Get hands-on designing secure, intelligent AI agent workflows using the Model Context Protocol (MCP) in this labs-driven course. You’ll see how AI systems connect to external tools, services, and data sources. You’ll learn how those connections can be designed to stay safe and predictable using structured permissions, user prompts, and validation workflows. And in hands-on labs, you’ll build agents that reason, retrieve information, and carry out tasks while maintaining security and control.
Build AI Agents using MCP
您将学到什么
Explain the architecture, components, and use cases of the Model Context Protocol (MCP), and how it differs from traditional APIs and tool calling
Build and run MCP servers using FastMCP, configuring tools, resources, and prompts to support AI applications such as retrieval-augmented generation
Develop MCP clients that connect to single and multiple servers using STDIO and Streamable HTTP for structured, context-aware LLM interactions
Implement secure, interactive MCP workflows by applying sampling, roots, and permission-based user-approval mechanisms for multi-agent applications
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February 2026
作业
10 项作业
授课语言：英语（English）
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本课程是 IBM RAG and Agentic AI 专业证书 专项课程的一部分
在注册此课程时，您还会同时注册此专业证书。
- 向行业专家学习新概念
- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 通过 IBM 获得可共享的职业证书
该课程共有3个模块
In this module, you will gain a hands-on introduction to the Model Context Protocol (MCP). You will explore what MCP is, why it is used, and how it solves challenges compared to traditional APIs and tool-calling approaches. You will examine MCP's architecture, including clients, servers, and transport mechanisms, and see how MCP applications work in practice. Through guided demos and labs, you will connect to existing MCP servers and build your own MCP application.
涵盖的内容
9个视频1篇阅读材料3个作业2个应用程序项目4个插件
In this module, you will learn how to build and enhance MCP servers. You will begin by converting tools into MCP servers and exploring simple "Hello World" examples. You will then extend server functionality with resources, prompts, and tools for real-world applications such as retrieval-augmented generation (RAG). Finally, you will explore MCP transport mechanisms, including streamable HTTP, standard IO, and deprecated SSE, while considering their security and performance trade-offs. Through guided labs, you will build and run MCP servers, connect to them using different transports, and experiment with enhanced capabilities.
涵盖的内容
2个视频3个作业2个应用程序项目2个插件
In this module, you will learn how MCP clients are built and optimized for real-world use. You will examine client architecture, lifecycle management, and performance strategies such as connection pooling, caching, and load balancing. You will also explore advanced features like sampling and root controls to understand bidirectional LLM calls and filesystem boundaries. Finally, through guided labs, you will create custom MCP clients, implement advanced features, and design secure, interactive applications.
涵盖的内容
4个视频2篇阅读材料4个作业3个应用程序项目2个插件
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人们为什么选择 Coursera 来帮助自己实现职业发展
Felipe M.
自 2018开始学习的学生
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Jennifer J.
自 2020开始学习的学生
''我直接将从课程中学到的概念和技能应用到一个令人兴奋的新工作项目中。'
Larry W.
自 2021开始学习的学生
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Chaitanya A.
''学习不仅仅是在工作中做的更好：它远不止于此。Coursera 让我无限制地学习。'
常见问题
This course equips professionals with valuable, hands-on skills used in roles such as MCP Developer, AI Agent Engineer, AI Tool Integration Specialist, Multi-Agent System Developer, and AI Workflow Engineer. It is ideal for software developers, Python programmers, and AI practitioners looking to expand into building and managing MCP-based AI applications. This course is also suitable for professionals reskilling to work on secure, multi-server AI agent systems.
You’ll need familiarity with basic programming skills (Python recommended) and a general understanding of how AI applications interact with tools and data sources. Completing the earlier courses in the IBM RAG and Agentic AI Professional Certificate is highly recommended for smooth progression.
You’ll work with MCP servers and clients, explore FastMCP, STDIO and HTTP transports, ReAct agents, and implement tools, prompts, resources, and user-aware workflows. Labs provide hands-on experience with multi-server interactions, context management, and secure elicitation workflows.
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