Connect AI to External Systems - Model Context Protocol

You've learned how to build agentic workflows and autonomous agents capable of complex reasoning and planning. However, for these AI systems to be truly effective in real-world scenarios, they need interact reliably with external systems - databases, APIs, file systems, and various specialized tools. Connecting these components individually often leads to brittle, hard-to-maintain integrations, creating a significant bottleneck known as the "M x N" problem where every new AI application might require custom code for every tool it needs.

This tutorial introduces the Model Context Protocol (MCP)¹, an open standard designed to solve this integration chaos. We will move beyond theoretical concepts and dive into practical implementation. You will build both an MCP server, acting as a bridge to a simple task management system, and an MCP client integrated into a Streamlit application, allowing a local LLM (via Ollama) to manage a todo list by communicating through the standardized MCP interface.