Dear community, Following the thread on Slack [1], initiated by Jason Sebastian Kusuma, I'd like to start an effort to officially support MCP in Airflow's codebase.
*Some background * Model Context Protocol (MCP) is an open standard, open-source framework that standardizes the way AI models like LLM integrate and share data with external tools, systems and data sources. Think of it as a "USB-C for AI" - a universal connector that simplifies and standardizes AI integrations. A notable example of an MCP server is GitHub's official implementation [3], which allows LLMs such as Claude, Copilot, and OpenAI (or: "MCP clients") to fetch pull request details, analyze code changes, and generate review summaries. *How could an MCP server be useful in Airflow?* Imagine the possibilities when LLMs can seamlessly interact with Airflow’s API: triggering DAGs using natural language, retrieving DAG run history, enabling smart debugging, and more. This kind of integration opens the door to a more intuitive, conversational interface for workflow orchestration. *Why do we need to support it officially?* Quid pro quo - LLMs become an integral part of the modern development experience, while Airflow evolves into the go-to for orchestrating AI workflows. By officially supporting it, we’ll enable multiple users to interact with Airflow through their LLMs, streamlining automation and improving accessibility across diverse workflows. All of that is viable with relatively small development effort (see next paragraph). *How should it be implemented?* As of today, there have been several implementations of MCP servers for Airflow API, the most visible one [4] made by Abhishek Bhakat from Astronomer. The efforts of implementing it and maintaining it in our codebase shouldn't be too cumbersome (at least in theory), as we could utilize packages like fastmcp to auto-generate the server using the existing OpenAPI specs. I'd be very happy if Abhishek could share his experience in this thread. *Where else could we utilize MCP?* Beyond the scope of the public API, I could also imagine using it to communicate with Breeze. *How do we proceed from here?* Feel free to share your thoughts here in this discussion. If there are no objections, I'll be happy to start working on an AIP. Sincerely, Shahar Epstein *References:* [1] Slack discussion, https://apache-airflow.slack.com/archives/C06K9Q5G2UA/p1746121916951569 [2] Introducing the model context protocol, https://www.anthropic.com/news/model-context-protocol [3] GitHub Official MCP server, https://github.com/github/github-mcp-server [4] Unofficial MCP Server made by Abhishek Hakat, https://github.com/abhishekbhakat/airflow-mcp-server
