在人工智能向“自主智能体”演进的道路上，我们正见证一个关键的范式转移：大型语言模型（LLM）不再仅仅是文本生成器，而是逐渐成为能感知环境、调用工具并执行复杂任务的智能核心。然而，传统的“文本输入-文本输出”模式，在面对需要精密逻辑、实时数据可视化与复杂业务流操控的生产力场景时，其交互深度的局限性暴露无遗。为了连接模型与广阔的外部世界，模型上下文协议（Model Context Protocol, MCP） 应运而生，旨在标准化模型与工具之间的通信，降低生态适配成本。

一、MCP 的基石与交互瓶颈

MCP 通过宿主（Host）、客户端（Client）与服务器（Server）的三层架构，实现了关注点的分离与协议的标准化。在其基础模型中，工具（Tools）通常返回结构化数据或静态 Markdown 文本。虽然这解决了功能调用的问题，但在处理如多维财务报表、交互式工程图纸或实时监控仪表盘等任务时，用户体验出现了断层——智能体只能“描述”数据，用户无法直接“操作”数据。这种交互深度的缺失，成为提升 AI 生产力的主要障碍。

二、MCP Apps：交互式 UI 的引入

2025年底，由 Anthropic、OpenAI 及社区推动的 MCP Apps 扩展（代号 SEP-1865） 正式发布，旨在彻底突破这一瓶颈。其核心创新在于，允许 AI 对话线程内直接交付并运行完整的、交互式的 Web 应用程序。

MCP Apps 的本质，是存在于 AI 对话中的沙箱化 HTML5 应用。它并非定义新的 UI 语言，而是拥抱成熟的 Web 技术栈（HTML/JS/CSS）。在 SEP-1865 框架下，工具定义可通过 _meta.ui 字段声明一个指向 UI 资源（使用 ui:// 协议）的引用。当 LLM 调用此类工具时，宿主便能识别该声明，并从服务器拉取对应的 UI 捆绑包进行渲染。

与传统 MCP 工具相比，MCP Apps 带来了根本性的提升：

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