Model Context Protocol (MCP): Fundamentals and Real-World Server Examples

The **Model Context Protocol (MCP)** is rapidly emerging as a standard for connecting AI models to external tools, data sources, and workflows in a secure, extensible, and interoperable way. By acting as a bridge between large language models and the systems they need to interact with, MCP enables developers to extend model capabilities while preserving governance and context control. In this session, we’ll cover the **fundamentals of the MCP Server**—its purpose, architecture, and core concepts including schema, capabilities, request/response patterns, and security considerations. We’ll also explain how MCP fits into the broader AI developer ecosystem, including its integration with editors like VS Code, GitHub Copilot, and enterprise platforms such as Microsoft Fabric and Azure AI. To make the concepts real, we’ll walk through **examples of MCP servers you can use today**, such as: * **File System MCP Server** – enabling safe, contextual access to local or cloud file structures. * **SQL Database MCP Server** – providing query execution and schema insights from relational databases. * **GitHub MCP Server** – integrating with GitHub repositories for issues, pull requests, and code navigation. * **Azure DevOps MCP Server** – connecting to project tracking and ticketing systems for AI-assisted DevOps. * **Fabric/OneLake MCP Server** (emerging) – exposing structured data and governance signals directly to AI agents. By the end of this 1-hour session, attendees will understand how MCP servers work, where they fit in enterprise AI architectures, and how to get started with available open-source and enterprise-ready implementations to build their own AI-augmented workflows.