从 Agent Loop 到 生产服务器

关于**模型上下文协议（MCP）**的O’Reilly现场实践培训—— 开放标准（现归Linux基金会代理人工智能基金会管理） 用于将人工智能代理与工具、数据及外部系统连接起来。

课程曾问过*“人工智能的HTTP时刻？”* ——问题是 回答了。MCP 于 2025 年 12 月捐赠给了 Linux 基金会，所有 主要供应商（Anthropic、OpenAI、Google、Microsoft、AWS等）参与其中。就是这样 课程教你以既定标准构建：来自代理人 会循环到已部署、安全的生产服务器。

🎯 什么是MCP？

• 一个协议，多个主机——你在模块01中写入的服务器是 未经修改地被 Claude Desktop、Claude Code 以及 Claude 代理 SDK 所吞， 光标，以及（已部署的）Claude 网页。
• 工具、资源、提示符 — 一流原语，可在 运行时。**MCP 应用（交互式界面）作为首个官方发布 延期于2026年1月。
• 本地或远程 — stdio用于开发，streamable-http （无国籍）用于生产。

📚 弧线 — 6个模块

模块00–03会生成一个产物——个人研究助理——一层 一次一次。

Building AI Agents with MCP

From Agent Loop to Production Servers

A hands-on O’Reilly Live Training on the Model Context Protocol (MCP) — the open standard (now under the Linux Foundation’s Agentic AI Foundation) for connecting AI agents to tools, data, and external systems.

The course used to ask “The HTTP Moment of AI?” — the question has been answered. MCP was donated to the Linux Foundation in Dec 2025 with every major vendor (Anthropic, OpenAI, Google, Microsoft, AWS…) on board. This course teaches you to build with the settled standard: from the agent loop up to deployed, secured, production servers.

🎯 What is MCP?

• One protocol, many hosts — the server you write in module 01 is consumed unmodified by Claude Desktop, Claude Code, the Claude Agent SDK, Cursor, and (deployed) Claude web.
• Tools, resources, prompts — first-class primitives, discoverable at runtime. MCP Apps (interactive UIs) shipped as the first official extension in Jan 2026.
• Local or remote — stdio for development, streamable-http (stateless) for production.

📚 The arc — 6 modules

Modules 00–03 grow one artifact — a personal research assistant — a layer at a time. Module 04 then ships a real product end to end: a shop analytics agent with its own MCP server, deployed as two containers. Module 05 attacks and defends it. Every module ends on “why this is the current way.”

Take-home: demos/exercises/link-checker/. Retired material: demos/archive/.

🚀 Quick start

Every script carries uv inline metadata — no environment juggling:

curl -LsSf https://astral.sh/uv/install.sh | sh
git clone https://github.com/EnkrateiaLucca/mcp-course.git
cd mcp-course

# run anything directly
uv run --with mcp==2.3.0 --with mcp[cli]>=2.3,<3 --with ddgs mcp run ./demos/01-introduction-to-mcp/mcp_server.py

# inspect any MCP server (install the `mcp` CLI once; uv alone doesn't put it on PATH)
uv tool install "mcp[cli]>=2.3,<3"
uv run --with mcp==2.3.0 --with mcp[cli]>=2.3,<3 --with ddgs mcp dev demos/01-introduction-to-mcp/mcp_server.py

Notebook setup (Jupyter kernel)

The scripts need nothing beyond uv. The notebook