About Azure
Azure is an MCP server for integrating Azure APIs with GenAI applications. It supports multiple authentication methods, asynchronous operations, rate limiting, and error retries.
Key features
- 01Integrate Azure APIs
- 02Support multiple authentication methods
- 03Apply rate limiting
- 04Handle errors with retry logic
- 05Support asynchronous operations
Use cases
- 01Call Azure APIs from GenAI applications
- 02Configure authentication for Azure API requests
- 03Handle API rate limits and transient errors
MCP support
The MCP server provides Azure API integration for GenAI applications.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
uvx azure-mcp-serverAZURE_SUBSCRIPTION_IDAzure Subscription IDAZURE_CLIENT_IDSecretAzure Client IDAZURE_CLIENT_SECRETSecretAzure Client SecretAZURE_TENANT_IDAzure Tenant IDLocal servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEAzure MCP Server
A Model Context Protocol (MCP) server for integrating Azure with GenAI applications.
Overview
Microsoft Azure cloud services integration
Features
- Comprehensive Azure API coverage
- Multiple authentication methods
- Enterprise-ready with rate limiting
- Full error handling and retry logic
- Async support for better performance
Installation
pip install azure-mcp-server
Or install from source:
git clone https://github.com/asklokesh/azure-mcp-server.git
cd azure-mcp-server
pip install -e .
Configuration
Create a .env file or set environment variables according to Azure API requirements.
Quick Start
from azure_mcp import AzureMCPServer
# Initialize the server
server = AzureMCPServer()
# Start the server
server.start()
License
MIT License - see LICENSE file for details
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.