Model Context Protocol in Practice

Before writing any code, it helps to see what a finished integration looks like. Consider a desktop AI chat client with an MCP configuration file that adds a server such as an "Azure MCP Server." After pasting in a small JSON configuration and restarting the client, the conversation's tool list suddenly includes a whole set of new capabilities pulled from that server — in this example, one of them is Azure AI Search.

When the user asks the assistant to describe the contents of a specific search index, the client shows a "tool call" card containing the request and response for the tool the model chose to invoke (get_search_index, for example). Interestingly, calling that tool triggers a native sign-in dialog — not a browser popup, but an authentication prompt handled directly inside the desktop client. After signing in, the model receives real information about the search index and answers the question.

The practical goal of this material is to build exactly this kind of integration end-to-end: an MCP server that exposes tools, resources, and prompts backed by real enterprise infrastructure, then secure it with OAuth so it can be safely exposed as a remote server.