Overview
What is Airflow MCP?
Airflow MCP is a Model Context Protocol (MCP) server integrated with Apache Airflow, designed to manage and monitor the execution of Airflow's Directed Acyclic Graphs (DAGs).
How to use Airflow MCP?
To use Airflow MCP, configure the server with the necessary Airflow API credentials and run the server using the provided commands. You can trigger DAGs, check their status, and retrieve logs through the API.
Key features of Airflow MCP?
- Trigger specified DAGs to run.
- Enable or disable specific DAGs.
- Retrieve daily reports summarizing DAG runs.
- List all available DAGs.
- Batch retrieve DAG run records.
- Get the status of specific DAG runs.
- Access logs for DAG executions.
- Backfill data for specified date ranges.
Use cases of Airflow MCP?
- Automating the execution of data pipelines.
- Monitoring the health and status of data workflows.
- Generating reports for data processing activities.
FAQ from Airflow MCP?
- Can Airflow MCP manage multiple DAGs?
Yes! Airflow MCP can manage and monitor multiple DAGs simultaneously.
- Is Airflow MCP easy to set up?
Yes! With the provided configuration examples, setting up Airflow MCP is straightforward.
- What programming language is Airflow MCP written in?
Airflow MCP is written in Python.
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