需要提前安装什么?
需要 Python 环境和 `uv` 工具(推荐),或者直接使用 `uvx` 运行。还需一个正在运行的 Apache Airflow 实例。
@treerootboy
Basic information
No overview available yet
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"airflow-mcp": {
"command": "docker",
"args": [
"compose",
"up",
"-d"
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Airflow MCP 服务器是一个与 Apache Airflow 集成的 Model Context Protocol (MCP) 服务器,提供管理和监控 Airflow DAG 运行的工具。它面向需要以标准化协议交互 Airflow 的开发者或自动化工具。
通过配置 MCP 客户端(如 Claude),在 mcpServers 中添加 airflow 条目,指定命令 uvx 和参数 airflow-mcp,并设置环境变量 AIRFLOW_API_BASE、AIRFLOW_USERNAME 和 AIRFLOW_PASSWORD。也可以使用 uv run 方式在本地开发。
trigger-dag)enable-dag)get-daily-report)list-dags)list-dag-runs)get-dag-status)get-dag-logs)backfill-dag)需要 Python 环境和 `uv` 工具(推荐),或者直接使用 `uvx` 运行。还需一个正在运行的 Apache Airflow 实例。
设置环境变量 `AIRFLOW_API_BASE`(Airflow API 地址,如 `http://localhost:8000/api/v1`)、`AIRFLOW_USERNAME` 和 `AIRFLOW_PASSWORD`。
该服务器本身不指定传输协议;它作为 MCP 服务器运行,通过 MCP 客户端(如 Claude)的 stdio 传输进行交互。
使用 Basic Auth,通过 `AIRFLOW_USERNAME` 和 `AIRFLOW_PASSWORD` 环境变量配置。
所有数据通过 Airflow 的 REST API 从配置的 Airflow 实例获取,服务器本身不持久化数据。

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