Yeah, we've been following it closely. We already support the majority of the MCP spec and plan to add support for UI over MCP.
But our use case is a little different. MCP Apps embed interfaces into other agents. Tambo is an embedded agent that can render your UI. There's overlap for sure, but many of the developers using us don't see themselves putting their UI inside ChatGPT or Claude. That's just not how users use their apps.
That said, we're thinking about how we could make it easy to build an embedded agent and then selectively expose those UI elements over MCP Apps where it makes sense.
There's overlap for sure. I'd say we've built a more drop-in solution. We actually migrated to AG-UI events under the hood, and we have plans to expand cross-compatibility across standards.
The major difference is we provide an agent. You don't need to bring your own agent or framework. A lot of our developers are using our agent, really happy with it, and we have a bunch of upcoming features to make it even better out of the box.
You install the React SDK, register your React components with Zod schemas, and then the agent responds to users with your UI components.
Developers are using it to build agents that actually solve user needs with their own UI elements, instead of text instructions or taking actions with minimal visibility for the user.
We're building out a generative UI library, but as of right now it doesn't generate any code (that could change).
We do have a skill you can give your agent to create new UI components:
I'm not from the era of these games, but I remember trying them and finding them frustrating for the same reason.
But when I tried this, I literally couldn't stop. I could just write some random action.
It's actually amazing to me how many situations they were able to consider in the game, but having the LLM translate my language into the right action made the game feel way more natural.
I'd be interested in seeing how people can dress up these games with images, or more complex interactions. It could be a whole sub-genre.
These apps will likely require extensive documentation.
The question is: Why expose it to the user if you can use an LLM to surface only the relevant information, contextualized to what they are doing?
We use this in our app, and it reads our docs to provide context when rendering the UI. I hope that most users never actually read our docs, and eventually learn to ask our app.
It can generally show the right UI, help them configure it, and use docs to ground it.
I have to keep asking the same questions. Do you think it could remember what I typically ask and generate or suggest it?