A Model Context Protocol server for controlling Airflow via Airflow APIs.
MCP.Pizza Chef: abhishekbhakat
The airflow-mcp-server is an MCP server that integrates Apache Airflow with the Model Context Protocol, allowing real-time control and interaction with Airflow workflows through its APIs. It facilitates secure, scoped access to Airflow's OpenAPI endpoints, enabling LLMs and AI agents to manage DAGs, trigger tasks, and monitor workflows programmatically within an MCP-enabled environment.
Use This MCP server To
Trigger Airflow DAG runs via natural language commands Monitor Airflow task statuses in real time Fetch Airflow DAG and task metadata for analysis Automate workflow management using LLM-driven agents Integrate Airflow control into AI-enhanced developer tools
README
airflow-mcp-server FAQ
How do I configure the airflow-mcp-server base URL?
Set the base URL to your Airflow root URL without the /api/v2 suffix; the server auto-fetches the OpenAPI spec.
What authentication methods does airflow-mcp-server support?
It supports JWT token authentication to securely access Airflow APIs.
Can airflow-mcp-server be used with multiple LLM providers?
Yes, it is provider-agnostic and works with OpenAI, Claude, Gemini, and others.
How does airflow-mcp-server handle API versioning?
It automatically fetches and adapts to the Airflow OpenAPI spec for the configured version.
Is it possible to monitor Airflow task progress through this server?
Yes, the server exposes task status and metadata for real-time monitoring.
What platforms can run airflow-mcp-server?
It can run on any platform supporting Python and network access to Airflow's API endpoint.
How do I secure communication between the MCP client and airflow-mcp-server?
Use HTTPS and JWT tokens to ensure secure, authenticated communication.
Can I extend airflow-mcp-server to support custom Airflow plugins?
Yes, by modifying the server to expose additional API endpoints or custom logic.