Apache Airflow MCP Server

The mcp-server-apache-airflow is a dedicated Model Context Protocol (MCP) server that bridges the gap between AI assistants and Apache Airflow. By exposing Airflow’s RESTful capabilities through a standardized MCP interface, it enables AI agents to query, manipulate, and orchestrate data pipelines without needing bespoke integrations. This abstraction is particularly valuable for developers who rely on AI-driven workflows, as it eliminates the friction of handling raw HTTP calls and authentication details.

At its core, the server wraps Airflow’s official client library, ensuring that every operation—whether listing DAGs, pausing workflows, or inspecting run histories—is performed through a familiar and well-tested API. The MCP server translates generic context requests into Airflow-specific calls, returning structured JSON responses that AI assistants can readily consume. This design not only guarantees compatibility with future Airflow releases but also provides a single, consistent point of contact for all pipeline management tasks.

Key capabilities include comprehensive DAG management (listing, pausing, unpausing, updating, deleting, and source retrieval), as well as robust DAG run handling (creating runs, fetching details, updating status, deleting, clearing, and batch retrieval). The server also supports advanced operations such as patching multiple DAGs simultaneously and re-parsing DAG files, which are essential for dynamic data engineering environments. Each feature is fully documented and exposed via clear API paths, allowing AI agents to construct precise queries or commands.

Real‑world scenarios that benefit from this MCP server abound. A data scientist can ask an AI assistant to "pause the nightly ETL DAG while we deploy a new transformation," and the assistant will translate that into an MCP call that pauses the workflow instantly. An operations engineer might request a "summary of all failed DAG runs in the last 24 hours," prompting the server to fetch and aggregate run data, which the assistant can then present in a concise report. Because the server adheres to MCP standards, these interactions fit seamlessly into larger AI-driven orchestration frameworks, enabling end‑to‑end automation from intent to execution.

What sets this implementation apart is its commitment to reliability and maintainability. By leveraging the official Airflow client, it avoids duplication of logic and stays aligned with Airflow’s evolving feature set. Additionally, the server’s feature table provides transparent visibility into supported operations, helping developers quickly assess whether their use case is covered. For teams looking to embed Airflow control into conversational AI or decision‑support systems, this MCP server offers a plug‑and‑play solution that delivers both power and simplicity.