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The azure-aigateway skill helps you configure Azure API Management as a centralized AI gateway for AI models, MCP tools, and agents. Use it to set up routing, load balancing, authentication, and rate limiting for AI traffic across multiple backends.
Skill azure-aigateway | Source code
What it provides
You get guidance to configure Azure API Management as a centralized AI gateway that handles routing, load balancing, authentication, and rate limiting for AI traffic across multiple backends — including Azure OpenAI endpoints, custom model deployments, and MCP tool servers.
Prerequisites
- Azure subscription: Create a free account if you don't have one.
- AI assistant with Azure Skills: GitHub Copilot for Azure, Visual Studio Code with Azure MCP extension, Claude Code, or another compatible MCP client.
- Azure CLI (v2.60.0+): Install and sign in with
az login.
When to use this skill
Use this skill when you need to:
- Work with semantic caching, token limit, content safety, and load balancing
- Work with AI model governance, MCP rate limiting, and jailbreak detection
- Add Azure OpenAI back end
- Add AI Foundry model
- Test AI gateway in Azure
- Work with LLM policies
- Configure AI back end in Azure
- Work with token metrics, AI cost control, convert API to MCP, and import OpenAPI to gateway
Example prompts
Try these prompts to activate this skill:
- "Set up semantic caching for my AI gateway"
- "Configure token limits for Azure OpenAI"
- "Add content safety policies to my gateway"
- "Set up load balancing across AI backends"
- "Configure jailbreak detection"
- "Add an Azure OpenAI backend to my gateway"
- "Import OpenAPI spec to my AI gateway"
- "Configure rate limiting for MCP tools"
- "Set up AI cost control policies"
- "Convert my API to MCP"