Azure AI Foundry MCP Server

Overview

The MCP Foundry server acts as a bridge between AI assistants and Azure AI Foundry, turning the platform’s rich ecosystem of models, knowledge stores, and evaluation tools into a single, discoverable API surface. By exposing the catalog of available models, deployment primitives, and search‑index operations through MCP, developers can let language agents autonomously browse, prototype, and ship AI solutions without leaving the conversational context. This eliminates repetitive manual steps such as logging into the Azure portal, hunting for the correct SDK calls, or managing authentication tokens—tasks that traditionally break workflow continuity.

At its core, the server offers three broad capability families: Models, Knowledge, and Evaluation. The Models family lets agents list cataloged models, fetch detailed metadata, and even spin up new projects or deploy a chosen model to Azure AI Services. The Knowledge family wraps the AI Search Service, providing tools for index lifecycle management (create, modify, delete), document ingestion and removal, as well as query execution. This means an assistant can dynamically build a domain‑specific search index from scratch, populate it with content, and then use that data to answer user queries in real time. The Evaluation family exposes evaluators that can be invoked to measure text or agent performance, enabling continuous quality monitoring directly from the conversational loop.

Real‑world use cases abound. A data scientist can ask an assistant to “list all available state‑of‑the‑art vision models in Azure AI Foundry Labs,” receive a curated list, and then automatically prototype the chosen model in GitHub using . A product manager might instruct the assistant to “create an index for all PDF contracts in a shared drive,” have the tool fetch local files, add them to an Azure AI Search index, and then query that index whenever a new contract arrives. An operations engineer could employ the evaluation utilities to run periodic tests on deployed agents, ensuring that latency and accuracy remain within SLA thresholds.

Integration into existing AI workflows is seamless. The MCP Foundry server can be registered as a tool set in any Claude‑compatible client, and because it follows the MCP spec, any other AI platform that understands MCP can tap into its capabilities. Agents can chain calls—e.g., first list models, then deploy the selected one, and finally query the new deployment’s health status—without manual intervention. This declarative, scriptable approach empowers rapid experimentation and production‑grade deployment pipelines that stay entirely within the conversational UI.

In summary, MCP Foundry delivers a unified, agent‑friendly interface to Azure AI Foundry’s powerful features. By consolidating model discovery, project provisioning, index management, and evaluation into a single protocol surface, it removes friction for developers and accelerates the journey from idea to deployed AI service.