Model Context Protocol in Python: Building Clients and Servers

Connecting large language models to external data and internal tools is an essential skill in modern AI engineering. This text-based course guides you through the Model Context Protocol (MCP), showing you how to standardize and scale AI interactions with external environments. You will build a practical understanding of client-server architecture in AI workflows, learning how to expose tools, resources, and prompts systematically while maintaining robust LLMOps practices. What you'll learn: - Understand core Model Context Protocol concepts, specifications, and data flows - Build custom MCP servers in Python to expose tools and structured data - Create MCP clients capable of orchestrating context and executing external actions - Apply modern Python techniques including async programming and type hints - Configure secure tool execution boundaries and structured error handling - Practice foundational LLMOps patterns for testing and monitoring model integrations The course starts with essential terminology, architectural principles, and communication formats before advancing into detailed written code walkthroughs and implementation exercises. This course is designed for developers, data professionals, and curious beginners looking to expand their generative AI toolkit. Basic familiarity with Python is helpful, but no prior experience with MCP or advanced LLMOps is required. Begin reading to master standardized LLM integrations and start building your own MCP services today.