Airflow MCP Server – Control Apache Airflow with AI Assistants

The Airflow MCP Server turns a running Airflow instance into a first‑class AI‑controlled tool. By exposing the full Airflow REST API through the Model Context Protocol, Claude and other AI assistants can discover, query, and manipulate workflows as if they were native language commands. This solves the common pain point of needing to manually open a browser or use for every Airflow operation, enabling developers to orchestrate complex data pipelines through natural language or scripted prompts.

At its core, the server retrieves Airflow’s OpenAPI specification () and translates it into MCP resources, tools, and prompts. It then presents these as callable actions to the AI client: “list DAGs”, “trigger a DAG run”, or “fetch task logs”. The server handles authentication automatically via JWT tokens, the only supported method in Airflow 3.0, and ensures that the AI’s requests are routed correctly to the underlying Airflow endpoints without exposing credentials or internal URLs.

Key capabilities include:

• Safe and Unsafe Modes: Run the server in a read‑only mode () to audit or monitor pipelines, or enable full write access (, the default) for dynamic orchestration.
• Dynamic Pagination: Automatically respects Airflow’s page limits, configurable through in , preventing over‑fetching while keeping responses concise.
• OpenAPI Driven Tool Generation: Every Airflow endpoint becomes a discoverable tool with rich descriptions, enabling the AI to suggest appropriate actions based on context.
• JWT Authentication Integration: Seamlessly injects the required token into each request, keeping security tight and compliant with Airflow 3.0’s authentication model.

Real‑world scenarios illustrate its value: a data engineer can ask Claude to “list all DAGs that run on the last Friday” and receive an instant, accurate list; a DevOps team can trigger a DAG run from a chat interface during incident response; or an analytics platform can schedule periodic jobs by instructing the AI to “run the sales report DAG tomorrow at 2 AM”. In each case, the MCP server bridges the gap between natural language intent and Airflow’s powerful orchestration engine.

By integrating directly into AI workflows, the Airflow MCP Server empowers developers to embed pipeline control within conversational agents, automated scripts, or custom applications. It abstracts the complexity of Airflow’s REST API while preserving full control and security, making it a standout tool for any team looking to modernize data engineering operations with AI.