MCP Apps allow you to render interactive UI directly inside any agent supporting the Model Context Protocol. Instead of a wall of text, your agent can now provide a functional chart, a checkout form, or a video player. This bridges the gap in agentic workflows: clicking a button is often clearer than describing the action you hope an agent executes.

MCP Apps originated as MCP-UI, an experimental project. After adoption by early clients like goose, the MCP maintainers incorporated it as an official extension. Today, it's supported by clients like goose, MCPJam, Claude, ChatGPT, and Postman.

Even though MCP Apps use web technologies, building one isn't the same as building a traditional web app. Your UI runs inside an agent you don't control, communicates with a model that can't see user interactions, and needs to feel native across multiple hosts.

After implementing MCP App support in our own hosts and building several individual apps to run on them, here are the practical lessons we've picked up along the way.

MCP-UI is fun. It’s scrappy. It’s early. And like I said in my last post, there’s something genuinely addictive about building this close to the edges of an ecosystem while everything is still taking shape.

But MCP Apps feels different.

Not in a “shiny new feature” way. More in a “this is the ecosystem maturing” way.

In 2025 we did a fairly cutting edge take on whole device automation using Android (code name was gosling) which was an on-device agent that would take over your device (mic even used it to do some shopping - which he realized after some things arrived at his door that it had automatically purchased as the result of an email - hence the PoC/experimental label!)

Recently we consolidated the apps for goose mobile.

The goose-ios client is more production ready, and in the app store (still early days). We hope to have a port of that to Android, which will be strictly a client (and won't take over your device!) to your remote agent. The aim of the client (vs an on device agent) is for you to take your work on the go with you.

Really great for long running tasks, checking on things, or just shooting off an idea but still keeping things local to your personal agent (where all your stuff is) securely.

The first question I had when I heard about MCP sampling was:

“Can’t I just write better tool descriptions and tell the tool it’s an expert?”

The MCP ecosystem is standardizing how servers deliver interactive UIs to hosts, and goose is an early adopter. Today we're shipping support for the draft MCP Apps specification (SEP-1865), bringing goose in line with the emerging standard, as other hosts like Claude and ChatGPT move toward adoption.

In our previous blog post we detailed the Model Context Protocol (MCP) system and discussed some security concerns and mitigations. As a brief recap, MCP provides agents with a means to accomplish tasks using defined tools; reducing the burden of using complex and varied APIs and integrations on the agent.

Can you automate taste? The short answer is no, you cannot automate taste, but I did make my design preferences legible.

But for those interested in my experiment, I'll share the longer answer: I wanted to participate in Genuary, the annual challenge where people create one piece of creative coding every day in January.

My goal here wasn't to "outsource" my creativity. Instead, I wanted to use Genuary as a sandbox to learn agentic engineering workflows. These workflows are becoming the standard for how developers work with technology. To keep my skills sharp, I used goose to experiment with these workflows in small, daily bursts.

As AI agents grow in capability, more people feel empowered to code and contribute to open source. The ceiling feels higher than ever. That is a net positive for the ecosystem, but it also changes the day-to-day reality for maintainers. Maintainers like the goose team face a growing volume of pull requests and issues, often faster than they can realistically process.

We embraced this reality and put goose to work on its own backlog.

Every time there's a hot new development in AI, Tech Twitter™ declares a casualty.

This week's headline take is "Skills just killed MCP"

It sounds bold. It sounds confident. It's also wrong.

One day, we will tell our kids we used to have to wait for agents, but they won't know that world because the agents in their day would be so fast. I joked about this with Nick Cooper, an MCP Steering Committee Member from OpenAI, and Bradley Axen, the creator of goose. They both chuckled at the thought because they understand exactly how clunky and experimental our current "dial-up era" of agentic workflows can feel.

Model Context Protocol (MCP) has moved the needle by introducing a new norm: the ability to connect agents to everyday apps. However, the experience isn't perfect. We are still figuring out how to balance the power of these tools with the technical constraints of the models themselves.