Watch out (mobile) app stores!
Dynamic web apps are back, with help of AI.
From AI providers: Google, OpenAI, (Anthropic) MCP.
How this works: AI Apps (usually chat or search)
dynamically generate JSON or similar metadata as part of response to prompts.
This is converted to web UI by JS libraries / tools.
The innovations is standardization,
and in this case either custom JSON format by Google,
or MCP extensions from OpenAI and Anthropic.
The future is all about AI-generated user interfaces (says Google) - YouTube by
Google got a new project! Okay ... that happens frequently. But it's a project that's in-line with other industry efforts to dynamically generate purpose-built UIs with AI. Kind of like new "mini app stores" that might end up being not so mini.
This extension addresses one of the most requested features from the MCP community and builds on proven work from MCP-UI and OpenAI Apps SDK - the ability for MCP servers to deliver interactive user interfaces to hosts.
MCP Apps Extension introduces a standardized pattern for declaring UI resources, linking them to tools, and enabling bidirectional communication between embedded interfaces and the host application."
Summary by Gemini
Recent developments in late 2024 and 2025 have crystallized a new paradigm often called "AI UI" or Generative UI. This is the shift from static, pre-built user interfaces to dynamic interfaces generated on-the-fly by AI agents to match a user's specific intent.
The three terms you listed—A2UI, OpenAI Apps SDK, and MCP Apps—are the competing and complementary standards emerging to build this future.
1. The Big Picture: What is AI UI?
Traditionally, apps have fixed buttons and menus. If you want to book a flight, you navigate a static form. In AI UI, an AI agent (like ChatGPT or Claude) determines what you need and generates the interface in real-time.
- Old Way: You scroll through a list of 50 tools to find the "Calendar" button.
- AI UI Way: You say "Schedule a meeting," and the AI generates a date-picker widget right in the chat.
2. A2UI (Agent-to-User Interface)
"The Native, Declarative Standard"
- Origin: An open-source project initiated by Google.
- Core Concept: A2UI allows an AI agent to send a "blueprint" (a JSON file) of a UI to your device, which your device then builds using its own native components (like native iOS buttons or Android sliders).
- How it works:
- The agent does not send code (like HTML or JavaScript) to be executed.
- Instead, it sends a secure list of components (e.g.,
type: "button", label: "Book Now"). - The client application (your phone or browser) renders this safely.
- Why it matters: It is highly secure because no arbitrary code is running. It also ensures the UI looks perfectly consistent with the app you are using (e.g., a dark-mode iOS app will render a dark-mode iOS button).
3. OpenAI Apps SDK
"Apps Inside ChatGPT"
- Origin: OpenAI (built on top of the MCP standard).
- Core Concept: A framework that lets developers build mini-applications that run directly inside the ChatGPT interface.
- How it works:
- It extends the Model Context Protocol (see below) to include UI capabilities.
- Developers define "widgets" (interactive cards, charts, dashboards) that the AI can choose to display during a conversation.
- Unlike A2UI's strictly native approach, this often leverages web technologies to render rich, app-like experiences inside the chat window.
- Why it matters: It turns ChatGPT from a chatbot into a platform/OS. Instead of just talking, you can use a "Spotify App" or "Jira App" inside the chat to perform complex tasks with visual feedback.
4. MCP Apps (Model Context Protocol Apps)
"The Universal Standard for UI-as-a-Resource"
- Origin: An extension of the Model Context Protocol (MCP), originally championed by Anthropic and widely adopted by the industry.
- Core Concept: MCP provided a standard way for AI models to connect to data (like reading a database). MCP Apps adds a standard way for those tools to return User Interfaces as a resource.
- How it works:
- It treats a UI like a file or a web page. A tool can return a
ui://URI, which the AI client (like Claude Desktop or an IDE) fetches and displays. - It typically uses sandboxed technologies (like iframes) to show these interfaces securely.
- It treats a UI like a file or a web page. A tool can return a
- Why it matters: It solves the "fragmentation" problem. A developer can build one "MCP App" (e.g., a stock ticker tool) and it should theoretically work in Claude, ChatGPT, Cursor, or any other AI client that supports the standard.
Summary Comparison
| Feature | A2UI | OpenAI Apps SDK | MCP Apps |
|---|---|---|---|
| Primary Philosophy | Native First: Sends a JSON "blueprint"; client builds the UI. | Platform First: Builds apps specifically for the ChatGPT ecosystem. | Resource First: Treats UI as a fetchable resource (like a webpage). |
| Rendering | Uses the host app's native widgets (Swift, Kotlin, Flutter components). | Renders web-based widgets/cards inline within the chat. | Often uses sandboxed web content (HTML/iframes) or standard templates. |
| Security | High (No executable code sent). | High (Managed by OpenAI's sandbox). | High (Sandboxed isolation). |
| Best For... | Mobile apps and deeply integrated agents where "look and feel" matters. | Developers wanting to sell/distribute tools to ChatGPT's massive user base. | Tool builders who want their tool to work across any AI model (Claude, OpenAI, etc.). |
Summary by Claude
These appear to be emerging concepts in the AI interface/application space. Let me break down what I know and search for current information:Now I have enough context. Let me explain these three concepts:
A2UI (Agent-to-User Interface)
A2UI is a declarative UI protocol for agent-driven interfaces created by Google (just announced 3 days ago, v0.8 preview). The core problem it solves: how can AI agents safely send rich UIs across trust boundaries? Instead of text-only responses or risky code execution, A2UI lets agents send declarative component descriptions that clients render using their own native widgets.
Key characteristics:
- Security: No iframes or arbitrary code execution. The client renders with its own UI framework, inheriting app styling, accessibility, and performance.
- The agent sends a description of the component tree and data model, and the client application maps those to its native widgets—web components, Flutter widgets, React, SwiftUI, etc.
- LLM-Friendly: Flat component list with ID references that's easy to generate incrementally, correct mistakes, and stream.
Think: agent says "render a booking form with these fields" in JSON, your app renders it natively in your design system.
OpenAI Apps SDK
OpenAI's Apps SDK enables developers to build apps that live inside ChatGPT. Apps appear in conversation naturally and include interactive interfaces you can use right in the chat.
What it does:
- The Apps SDK builds on the Model Context Protocol (MCP)—so it's MCP-based under the hood
- With the Apps SDK, you get the full stack: connect your data, trigger actions, render a fully interactive UI
- Pilot partners include Booking.com, Canva, Coursera, Figma, Expedia, Spotify, Zillow
This is OpenAI's play for distribution—you build once, and your app reaches ChatGPT's 800M weekly users.
Related: OpenAI also released AgentKit, which includes Agent Builder (visual canvas for multi-agent workflows), ChatKit (embeddable chat UI toolkit), and Connector Registry. The Agents SDK (separate from Apps SDK) is their lightweight framework for building agentic AI apps with primitives like Agents, Handoffs, Guardrails, and Sessions.
MCP Apps
The MCP Apps Extension (SEP-1865) standardizes support for interactive user interfaces in the Model Context Protocol. It introduces a pattern for declaring UI resources, linking them to tools, and enabling bidirectional communication between embedded interfaces and the host application.
The problem it addresses: Currently, MCP servers are limited to exchanging text and structured data with hosts. While this works well for many use cases, it creates friction when tools need to present visual information or gather complex user input.
How it works:
- UI templates are resources with the
ui://URI scheme, referenced in tool metadata - The initial spec supports only text/html content rendered in sandboxed iframes with restricted permissions
- All UI-to-host communication goes through loggable JSON-RPC
The SEP was authored by MCP Core Maintainers at OpenAI and Anthropic, together with the MCP-UI creators.
How They Relate
| A2UI | OpenAI Apps SDK | MCP Apps | |
|---|---|---|---|
| Who | OpenAI | Anthropic + OpenAI joint | |
| Scope | Protocol for agent-generated UI | Full app platform in ChatGPT | UI extension for MCP |
| Rendering | Native widgets (no iframe) | Full interactive UI | Sandboxed iframes |
| Transport | Works with A2A, AG-UI | Built on MCP | Part of MCP spec |
A2UI and AG-UI/CopilotKit work well together—A2UI is a generative UI specification while AG-UI is the agent↔user interaction protocol connecting frontend to backend.
These are all part of the emerging "agentic UI" layer—the recognition that agents need to go beyond text responses to deliver rich, interactive experiences. Given your work with MCP clients/servers, the MCP Apps extension is probably most directly relevant to you.
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