Connect AI to External Systems - Model Context Protocol

Learn to connect AI/LLMs to external systems using the Model Context Protocol (MCP). This hands-on tutorial guides AI engineers through building MCP servers and clients with Python, Ollama, and Streamlit, solving complex integration challenges with a standardized approach. Build a practical todo list agent.

Updated May 22, 202519 min read

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.