AirTrack

Overview

The AirTrack Model Context Protocol (MCP) server bridges the gap between Apache Airflow and AI‑powered assistants by exposing Airflow’s DAG metadata, run status, and task details through a standardized MCP interface. This eliminates the need for custom adapters or raw REST calls, allowing AI clients such as Claude to query and manipulate Airflow workflows with the same simple, language‑agnostic request format used for other tools. By wrapping Airflow’s official REST API, the server guarantees compatibility with future Airflow releases and leverages existing authentication mechanisms.

Why it matters for developers

Developers who build AI‑augmented operations pipelines can now treat Airflow as a first‑class tool in their conversational agents. Instead of writing bespoke scripts to parse logs or poll task states, an AI assistant can ask for the latest DAG run results, request a rerun of a failed task, or even trigger downstream workflows—all through MCP calls. This streamlines monitoring, debugging, and automation, turning Airflow from a silent scheduler into an interactive component of the AI ecosystem.

Key capabilities

• Standardized DAG discovery – List all available DAGs, including metadata such as schedule interval and owner.
• Run status interrogation – Retrieve the current state, start/finish timestamps, and execution context for any DAG run.
• Task insights – Access individual task instances, their logs, and upstream/downstream relationships.
• Actionable commands – Trigger DAG runs or task retries directly from an MCP client, enabling dynamic workflow control.
• Extensible architecture – Built on the official Airflow client library, it can be extended to support WebSocket streaming of live updates or role‑based access controls in future releases.

Real‑world use cases

• Operational monitoring: A DevOps chatbot can surface the health of critical pipelines and alert on failures without leaving the chat interface.
• Automated remediation: When an AI assistant detects a recurring task failure, it can automatically trigger a rerun or adjust the DAG’s parameters.
• Data science collaboration: Data scientists can ask an assistant to pull the latest results from a training DAG, simplifying reproducibility.
• CI/CD integration: Build pipelines can expose their status to AI agents, enabling on‑call engineers to get instant diagnostics.

Integration flow

1. Deploy Airflow via Docker Compose (or any preferred environment).
2. Run the AirTrack MCP server alongside Airflow; it consumes the Airflow REST API and exposes MCP endpoints.
3. Configure an AI client (e.g., Claude Desktop or OpenWebUI) to point to the MCP server URL.
4. Issue high‑level queries or commands through the AI interface, which are translated into MCP calls that interact with Airflow underneath.

Standout advantages

• Zero‑code client experience – Once the server is running, AI assistants can interact with Airflow without any additional coding.
• Future‑proof design – By adhering to MCP standards, the server remains compatible with new Airflow releases and other MCP‑compliant tools.
• Rapid prototyping – Developers can prototype AI‑driven workflow controls in minutes, accelerating the adoption of intelligent operations.

AirTrack turns Apache Airflow into a conversationally accessible service, empowering AI assistants to monitor, debug, and orchestrate data pipelines seamlessly within the broader MCP ecosystem.