This comprehensive course offers a deep dive into MCP (Model Context Protocol), guiding learners from fundamental concepts to advanced applications for building and connecting tools for Large Language Models (LLMs). You’ll start by setting up the MCP environment and exploring the server-client architecture, learning how to connect clients to locally and remotely hosted MCP servers. The course covers key topics such as using MCP servers to interact with local files, make API calls, and integrate with external AI models or web searches. It also explores advanced concepts, including managing memory, deploying solutions via package managers like Git, and working with complex inputs. Special focus is given to remote MCP servers using Streamable HTTP and virtual machines, ensuring learners are equipped to handle both local and remote configurations. Through hands-on examples like building a memory tracker and multi-server solutions, this course provides a robust understanding of MCP tools, resources, prompts, and deployment techniques. By the end, you’ll have the knowledge to build and deploy sophisticated MCP systems, enhancing your ability to work with LLMs and integrate them with other tools effectively.