@StainlessAPI @RattrayAlex YouTube: Spotify:
@StainlessAPI @RattrayAlex YouTube：Spotify：
Anthropic just acquired developer tool startup @StainlessAPI, whose biggest customers were OpenAI and Google. Back in October, I had Stainless CEO and founder Alex Rattray (@RattrayAlex) on AI & I to talk about MCP servers and the unglamorous plumbing that makes AI agents actually work. (Disclosure: I’m a small investor in the company.) After Monday's news, the conversation lands differently—in it, Alex essentially walks me through the design thinking for building APIs, SDKs, and MCP servers that Anthropic paid a reported $300 million for. On @every's AI & I, we get into MCP and the future of the AI-native internet. Highlights include: - Design MCP servers to be lean and precise. Alex's best practices for building reliable MCP servers start with keeping the toolset small, giving each tool a precise name and description, and minimizing the inputs and outputs the model has to handle. At Stainless, they also often add a JSON filter on top to strip out unnecessary data. - Make complex APIs manageable with dynamic mode. To solve the problem of how an AI figures out which tool to use in larger APIs, Stainless switches to "dynamic mode," where the model gets only three tools: List the endpoints, pick one and learn about it, and then execute it. - MCP servers as business copilots. At Stainless, Alex uses MCP servers to connect tools like @NotionHQ and @HubSpot, so he can ask questions like, "Which customers signed up last week?" The system queries multiple databases and returns a summary that would've otherwise taken multiple logins and searches. - Create a "brain" for your company with Claude Code. Alex built a shared company brain at Stainless by keeping Claude Code running on his system and asking it to save useful inputs—like customer feedback and SQL queries—into GitHub. Over time, this creates a curated archive his team can query easily. - The future of MCP is code execution. Instead of giving models hundreds of tools, Alex believes the most powerful setup will be a simple code execution tool and a doc search tool. The AI writes code against an API's SDK, runs it on a server, and checks the docs when it gets stuck. This is a must-watch for anyone who wants to understand MCP—and learn how to use them as a competitive edge. Watch below! Timestamps Introduction: 00:01:15 APIs and MCP, the connectors of the new internet: 00:05:09 Why MCP exists: 00:11:00 Why MCP servers are hard to get right: 00:17:15 Design principles for reliable MCP servers: 00:20:24 Using MCP for business ops at Stainless: 00:25:06 Alex’s take on the security model for MCP: 00:40:57 How one-off AI actions become permanent production software: 00:44:42
Anthropic 刚刚收购了开发者工具初创公司 @StainlessAPI，而它最大的客户一直是 OpenAI 和 Google。早在 10 月，我就曾在 AI & I 节目中邀请 Stainless 的 CEO 兼创始人 Alex Rattray（@RattrayAlex）来聊 MCP servers，以及那些让 AI agents 真正运转起来、并不起眼的底层“管道”。（披露一下：我本人是这家公司的小额投资人。）在周一的消息之后，再回看这段对话，会有不同的感受——在这期内容里，Alex 基本上向我完整讲解了构建 APIs、SDKs 和 MCP servers 的设计思路，而 Anthropic 据报道正是为这些能力支付了 3 亿美元。 在 @every 的 AI & I 中，我们深入聊了 MCP 和 AI-native internet 的未来。精彩要点包括：- 将 MCP servers 设计得精简而精确。Alex 关于构建可靠 MCP servers 的最佳实践，首先是保持工具集尽量小，为每个工具提供精确的名称和描述，并尽量减少 model 需要处理的输入和输出。在 Stainless，他们还经常额外加上一层 JSON 过滤器，以剔除不必要的数据。- 用 dynamic mode 让复杂 API 更易管理。为了解决 AI 在大型 API 中该如何判断使用哪个工具的问题，Stainless 会切换到 “dynamic mode”，在这种模式下，model 只会拿到三个工具：列出 endpoints、选择其中一个并了解其信息，然后执行它。- 把 MCP servers 当作业务 copilot。在 Stainless，Alex 用 MCP servers 连接了 @NotionHQ 和 @HubSpot 之类的工具，这样他就能提出类似“上周有哪些客户注册了？”这样的问题。系统会查询多个数据库并返回摘要，而这原本需要多次登录和搜索才能完成。- 用 Claude Code 为公司建立一个“brain”。Alex 在 Stainless 搭建了一个共享的公司 brain：他让 Claude Code 持续运行在自己的系统上，并要求它把有用的输入——比如客户反馈和 SQL queries——保存到 GitHub。随着时间推移，这会形成一个经过整理的归档，团队可以很方便地查询。- MCP 的未来是 code execution。Alex 认为，与其给 model 几百个工具，不如提供一个简单的 code execution 工具和一个文档搜索工具，这才是最强大的配置。AI 可以针对某个 API 的 SDK 编写代码，在服务器上运行，并在卡住时查阅文档。对于任何想真正理解 MCP——以及学会如何把它作为竞争优势来使用的人来说，这期内容都非常值得一看。可在下方观看！时间戳如下：引言：00:01:15 API 和 MCP，作为新互联网的连接器：00:05:09 MCP 为什么会出现：00:11:00 为什么 MCP servers 很难做好：00:17:15 可靠 MCP servers 的设计原则：00:20:24 在 Stainless 用 MCP 处理业务运营：00:25:06 Alex 对 MCP 安全模型的看法：00:40:57 一次性的 AI 动作如何变成长期的生产软件：00:44:42