Anthropic Acquires StainlessAPI for $300 Million to Boost AI Agent Tools

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