Block's Goose blog publishes a deep dive into MCP-UI, a technology that, according to the team, does away with the "endless walls of text" of AI agent conversations. The article draws on an episode of the "Wild Goose Case" podcast bringing together the creators of MCP-UI, Ido Salomon and Liad Yosef of Monday.com, and Andrew Harvard of Block, to explore how this technology reshapes the future of agentic interfaces.
The text-only interface problem
Asking an agent for help with a purchase today results in a wall of text: product names, prices, descriptions, followed by copy-pasted URLs and multiple tabs — the user redoes all the work. As Ido Salomon puts it, everyone has experienced that "rage quit" moment in front of an assistant that returns nothing but text. These interfaces work for early adopters, not for the general public.
MCP-UI: protocol and SDK
MCP-UI (Model Context Protocol User Interface) makes it possible to embed rich, interactive web components directly into agent conversations. The underlying philosophy is simple: why discard decades of web UI/UX expertise when it can instead be augmented with AI? Liad Yosef notes that more than a decade of web interfaces refined for human cognitive limits should not disappear with the rise of agents. The system leverages the embedded resources of the MCP specification: an MCP server can return UI components instead of plain text. Four key contributions: rich components (catalogs, seat maps, forms), brand preservation (Shopify keeps its own experience), secure integration, and cross-platform compatibility.
Technical foundations
Security is paramount: components are rendered in sandboxed iframes that communicate with the host only via post messages, preventing third-party code from manipulating the parent application. Three content types are supported: external URLs, raw HTML, and remote DOM (rendered in separate workers). Getting started is minimal: createUIResource({ type: 'html', content: '<h1>Hello World</h1>' }).
Demonstrations and ecosystem
The demos show visual shopping (interactive Shopify catalog with add-to-cart), trip planning (seat selection on a visual map, automatic destination weather), and restaurant discovery (cards with photos, ratings, menus, direct ordering) — all without leaving the conversation. Success rests on four stakeholder groups: agent developers (such as the Goose team), MCP server developers, service providers (Shopify, Square), and end users. The approach creates a network effect: a component implemented once works across all compatible agents.
Vision
Beyond prettier interfaces: an accessibility revolution ("what could be more accessible than an agent that knows you and builds the UI to your preferences?") and, eventually, generative UI producing interfaces tailored to each user's needs.