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AI-Gateway MCP Server

@Azure-Samples

代码仓库主页

基本信息

分类

其他

许可证

MIT

传输方式

stdio

作者

Azure-Samples

关于 AI-Gateway MCP Server

Labs to explore AI Models, MCP servers, and Agents with the AI Gateway powered by Azure API Management and Microsoft Foundry 🚀

#azure#mcp#openai#agents#foundry#autogen#apimanagement#openai-api#genai#mcp-server

AI-Gateway MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "AI-Gateway": {
      "command": "uv",
      "args": [
        "sync"
      ]
    }
  }
}

AI-Gateway MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is AI Gateway?

AI Gateway is an enterprise-grade gateway for managing AI models, tools, and agents, powered by Azure API Management. It provides security, performance, observability, cost control, and extensibility (including MCP protocol support) for production AI applications. The repository offers 30+ hands-on Jupyter notebook labs, Bicep infrastructure templates, and APIM policies.

How to use AI Gateway?

Clone the repository, install Python 3.12+ and uv, open the notebooks in VS Code with the Jupyter extension, and follow step-by-step instructions. Each lab deploys to your Azure subscription using provided Bicep templates and APIM policies.

Key features of AI Gateway

  • 🔐 Security via OAuth 2.0, managed identities, and content safety filtering
  • ⚡ Performance with load balancing, semantic caching, and request routing
  • 📊 Observability through token metrics, built-in logging, and tracing
  • 💰 Cost control using rate limiting, quota management, and FinOps framework
  • 🔌 Extensibility with MCP protocol support, function calling, and multi-model routing
  • 🏛️ Labs follow Azure Well-Architected Framework pillars (security, reliability, performance, operations, cost)

Use cases of AI Gateway

  • Manage and control access to Large Language Models with enterprise-grade policies
  • Enable secure tool access using MCP protocol and function calling capabilities
  • Build and control agentic applications with orchestration frameworks
  • Implement token rate limiting, semantic caching, and backend pool load balancing
  • Automate failover between AI models using circuit breaker and retry patterns

常见问题

1

What dependencies and runtime requirements does AI Gateway need?

You need Python 3.12+, uv (Python package manager), VS Code with Jupyter extension, an Azure subscription with Contributor + RBAC Administrator roles, and the Azure CLI authenticated to your subscription.

2

Where does AI Gateway data live?

All infrastructure and data are deployed to your own Azure subscription. No data leaves your controlled environment.

3

What authentication and authorization mechanisms are supported?

AI Gateway supports OAuth 2.0, managed identities, and client authorization flows for MCP tools.

4

What is AI Gateway's known limitation?

The README does not explicitly state limitations. Labs assume an Azure subscription and may incur costs; cleanup notebooks are provided.

5

How does AI Gateway compare to other AI gateway alternatives?

AI Gateway is built on Azure API Management, offering a centralized governance layer for security, safety, cost controls, resiliency, observability, and governance across models, tools, and agents.

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