48 lines
1.3 KiB
Markdown
48 lines
1.3 KiB
Markdown
# airflow-mcp-server: An MCP Server for controlling Airflow
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### Find on Glama
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<a href="https://glama.ai/mcp/servers/6gjq9w80xr">
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<img width="380" height="200" src="https://glama.ai/mcp/servers/6gjq9w80xr/badge" />
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</a>
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## Overview
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A Model Context Protocol server for controlling Airflow via Airflow APIs.
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## Demo Video
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https://github.com/user-attachments/assets/f3e60fff-8680-4dd9-b08e-fa7db655a705
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## Setup
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### Usage with Claude Desktop
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```json
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{
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"mcpServers": {
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"airflow-mcp-server": {
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"command": "uvx",
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"args": [
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"airflow-mcp-server"
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],
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"env": {
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"AIRFLOW_BASE_URL": "http://<host:port>/api/v1",
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"AUTH_TOKEN": "<base64_encoded_username_password>"
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}
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}
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}
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}
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```
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# Scope
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2 different streams in which Airflow MCP Server can be used:
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- Adding Airflow to AI (_complete access to an Airflow deployment_)
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- This will enable AI to be able to write DAGs and just do things in a schedule on its own.
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- Use command `airflow-mcp-server` or `airflow-mcp-server --unsafe`.
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- Adding AI to Airflow (_read-only access using Airflow Plugin_)
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- This stream can enable Users to be able to get a better understanding about their deployment. Specially in cases where teams have hundreds, if not thousands of dags.
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- Use command `airflow-mcp-server --safe`.
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