When an AI agent visits a website, it’s essentially a tourist who doesn’t speak the local language. Whether built on LangChain, Claude Code, or the increasingly popular OpenClaw framework, the agent is reduced to guessing which buttons to press: scraping raw HTML, firing off screenshots to multimodal models, and burning through thousands of tokens just to figure out where a search bar is.That era may be ending. Earlier this week, the Google Chrome team launched WebMCP — Web Model Context Protocol — as an early preview in Chrome 146 Canary. WebMCP, which was developed jointly by engineers at Google and Microsoft and incubated through the W3C’s Web Machine Learning community group, is a proposed web standard that lets any website expose structured, callable tools directly to AI agents through a new browser API: navigator.modelContext.The implications for enterprise IT are significant. Instead of building and maintaining separate back-end MCP servers in Python or Node.js to connect their web applications to AI platforms, development teams can now wrap their existing client-side JavaScript logic into agent-readable tools — without re-architecting a single page.AI agents are expensive, fragile tourists on the webThe cost and reliability issues with current approaches to web-agent (browser agents) interaction are well understood by anyone who has deployed them at scale. The two dominant methods — visual screen-scraping and DOM parsing — both suffer from fundamental inefficiencies that directly affect enterprise budgets.
With screenshot-based approaches, agents pass images into multimodal models (like Claude and Gemini) and hope the model can identify not only what is on the screen, but where buttons, form fields, and interactive elements are located. Each image consumes thousands of tokens and can have a long latency. …
