run", "-i", "--rm",
               "-e", "airflow_base_url",
               "-e", "airflow_username",
               "-e", "airflow_password",
               "-e", "airflow_version",
               "pondered/airflow-mcp:latest"],
      "env": {
        "airflow_base_url": "http://host.docker.internal:8088",
        "airflow_username": "airflow",
        "airflow_password": "airflow",
        "airflow_version": "2"
      }
    }
  }
}

Airflow 3 (uses /api/v2 and JWT authentication automatically):

{
  "mcpServers": {
    "airflow_mcp": {
      "command": "docker",
      "args": ["run", "-i", "--rm",
               "-e", "airflow_base_url",
               "-e", "airflow_username",
               "-e", "airflow_password",
               "-e", "airflow_version",
               "pondered/airflow-mcp:latest"],
      "env": {
        "airflow_base_url": "http://host.docker.internal:8080",
        "airflow_username": "airflow",
        "airflow_password": "airflow",
        "airflow_version": "3"
      }
    }
  }
}

Running MCP Locally with Claude Desktop

1. Clone this repository:

   git clone https://github.com/ponderedw/airflow-mcp

2. If you don't have a running Airflow environment, start one with:

   just airflow

   This will start an Airflow instance on port 8088, with username airflow and password airflow.

   You can access Airflow at http://localhost:8088/ and see multiple DAGs configured:

   

   These DAGs have complex dependencies, some running on a schedule and some using Airflow's Dataset/Asset functionality.

3. Configure Claude Desktop as shown in the Quick Start section above.

4. Test your setup by asking Claude: "What DAGs do we have in our Airflow cluster?"

Environment Variables

🔧 Available Tools

Read tools (always enabled)

Write tools (require POST_MODE=true)

Airflow 3 only (enabled automatically when airflow_version=3)

Schedule Insights tools (require AIRFLOW_INSIGHTS_MODE=true)

Requires the airflow-schedule-insights plugin installed in your Airflow environment.

📝 Example Usage

• "What DAGs do we have in our Airflow cluster?"

• "Identify all DAGs with failed status in their most recent execution and trigger a new run for each one"

• "What operators are used by the transform_forecast_attendance DAG?"

• "Has the transform_forecast_attendance DAG ever completed successfully?"

• "Show me all versions of the load_ticket_sales DAG and what changed"

• "Which assets does the load_box_office_reports DAG produce?"

• "What variables are configured in Airflow?"

Running MCP with LangChain

You can use from langchain_mcp_adapters.client import MultiServerMCPClient to add Airflow MCP as one of your tools in a LangChain app.

Option 1: Separate Container (SSE Transport)

mcp_host = os.environ.get('mcp_host', 'mcp_sse_server:8000')
mcps = {
    "AirflowMCP": {
        "url": f"http://{mcp_host}/sse",
        "transport": "sse",
        "headers": {"Authorization": f"Bearer {os.environ.get('MCP_TOKEN')}"}
    }
}

Option 2: Embedded Server (STDIO Transport)

Install the library first: pip install airflow-mcp-ponder

mcps = {
    "AirflowMCP": {
        'command': "python",
        'args': ["-m", "airflow_mcp_ponder.mcp_airflow"],
        "transport": "stdio",
        'env': {k: v for k, v in {
            'AIRFLOW_ASSISTENT_AI_CONN': os.getenv('AIRFLOW_ASSISTENT_AI_CONN'),
            'airflow_base_url': os.getenv('airflow_base_url'),
            'airflow_username': os.getenv('airflow_username'),
            'airflow_password': os.getenv('airflow_password'),
            'airflow_version': os.getenv('airflow_version'),
            'AIRFLOW_INSIGHTS_MODE': os.getenv('AIRFLOW_INSIGHTS_MODE'),
            'POST_MODE': os.getenv('POST_MODE'),
            'TRANSPORT_TYPE': 'stdio',
        }.items() if v is not None}
    }
}

Using the Tools

client = MultiServerMCPClient(mcps)
tools = await client.get_tools()

🤝 Contributing

We enthusiastically invite the community to contribute to this open-source initiative! Whether you're interested in:

• Adding new features

• Improving documentation

• Enhancing compatibility with different LLM providers

• Reporting bugs

• Suggesting improvements

Please feel free to submit pull requests or open issues on our GitHub repository.

🔗 Links

• GitHub Repository

• Docker Repository