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Intro to Model Context Protocol (MCP)

Intro to Model Context Protocol (MCP)

本课程是多个项目的一部分。

位教师：Maham Codes

5,877 人已注册

包含在中

了解更多

1个模块

深入了解一个主题并学习基础知识。

16 条评论

中级等级

推荐体验

1 小时完成

灵活的计划

自行安排学习进度

1个模块

深入了解一个主题并学习基础知识。

16 条评论

中级等级

推荐体验

1 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Connect AI models to tools and data using Model Context Protocol.
Set up and run an MCP server with defined tools and resources.
Build MCP clients that communicate with the server.

您将获得的技能

您将学习的工具

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

1 项作业

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

此课程作为的一部分提供

在注册此课程时，您还需要选择一个特定的合作项目。

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
获得可共享的职业证书

该课程共有1个模块

MCP (Model Context Protocol) lets developers securely connect tools, APIs, and data sources to AI models like Claude, GPT, or Gemini. With MCP, your AI isn’t just a chatbot—it becomes a powerful, action-taking agent that can interact with the outside world.

MCP is an open protocol by Anthropic that bridges the gap between language models and real-world context. It standardizes how models access resources, tools, and prompts. Whether you’re building local dev tools or production AI apps, MCP ensures your model always has the right context at the right time. MCP is perfect for developers who want to build AI agents that take meaningful actions, fetch live data from APIs, files, or databases, connect LLM models to IDE plugins or command-line tools, develop composable, multi-server AI workflows, and debug and inspect AI integrations with ease. What you'll build: In this hands-on course, you’ll build your own Weather MCP Server in Node.js/TypeScript and add Tools, Resources, and Prompts. You'll also connect your server to MCP Client (Claude Desktop and MCP Inspector) and see your AI agent fetch live responses in real time!