Chatbots answer questions and copilots offer suggestions. Enterprise AI agents, however, must take action: query databases, create tickets, read messages, or trigger deployments. Building every integration as a custom connector quickly becomes complex, fragile, and difficult to secure.

The Model Context Protocol (MCP) provides an open, standardized way for AI applications to connect with tools, data, and services—much like USB-C standardized connections between devices.

Through architecture walkthroughs and a live demonstration, we’ll examine MCP’s host-client-server model, connection lifecycle, capability negotiation, and its standard transports: stdio and Streamable HTTP. You’ll learn how Tools, Resources, and Prompts enable agents to move from conversation to action, and where features such as elicitation and structured outputs fit.

We’ll then address the production concerns that matter most: OAuth-based authorization, least-privilege access, user consent, prompt injection, tool poisoning, and data exfiltration.

You’ll leave with a practical framework for deciding when MCP is appropriate, designing secure integrations, and connecting enterprise agents to platforms such as Azure and Microsoft 365 without creating another collection of custom connectors.