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AI-Gateway MCP Server

@Azure-Samples

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基本情報

カテゴリ

その他

ライセンス

MIT

トランスポート

stdio

作者

Azure-Samples

AI-Gateway MCP Server について

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 の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

AI-Gateway MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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

よくある質問

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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