This document details the "Model Context Protocol (MCP) Course," a free educational program developed in partnership by Hugging Face and Anthropic. The course aims to guide participants from beginners to informed practitioners in understanding, utilizing, and building applications with MCP, described as a crucial and evolving topic in AI.

The core methodology of the course revolves around a structured progression from theoretical foundations to practical application and deployment. It is designed to impart knowledge on how AI models, particularly Large Language Models (LLMs), can effectively "leverage external data and tools using the latest MCP standards." This implies MCP defines a standardized interface or framework that enables LLMs to interact with and incorporate information from various external sources and execute actions via external tools, extending beyond their inherent pre-trained knowledge. While the document does not define MCP with specific technical formulas, its purpose is to facilitate the development of AI applications that are dynamic, data-aware, and capable of real-world interaction. This involves aspects such as intelligent data retrieval (e.g., from databases, APIs), tool invocation (e.g., performing calculations, sending emails, interacting with external services), and managing the conversational or operational context within the model's interaction loop.

The course is structured into foundational units, hands-on sessions, and real-world use case assignments, making it a "living project" that incorporates community feedback and contributions.

Course Syllabus:

• Chapter 0: Onboarding: Focuses on setting up the necessary tools and platforms for the course.
• Chapter 1: MCP Fundamentals, Architecture and Core Concepts: Delves into the theoretical underpinnings, architectural design principles, and core conceptual components of Model Context Protocol. It introduces a basic use case to illustrate MCP's application. This unit is critical for understanding the "what" and "why" of MCP, likely covering how models receive, process, and act upon external contextual information.
• Chapter 2: End-to-end Use case: MCP in Action: Guides students in building a complete, shareable MCP application. This chapter moves beyond theory to practical implementation, focusing on integrating the learned concepts into a functional prototype.
• Chapter 3: Deployed Use case: MCP in Action: Concentrates on deploying an MCP application using the Hugging Face ecosystem and services from partner organizations (e.g., Anthropic). This involves considerations for production environments, scalability, and integration within existing AI infrastructure.
• Chapter 4: Bonus Units: Offers supplementary content, focusing on advanced topics or specific integrations with partner libraries and services to enhance MCP implementations.

Prerequisites: Participants are expected to have a basic understanding of AI and LLM concepts, familiarity with software development principles, API concepts, and experience with at least one programming language (Python or TypeScript examples are provided). The course explicitly states that prior completion of the Hugging Face LLM or Agents courses is not strictly necessary if the underlying concepts are understood.

Certification: The course offers two types of free certification:

• Fundamentals Certification: Awarded upon completion of Unit 1, suitable for those primarily interested in understanding the latest trends in MCP.
• Certificate of Completion: Awarded upon completing Units 2 and 3, intended for students aiming to build and deploy full MCP applications.

The recommended pace is approximately one chapter per week, requiring 3-4 hours of work, emphasizing hands-on practice through quizzes and assignments. The course leverages community interaction through Discord for study groups and problem-solving. Authors include Ben Burtenshaw (Hugging Face) and Alex Notov (Anthropic), with acknowledgments to partners like Gradio, Continue, Llama.cpp, and Anthropic for their contributions.