AI Gateway

The Ai Gateway MCP server is designed to bridge the gap between cutting‑edge AI models and production‑grade API management. It addresses a core problem faced by modern developers: how to expose sophisticated AI services—such as Azure OpenAI, Azure AI Foundry, or custom LLMs—to downstream applications while retaining full control over security, governance, and cost. By leveraging the Model Context Protocol, Ai Gateway lets an AI assistant publish a rich set of capabilities (resources, tools, prompts, sampling) that can be consumed by client applications in a declarative, discoverable way.

At its heart, the server aggregates AI endpoints and presents them as a single, well‑defined MCP interface. Developers can then compose complex workflows—ranging from simple text generation to multimodal, real‑time audio interactions—without worrying about the underlying authentication or rate‑limiting mechanics. The gateway injects Azure API Management features such as OAuth2, JWT validation, and policy‑based throttling directly into the MCP flow, ensuring that every call is auditable and compliant with organizational policies. This tight coupling of AI logic and API governance gives teams confidence that production deployments will remain secure, reliable, and cost‑controlled.

Key capabilities include:

• Resource discovery: Clients can query the gateway to learn which AI models, data sources, or tools are available, along with their metadata and usage limits.
• Tool integration: Built‑in support for MCP tools lets an assistant invoke external services (e.g., a database query or a custom REST API) as part of the same conversational context.
• Prompt orchestration: The server can pre‑define prompt templates and sampling strategies, enabling consistent output quality across different clients.
• Real‑time streaming: With Azure OpenAI Realtime integration, the gateway can handle low‑latency audio and text streams, making it suitable for voice assistants or live chatbots.
• Client‑side authorization: Labs demonstrate a flow where the MCP client authenticates and receives scoped tokens, ensuring that only authorized users can access privileged AI capabilities.

In practice, Ai Gateway shines in scenarios such as enterprise chatbots that need to pull data from internal services, real‑time customer support agents that combine LLM responses with live call audio, or automated content generation pipelines that enforce brand guidelines through prompt templates. By exposing these workflows via MCP, developers can compose agents using standard tools (OpenAI Agents SDK, Azure AI Foundry) while still benefiting from the gateway’s robust policy engine.

For developers already familiar with MCP, Ai Gateway offers a turnkey solution that marries the flexibility of model‑centric design with the operational discipline of API management. Its unique blend of real‑time capabilities, policy integration, and tool orchestration positions it as a powerful pattern for building secure, scalable, and maintainable intelligent applications.