The Fastest Way to Prototype ChatGPT & MCP Apps (July 2026)

TL;DR: prototype the user moment before you build the whole app. A useful ChatGPT App or MCP App prototype should answer three questions: when should the model call it, what structured input should the model send, and whether the UI is better than a text answer. Fractal helps teams get that prototype in front of users quickly. sunpeak helps once the idea is ready for a real build, because you can test the same app across local ChatGPT and Claude-style runtimes before you spend host credits or submit for review.

ChatGPT Apps and MCP Apps have grown into a real product surface. The official MCP Apps documentation now describes the standard pattern: a tool declares a UI resource, the host renders that resource in a sandboxed iframe, and the app talks back to the host through a ui/* JSON-RPC bridge over postMessage. OpenAI’s current MCP Apps compatibility guide points builders in the same direction for ChatGPT Apps: use the MCP Apps standard keys and bridge by default, then add window.openai only for ChatGPT-only capabilities.

That is good news for builders because the same app idea can be designed as a portable MCP App first. It can run in ChatGPT, Claude, and other MCP Apps-compatible hosts if the app sticks to the shared contract. But portability does not answer the product question.

The harder question is still: should this app exist inside the conversation at all?

Building is not the first risk

Most teams still treat a ChatGPT App like a normal product build. They write requirements, design screens, wire tools, connect auth, deploy the MCP server, and test the final app with users after the expensive parts are done.

That sequence is backwards for most new app ideas. ChatGPT Apps and MCP Apps start with intent, not pages. The model decides whether to call your tool based on the user’s request, your tool metadata, available context, and host behavior. Then the host renders the UI in the conversation. If the trigger is wrong, the app may never appear. If the input is wrong, the UI can fail even though the app looks polished. If the output is no better than text, users will ignore it.

A strong ChatGPT App usually does at least one of these jobs:

1. It knows something the model cannot know from the conversation.
2. It does something the model cannot safely do on its own.
3. It shows something that text handles poorly.

That is why dashboards, calculators, booking flows, comparison tools, product selectors, workflow approvals, file viewers, and data exploration tools make sense inside chat. They give users a better way to act on the conversation. A static marketing page, a generic settings screen, or a whole web app squeezed into an iframe usually does not.

What changed since May 2026

The May version of this post focused on speed: get a working prototype, test the idea, and build later. That is still the right product loop. The ecosystem around the loop is clearer now, so the prototype should test a few more things.

First, MCP Apps are now the shared UI model to design against. A prototype should not assume a ChatGPT-only iframe API unless that capability is the point of the product. Start with the standard resource, tool result, and host bridge shape. Add host-specific behavior only when it materially helps the experience.

Second, ChatGPT distribution has moved through Plugins. OpenAI’s current plugin submission docs describe plugins as packaging for apps, skills, or both. An app-only plugin is backed by an MCP server. That means your prototype should already expose the workflow users will understand later in a listing, test case, and review flow.

Third, local development has become much better. The sunpeak quickstart shows the current two-step loop: scaffold with npx sunpeak new, then run pnpm dev to start the local MCP App inspector and MCP server. You can test the app locally, then use a tunnel when you need to connect ChatGPT Developer mode.

These changes make prototyping more useful, not less. You can validate the idea with a low-cost prototype, then use the production build to harden the exact behavior users already understood.

A prototype needs to run in the conversation

For a normal web app, a prototype can be a Figma mockup. For a ChatGPT App or MCP App, a static mockup misses the main risk because the screen is only one piece of the experience.

Your prototype needs to answer:

• What user prompt should make the app appear?
• What should the model pass into the tool?
• What should the app show with that input?
• Does the user understand why the app appeared?
• Does the app make the next step easier?
• What happens when the input is missing, vague, or wrong?

You cannot answer those questions from a screenshot. You need a working prototype that can be tested inside the conversation or in a close local replica of the host runtime. The point is not to simulate every production edge case. The point is to learn whether the workflow belongs in chat before a team commits weeks of engineering time.

Where Fractal fits

Fractal is a fast way to prototype ChatGPT Apps and MCP Apps before a full engineering build. It is useful when you need to explore the product shape, test the trigger, and show users something real enough to react to.

Instead of starting with a full repo, production auth, hosting, review prep, and custom frontend work, start with the smallest useful app description:

1. What problem should the app solve?
2. When should ChatGPT or another MCP host call it?
3. What input should the model send?
4. What should the app show or do with that input?
5. What should the user be able to change or confirm?

That is enough to test the important parts of the idea. If users do not understand the workflow in a lightweight prototype, a full engineering build will not fix the product shape by itself. If users do understand it, the team can move into implementation with clearer requirements and fewer guesses.

Fractal is most useful for founders, PMs, growth teams, and early product teams that need to validate an app idea before building a production MCP server. It is also useful for technical teams that know how to build but do not yet know which version is worth building.

Keep the prototype smaller than the product

The most common mistake is trying to bring an entire product into ChatGPT. That rarely works. Apps inside chat are strongest when they solve one high-friction moment.

A CRM app should not become a full CRM inside the conversation. A good prototype might show the next best follow-up for a lead. A finance app should not recreate every dashboard. It might explain one metric and show the chart that matters. An ecommerce app should not mirror the storefront. It might recommend three products based on the user’s stated constraints. A project management app should not expose every workflow. It might turn a conversation into draft tasks and ask the user to confirm them.

The prototype should be small enough that the user knows what to do without reading an explainer. If it needs a long onboarding flow, the app is probably trying to do too much.

Test the three decisions that matter

Before you invest in the production build, your prototype should answer three product questions.

Is the trigger right?

The trigger is the moment when the model should use your app. If the app appears too early, it interrupts. If it appears too late, users never discover it. If the trigger is vague, the host may call the tool inconsistently.

Test real prompts from real users. Watch whether the app appears when the user expects help, and whether users understand why it appeared. Tool descriptions and input schema wording matter here because the model uses them to choose tools.

Is the input right?

ChatGPT Apps and MCP Apps depend on structured input. The model may need to pass a location, budget, date range, product type, task list, account ID, or user preference. If that input is missing or wrong, the UI can fail even when the frontend is well built.

Use the prototype to inspect the input shape. If users naturally provide a budget but not a date range, the app should ask for the missing date range or use a sensible default. If users describe the same field in several ways, your production schema and tool description should make that field easy for the model to infer.

Is the output better than text?

This is the standard to keep. The app should make the answer clearer, faster, safer, or more actionable than a plain model response.

Text is good for summaries, explanations, and short recommendations. UI is better when the user needs to sort, filter, compare, preview, confirm, edit, approve, or monitor something over time. If the prototype is just a prettier paragraph, cut it or redesign it.

Turn a validated prototype into a tested app

Once the prototype is working with users, the work changes. You are no longer asking whether the app idea makes sense. You are asking whether it can keep working across model behavior, host runtimes, display modes, auth states, and failure modes.

That is where sunpeak fits. sunpeak gives teams a framework and local inspector for building and testing MCP Apps. Run npx sunpeak new for a new app, or use npx sunpeak inspect --server URL to inspect an existing MCP server. The inspector replicates ChatGPT and Claude-style runtimes locally, supports simulation fixtures, and lets you test themes, display modes, device sizes, tool calls, and resource rendering without a paid host account or AI credits.

For a prototype that is ready to become a product, use sunpeak to test:

• Whether the model calls the right tools for realistic prompts.
• Whether tool inputs and structuredContent match your expected schema.
• Whether the UI works in inline, picture-in-picture, modal, and fullscreen-like surfaces where supported.
• Whether loading, empty, error, cancelled, and auth states behave clearly.
• Whether the app keeps working across ChatGPT and Claude-style runtimes.
• Whether visual states stay stable in CI.

This is also when you should update the production contract. Prefer standard MCP Apps metadata such as _meta.ui.resourceUri, keep transcript-visible data in content or structuredContent, put UI-only data in result _meta, and feature-detect host-specific APIs before using them.

A practical workflow

The full loop looks like this:

1. Describe the smallest useful app moment.
2. Prototype it quickly in Fractal.
3. Test it with users, customers, or internal subject matter experts.
4. Refine the trigger, input schema, and UI.
5. Keep only the workflow that users understand.
6. Build the production MCP App with sunpeak or your preferred MCP stack.
7. Add simulations and tests for the states the prototype revealed.
8. Run local and CI checks before connecting real hosts or submitting for review.

This sequence saves time because the expensive build starts after the product shape is clearer. It also gives engineers better inputs: concrete prompts, expected tool arguments, UI states, edge cases, and user feedback.

The advantage is learning speed

ChatGPT Apps and MCP Apps give products a new place to meet users: inside high-intent conversations. That only helps if the app is useful in that moment. A smaller, sharper app is usually better than a broad app that makes the user feel like they left the conversation for a cramped website.

If you want to prototype ChatGPT Apps, test the app trigger, and learn what users need before writing the production version, Fractal is a fast place to start. Once the idea proves itself, use sunpeak to turn that app into a tested MCP App that can survive the real host loop.

Start with the user moment. Build the smallest useful prototype. Put it in front of people. Then build the product around the workflow that actually worked.