Add MCP-Airflow-API server to community-developed servers list in REA…#2555
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domdomegg merged 4 commits intomodelcontextprotocol:mainfrom Aug 17, 2025
call518:dev-airflow
Merged
Add MCP-Airflow-API server to community-developed servers list in REA…#2555domdomegg merged 4 commits intomodelcontextprotocol:mainfrom call518:dev-airflow
domdomegg merged 4 commits intomodelcontextprotocol:mainfrom
call518:dev-airflow
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…DME.md - Added a new entry for MCP-Airflow-API, which integrates Apache Airflow API with MCP. - Affected file: README.md
domdomegg
previously approved these changes
Aug 17, 2025
domdomegg
approved these changes
Aug 17, 2025
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…DME.md
Description
This PR introduces a new MCP server implementation — MCP-Airflow‑API.
Server Details
Motivation and Context
MCP-Airflow‑API is Model Context Protocol (MCP) server for Apache Airflow API integration. Provides comprehensive tools for managing Airflow clusters including service operations, configuration management, status monitoring, and request tracking.
How Has This Been Tested?
Deployed the MCP-Airflow-API server locally and validated its interaction with an MCP client (e.g., Claude Desktop or a simple TypeScript/Python test harness).
Tested core functionalities including service operations, configuration retrieval, status monitoring, and request tracking to ensure compliant MCP interactions.
Confirmed correct behavior over both standard input/output and Streamable HTTP transports, following MCP specification.
Verified that pip installation works (pip install mcp-airflow-api)
Confirmed compatibility with Python 3.11+ environments as stated on PyPI
Breaking Changes
No breaking changes introduced — this is an additive change. Existing MCP clients will not be affected.
Types of changes
Checklist
Additional context
Implementation details: The Airflow‑API server interfaces with Apache Airflow via HTTP REST endpoints to perform cluster management operations. It translates Airflow responses into MCP-protocol adherent format.
Configuration options: Host, port, authentication credentials, cluster name for Airflow connections are configurable via environment variables (e.g., AIRFLOW_API_URL, AIRFLOW_API_USERNAME, AIRFLOW_API_PASSWORD, AIRFLOW_LOG_LEVEL).
pip install mcp-airflow-api
Python Compatibility: Requires Python 3.11 or higher PyPI.
Publishing on PyPI enables straightforward distribution and improvements in user experience and adoption.