Add MCP-Airflow-API server to community-developed servers list in REA…

[call518]

…DME.md

• Added a new entry for MCP-Airflow-API, which integrates Apache Airflow API with MCP.
• Affected file: README.md

Description

This PR introduces a new MCP server implementation — MCP-Airflow‑API.

Server Details

• Server: Github(https://github.com/call518/MCP-Airflow-API) or Pypi(https://pypi.org/project/mcp-airflow-api/)
• Changes to: servers list in the README; integration of a new MCP server/tool entry.

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

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to change)
• Documentation update

Checklist

• I have read the MCP Protocol Documentation
• My changes follows MCP security best practices
• I have updated the server's README accordingly
• I have tested this with an LLM client
• My code follows the repository's style guidelines
• New and existing tests pass locally
• I have added appropriate error handling
• I have documented all environment variables and configuration options

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.