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🔄 Migration Notice

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Thank you for your understanding during this transition.

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?”

And more!

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.

🚀 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

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

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

You can also build the MCP server from source, detailed below.

Quick Start - Using the Prebuilt Docker Image

If you have an Airflow instance and want to use our prebuilt Docker image, simply follow these steps:

You’ll need to configure Claude Desktop to connect to your Airflow instance. If you haven’t configured Claude Desktop for use with MCP before, we recommend following the Claude Desktop documentation.

Here are the steps to configure Claude Desktop to connect to your Airflow instance, using our prebuilt Docker image:

• Open Claude Desktop
• Go to Settings → Developer tab
• Edit the MCP config with:

{
   "mcpServers": {
         "airflow_mcp": {
            "command": "docker",