大白话系列，是希望用最通俗的文字，揭示复杂技术背后的简单本质。
文 | Jomy @302.AI
编 | 南乔River @ShowMeAI
我们 302.AI 做 MCP 和 Agent 相关开发有一段时间了，期间一直与开发者和用户们保持着密切的交流。
有一个普遍的感受：尽管行业内几乎所有人都听过 MCP、Agent 这些术语，但只有极少数人真正理解它们的本质。
今天，我就基于 302.AI 的实践和成果，分享一些自己的见解，帮大家厘清概念的来龙去脉，并大胆预测一下未来的发展方向。
I. 缸中之脑：只能说不能做的大模型
让我们先从大语言模型（Large Language Model，LLM）说起。
大语言模型，顾名思义，就是一个只能处理和输出文字的系统。
早期的大语言模型，输出非常不稳定，准确率很低，经常「一本正经地胡说八道」。
所以，人们最多把它当成一个顾问：咨询意见，但不敢让它直接拍板决策或上手干活。
这个时期的大模型，有点像被限制在「缸」里的「大脑」（借用哲学上的「缸中之脑」假说）。
它能思考、能滔滔不绝地输出观点。但它没手没脚，不能对「缸」外的物理世界/数字世界直接做点什么。
但是，AI 技术发展飞快。
随着模型参数规模的扩大和训练方法的革新，语言模型的「智力」得到了肉眼可见的提升。人们惊喜地发现，AI 写出的文案、给到的建议、生成的代码，几乎不需要修改就能直接使用了！
眼看着 AI 越来越靠谱，一种想法自然而然地浮现出来：既然大模型这么能干，是时候解开 AI 的禁锢，让它不只能「动动嘴」，也能「动动手」了？
II. 调用工具：大模型学会了动手
怎么解开 AI 的「禁锢」呢？
答案就是让大模型能够自行使用工具，也就是我们常说的 Function Call（函数调用）或 Tool Use（工具使用）。
那么，一个只会输出文字的模型，是如何调用工具的呢？
这里需要做一个必要的澄清：模型调用工具，并不是模型真的「动手」去操作工具。
本质上，还是模型生成文本（结构化的文本），然后配套的程序接收到指令，再去调用工具。（如下图所示）
而所谓的「工具」，就是各种各样的程序接口（API）或者软件操作，例如搜索、编辑数据库、编辑文件等。
gpt-4o 调用工具的命令（JSON 格式）
这就像给一个思维敏捷但行动不便的人，配备了一台随时待命的智能计算机。
他只需要「说」出来需要做什么，计算机就会自动决策和执行所有的指令，整个过程不再需要人类的介入。
让 AI 模型使用工具，本质上是一种「放权」行为。
人们将 AI 从「缸」里释放出来，允许 AI 通过调用工具，直接对现实世界或数字世界产生实际的影响。
这无疑是 AI 迈出的关键一步，也是 Agent 得以诞生的基石。
III. MCP 诞生：不再重复造轮子
AI 学会了使用工具，这很好。
但很快就出现了新问题：每家公司、每个开发者都在用自己的方式定义和接入工具。这就导致了大量的重复劳动，并且工具难以复用和共享，只能「自己造自己用」。
Anthropic 公司敏锐地发现了这个问题。他们认为，工具应该有一套通用的「语言」和「接口规范」，于是提出了 MCP（Model Context Protocol，模型上下文协议）。
（MCP 统一了模型调用工具的方式）
这个协议对大模型发展的意义重大，完全可以类比为秦始皇当年规定的「书同文」和「车同轨」。从此，模型调用工具这个事情就被大大地加速了。
MCP 是什么呢？
简单来说，它是一套定义大模型如何发现、理解和调用外部工具（或称服务）的标准协议。
（MCP 中的 Prompt 和 Resource 暂且不论）
它明确了两个核心角色：
- MCP Client（客户端）： 通常是使用工具的一方。一般是 AI 应用，比如 Claude 客户端、Cursor 编程工具等。
- MCP Server（服务端）： 也就是提供工具的一方。任何拥有 API 或软件服务的公司，都可以按照 MCP 规范把自己包装成一个 MCP Server，把原来给人用的工具，改造成能让 AI 理解和调用的工具。
（MCP基本框架示意图）
(Norah Sakal 这张「MCP architecture」图非常出圈，说的也是一回事)
前段时间，海外知名投资机构 a16z 制作了一份 MCP Market Map，梳理了 MCP 发展现状。
可以看到，MCP Client 和 MCP Server 生态已经初具模型并在日益繁荣。（如下图所示）
（MCP 生态现状）
其中，在 MCP Marketplace 板块，MCP.so 导航网站的制作者，就是国内知名的独立开发者 idoubi。
目前，MCP.so 收录的 MCP Server 数量已经超过了 7000 个，其中就包括 302.ai 的 Sandbox MCP Server和 BrowserMCPServer。
用户只需要在客户端连接这两个MCP Server，就可以让AI对远程Linux服务器和远程浏览器进行操作，完成相应的任务，极其便利。
(mcp.so 主页截图)
IV. Agent 的诞生：更好地调用工具
MCP 让工具的供给变得更加方便了。
但新的挑战接踵而至：一个 AI 模型能有效掌握和使用的工具数量是有限的。就像一个人能熟练掌握和使用的技能，也是有限的。
在人类社会里，每个人都专注于擅长的领域，以医生、律师、教师、工程师、程序员等职业身份，把自己的事情做好。
AI 也是同理。
当工具不再是主要瓶颈后，如何让 AI 模型更聪明、更高效地使用这些工具，就成了核心问题。
Agent 就在这个背景下诞生了。
(Agent 最简公式)
我认为，理解 Agent 最简单的一个公式就是：
Agent = LLM + Tools
有工具使用权限的 AI 就是 Agent，中文翻译成「代理人」，有些地方会翻译为「智能体」。
（Agent 最简单的实现框架）
现在，对于 Agent 的发展，业界有两个大的方向：
- 通用 Agent（通才）：很多大模型公司都在往这个方向努力。但现阶段，受限于模型能力等各方面的挑战，这注定暂时只是一个美好的理想。
- 垂直 Agent（专才）： 专注于解决特定领域或特定类型任务。目前看来更容易落地、也更有可能在短期内产生实际价值。
在垂直 Agent 优化实践中，我们有几条关键经验，也在此分享给大家：
- 明确的指引。在实践中体现为精准的系统提示词。
- 垂直的工具。在实践中体现为只接入任务强相关的MCP Server。
- 完整的上下文。在实践中体现为完善的任务描述和任务记忆。
读到这里，你就已经追到了 AI Agent 领域发展的最前沿。
V. Agent 通信：新的协议应运而生
然后，更新的挑战又出现了。
单个垂直 Agent 能解决特定问题，但面对更复杂的现实任务，往往需要多个不同能力的垂直 Agent 协同配合。
现在，各家公司都在闭门造自己的 Agent。这些 Agent 之间缺乏统一的沟通方式和协作机制，注定重复且低效。
这有点像 MCP 出现之前的工具生态，又一次走到了需要「标准化」的路口，只不过这次标准化的对象是 Agent 本身。
为了解决 Agent 之间的信息互通问题，一些新的协议开始进入起草阶段，其中比较受关注的有：
- ANP（Agent Network Protocol）：中国开发者率先提出并推动的一个协议。
- A2A（Agent-to-Agent）Protocol：Google 也在探索类似的概念和协议。
（Google A2A Protocol 原理示意图）
这些协议的核心目标，大致可以归纳为两点：
- 第一，让 Agent 之间明确彼此的能力，便于协作。就像外包网站的个人主页，清晰写明自己的专长，其他人可以按需查找，找到合适了的人就一起做项目。
- 第二，让 Agent 之间可以高效地传递信息。就像团队协作之前，大家约定好沟通方式（比如人会约定好用飞书还是用钉钉）以及消息格式（类似布置任务需要包含哪些信息）。
至于未来哪个协议会成为主流，现在下结论还为时尚早。
但可以肯定的是，Agent 之间的互联互通，将进一步释放 AI 潜能，催生一个更加靠近 C 端（用户端）、更加繁荣、更加有想象力的巨大市场。
VI. 展望：2025 Agent 之年的无限机遇
最后，让我们来系统回顾一下这条演进路径：
- 随着大模型能力的提升，AI 输出的内容越来越靠谱。人们开始让 AI 通过调用工具与外部世界直接交互，把 AI 变成了 Agent。
- MCP 协议统一了工具的开发标准，从而简化了 Agent 的开发难度。
- 现阶段单个 Agent 无法很好地使用大量工具，所以垂直 Agent 成为了当前的主流。
- 垂直 Agent 之间需要配合才可以完成复杂的任务，这催生了 ANP / A2A 等新的互联协议。
你看，技术的发展往往不是一蹴而就的。
每个重大节点（Function Call/Tool Use、Agent、MCP、ANP/A2A）的出现，背后都有其历史必然性。
再往后呢？
考虑到当前基础模型的发展速度和架构特点，我认为，垂直 Agent 市场将会在历史舞台上存在相当长的一段时间。
即使未来出现了能力极强的「超级通用 Agent」，在很多场景下，高度优化、成本可控的垂直 Agent 组合，可能仍然是更具性价比、更可靠的选择。
恰似古语有云：三个臭皮匠，顶一个诸葛亮。
Agent 生态的爆发，可能来得比大多数人想象中还要快。
2025 是名副其实的 Agent 之年。这背后蕴藏着的，是巨大的技术变革和商业机会，以及我们这代人几十年才得一遇的科技浪潮。
衷心希望我今天的分享，能帮你更好地理解这个正在发生的未来。也希望每一位在这个领域努力探索的朋友，都能抓住机遇，在今年摘取到属于自己的胜利果实。
官方网站
302.AI : https://302.ai
MCP.so: https://mcp.so
Anthropic | Introducing the Model Context Protocol: https://www.anthropic.com/news/model-context-protocol
Anthropic | Model Context Protocol: https://modelcontextprotocol.io/introduction
ANP | Agent Network Protocol: https://agent-network-protocol.com
ANP | AgentNetworkProtocol : https://github.com/agent-network-protocol/AgentNetworkProtocol
A2A | A2A Protocol: https://google.github.io/A2A
A2A | Agent2Agent Protocol: https://github.com/google/A2A
推荐阅读
Norah Sakal | What is Model Context Protocol (MCP)? How it simplifies AI integrations compared to APIs: https://norahsakal.com/blog/mcp-vs-api-model-context-protocol-explained
a16z | A Deep Dive Into MCP and the Future of AI Tooling: https://a16z.com/a-deep-dive-into-mcp-and-the-future-of-ai-tooling
What is Model Context Protocol (MCP): Explained: https://composio.dev/blog/what-is-model-context-protocol-mcp-explained
What Is the Model Context Protocol (MCP) and How It Works: https://www.descope.com/learn/post/mcp#llm-isolation-&-the-nxm-problem
常高伟 | 多角度全面对比Google最新的A2A、ANP、MCP: https://mp.weixin.qq.com/s/n1R-wOtNBTKtHdIHOUhQmw
idoubi | 详解 MCP 核心架构: https://mp.weixin.qq.com/s/uTsr06MnJ9t3sGDzLD99_g
Comments(67)
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[…] 上一篇文章 聊了聊 Tool、MCP 和 Agent 三者之间的关系。简单来说就是 Agent = LLM + Tools，而 MCP 统一了 Tools 开发和使用的过程。 […]
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