Welcome to the 🤗 Model Context Protocol (MCP) Course
Welcome to the most exciting topic in AI today: Model Context Protocol (MCP)!
欢迎来到当今 AI 领域最令人兴奋的主题：模型上下文协议（MCP）！
This free course, built in partnership with Anthropic, will take you on a journey, from beginner to informed, in understanding, using, and building applications with MCP.
这门免费课程由我们与 Anthropic 合作打造，将带你走完一段从入门到精通的旅程，学会理解 MCP、使用 MCP，并用 MCP 构建应用。
This first unit will help you onboard:
第一个单元将帮你完成入门：
- Discover the course's syllabus.
- Get more information about the certification process and the schedule.
- Get to know the team behind the course.
- Create your account.
- Sign-up to our Discord server, and meet your classmates and us.
了解课程大纲；获取认证流程与时间安排的更多信息；认识课程背后的团队；创建你的账号；加入我们的 Discord 服务器，与同学和讲师见面。
Let's get started!
让我们开始吧！
What to expect from this course?
In this course, you will:
在这门课程中，你将：
- 📖 Study Model Context Protocol in theory, design, and practice.
- 🧑💻 Learn to use established MCP SDKs and frameworks.
- 💾 Share your projects and explore applications created by the community.
- 🏆 Participate in challenges where you will evaluate your MCP implementations against other students'.
- 🎓 Earn a certificate of completion by completing assignments.
📖 从理论、设计与实践三个层面学习模型上下文协议；🧑💻 学习使用成熟的 MCP SDK 与框架；💾 分享你的项目，并探索社区作品；🏆 参与挑战，用你的 MCP 实现与其他同学同台评测；🎓 完成作业即可获得结业证书。
And more!
以及更多内容！
At the end of this course, you'll understand how MCP works and how to build your own AI applications that leverage external data and tools using the latest MCP standards.
课程结束时，你将理解 MCP 的工作原理，并能按照最新的 MCP 标准，构建利用外部数据与工具的自有 AI 应用。
Don't forget to sign up to the course!
别忘了报名参加课程！
What does the course look like?
The course is composed of:
本课程由以下几部分组成：
- Foundational Units: where you learn MCP concepts in theory.
- Hands-on: where you'll learn to use established MCP SDKs to build your applications. These hands-on sections will have pre-configured environments.
- Use case assignments: where you'll apply the concepts you've learned to solve a real-world problem that you'll choose.
- Collaborations: We're collaborating with Hugging Face's partners to give you the latest MCP implementations and tools.
基础单元：从理论层面理解 MCP 概念；动手实践：学习使用成熟的 MCP SDK 构建应用，这部分提供预配置环境；用例作业：把学到的概念用于解决一个你自选的真实问题；合作内容：我们与 Hugging Face 的合作伙伴携手，带来最新的 MCP 实现与工具。
This course is a living project, evolving with your feedback and contributions! Feel free to open issues and PRs in GitHub, and engage in discussions in our Discord server.
这门课程是一个持续生长的项目，会随着你的反馈与贡献不断演进！ 欢迎在 GitHub 上提交 issue 和 PR，也欢迎加入我们的 Discord 服务器参与讨论。
What's the syllabus?
Here is the general syllabus for the course. A more detailed list of topics will be released with each unit.
以下是课程总大纲。更详细的主题清单会随每个单元陆续发布。
| Chapter | Topic | Description |
|---|---|---|
| 0 | Onboarding | Set you up with the tools and platforms that you will use. |
| 1 | MCP Fundamentals, Architecture and Core Concepts | Explain core concepts, architecture, and components of Model Context Protocol. Show a simple use case using MCP. |
| 2 | End-to-end Use case: MCP in Action | Build a simple end-to-end MCP application that you can share with the community. |
| 3 | Deployed Use case: MCP in Action | Build a deployed MCP application using the Hugging Face ecosystem and partners' services. |
| 4 | Bonus Units | Bonus units to help you get more out of the course, working with partners' libraries and services. |
What are the prerequisites?
To be able to follow this course, you should have:
要跟上本课程，你应当具备：
- Basic understanding of AI and LLM concepts
- Familiarity with software development principles and API concepts
- Experience with at least one programming language (Python or TypeScript examples will be shown)
对 AI 与大语言模型概念有基本了解；熟悉软件开发原则与 API 概念；至少掌握一门编程语言（课程示例将使用 Python 或 TypeScript）
If you don't have any of these, don't worry! Here are some resources that can help you:
如果这些你都不具备，也不必担心！以下资源可以帮到你：
- LLM Course will guide you through the basics of using and building with LLMs.
- Agents Course will guide you through building AI agents with LLMs.
LLM 课程会带你了解使用与构建大语言模型的基础；Agents 课程会带你用大语言模型构建 AI 智能体。
TIP
The above courses are not prerequisites in themselves, so if you understand the concepts of LLMs and agents, you can start the course now!
上述课程本身并不是先修要求——只要你已经理解大语言模型与智能体的基本概念，现在就可以开始本课程！
What tools do I need?
You only need 2 things:
你只需要两样东西：
- A computer with an internet connection.
- An account: to access the course resources and create projects. If you don't have an account yet, you can create one here (it's free).
一台能联网的电脑；一个账号，用于访问课程资源与创建项目。如果还没有账号，可以在这里免费注册。
The Certification Process
You can choose to follow this course in audit mode, or do the activities and get one of the two certificates we'll issue. If you audit the course, you can participate in all the challenges and do assignments if you want, and you don't need to notify us.
你可以选择以旁听模式学习本课程，也可以完成各项活动、拿到我们将颁发的两种证书之一。如果只是旁听，你同样可以参加所有挑战、按需完成作业，无需通知我们。
The certification process is completely free:
认证流程完全免费：
- To get a certification for fundamentals: you need to complete Unit 1 of the course. This is intended for students that want to get up to date with the latest trends in MCP, without the need to build a full application.
- To get a certificate of completion: you need to complete the use case units (2 and 3). This is intended for students that want to build a full application and share it with the community.
获得基础认证：需要完成课程第一单元，面向希望跟上 MCP 最新趋势、但不必构建完整应用的学习者；获得结业证书：需要完成用例单元（第二、三单元），面向希望构建完整应用并与社区分享的学习者。
What is the recommended pace?
Each chapter in this course is designed to be completed in 1 week, with approximately 3-4 hours of work per week.
本课程每一章都按一周内完成、每周约 3–4 小时投入来设计。
Since there's a deadline, we provide you a recommended pace:
由于存在截止时间，我们为你提供了一份推荐节奏：
How to get the most out of the course?
To get the most out of the course, we have some advice:
为了让你从课程中获得最大收获，我们有几点建议：
- Do the quizzes and assignments: The best way to learn is through hands-on practice and self-assessment.
- Define a schedule to stay in sync: You can use our recommended pace schedule below or create yours.
2. 完成自测与作业：学习效果最好的方式是动手实践与自我评估。3. 制定学习节奏：你可以使用下面推荐的进度表，也可以自己安排。
Who are we
About the authors:
关于作者：
Ben Burtenshaw
Ben is a Machine Learning Engineer at Hugging Face who focuses on building LLM applications, with post training and agentic approaches. Follow Ben on the Hub to see his latest projects.
Ben 是 Hugging Face 的机器学习工程师，专注于构建 LLM 应用，方向涵盖后训练与智能体方法。可以在 Hub 上关注 Ben，查看他的最新项目。
Alex Notov
Alex is Technical Partner Enablement Lead at Anthropic and worked on unit 3 of this course. Alex trains Anthropic's partners on Claude best practices for their use cases. Follow Alex on LinkedIn and GitHub.
Alex 是 Anthropic 的技术合作伙伴赋能负责人，参与编写了本课程的第 3 单元。Alex 负责培训 Anthropic 的合作伙伴，帮助他们在各自场景中用好 Claude。可以在 LinkedIn 和 GitHub 上关注 Alex。