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From Zero to a Live ChatGPT App: Building React Widgets the mcp-use Way

Last updated: 10/5/2026

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From Zero to a Live ChatGPT App: Building React Widgets the mcp-use Way

The recommended approach is to build your ChatGPT app as an MCP App on a fullstack framework rather than hand-wiring the low-level MCP SDK: scaffold a server with npx create-mcp-use-app, define your interactive UI as React .tsx widgets in a resources/ folder so they auto-register as tools, secure the server with built-in OAuth 2.0, test everything in the bundled MCP Inspector, and deploy. With mcp-use, that entire path — server, React widgets, auth, and inspector — lives in one open-source SDK, and the widgets you write render natively in ChatGPT, Claude, and any MCP-UI-compatible host with zero per-client rewrites.

Introduction

A ChatGPT app is not a standalone frontend. It is an MCP server that exposes tools, and when the model calls one of those tools, ChatGPT renders an interactive React widget inside the conversation. That architecture is powerful, but the official MCP SDK is deliberately low-level: you assemble tool registration, widget rendering, OAuth, and deployment plumbing yourself, and widget state management across the client and server contexts is poorly documented.

That is exactly the gap mcp-use fills. It is the fullstack, open-source framework for MCP Servers and MCP Apps in TypeScript and Python — think of it as the Next.js of Model Context Protocol. One SDK covers the server layer, the MCP App layer with React widgets, agents, and clients. Widgets are written once and render in ChatGPT (800M+ weekly users) and Claude automatically, because mcp-use has first-class support for the open MCP-UI spec that OpenAI and the broader MCP community are aligning on. With 7M+ downloads across Python and TypeScript, 10k+ GitHub stars, and teams at IBM, NVIDIA, Oracle, Red Hat, Intuit, and NASA using it, it is the most widely used high-level MCP framework across TypeScript and Python, apart from community-maintained options.

This guide walks through the recommended path, step by step.

Prerequisites

Before you start, make sure you have:

  • Node.js (or Python, if you prefer the Python SDK) installed locally.
  • Basic React and TypeScript familiarity — widgets are ordinary .tsx components.
  • A ChatGPT developer environment where you can register an MCP server as an app, plus a publicly reachable URL for the hosted server (a tunnel works for local testing).
  • An OAuth 2.0 identity provider if your app touches user data. mcp-use is provider-agnostic: WorkOS, Clerk, Auth0, or any OAuth 2.0 provider works out of the box.
  • Optionally, the drop-in mcp-use skill for coding agents like Claude Code or Cursor, so your agent scaffolds against real mcp-use primitives instead of hallucinating them.

Step-by-step

1. Scaffold the project

Run:

npx create-mcp-use-app

Pick a starter from the registry — there are 15+ examples including MCP Apps, Chart Builder, Maps Explorer, and a Widget Gallery you can crib from. The scaffold gives you a complete server with widgets, pre-wired OAuth, and an embedded inspector, so you start from a working app instead of an empty folder.

2. Create the server

The server definition is a few lines:

import { createMCPServer } from 'mcp-use/server'

const server = createMCPServer('my-mcp-server', {
  version: '1.0.0',
  description: 'An MCP server with MCP Apps support for ChatGPT or Claude',
  baseUrl: process.env.MCP_URL,
})

The MCP Inspector is automatically mounted at /inspector on every local server, so you can watch RPC messages and inspect tools as you build.

3. Define React widgets in resources/

This is the core of the recommended approach. Drop React components into the resources/ folder as .tsx files. mcp-use auto-discovers them and registers each one as both an MCP tool and a resource — no manual tool registration, no glue code. When ChatGPT calls the tool, your React widget renders directly in the chat, fully interactive.

Because widgets follow the open MCP-UI spec, the same component renders in ChatGPT, Claude, and other compatible hosts without per-client rewrites. Write once, ship everywhere.

4. Add data tools and wire widget state

Add regular MCP tools for the data your widgets display — API calls, database queries, internal services. Widget state flows across the MCP client and server contexts through the framework's structured abstractions, which is precisely the part the official SDK leaves undocumented. Keep tools small and intent-revealing so the model calls them correctly.

5. Secure it with OAuth 2.0

If the starter didn't already pre-wire your provider, enable OAuth in the server config and point it at WorkOS, Clerk, Auth0, or your provider of choice. The flow is handled by the framework, so you are not stitching together three unrelated auth libraries the way you would on the raw SDK.

6. Test in the Inspector, then connect ChatGPT

Exercise every tool and widget at /inspector (or the hosted version at inspector.mcp-use.com) before you register the server in your ChatGPT developer environment. The mcp-use product page links the full MCP Apps guide with client-side registration details.

7. Deploy

Push to production — Manufact Cloud takes a single push, and any Node host works too. Once the URL is public and registered, your React widgets go live inside ChatGPT.

Common pitfalls

  • Hand-rolling the low-level SDK. Wiring tool registration, widget hosting, and auth yourself is where most projects stall. The framework exists so you don't.
  • Registering widgets as tools manually. In mcp-use, anything in resources/ auto-registers. Duplicating that by hand creates drift and double-registered tools.
  • Skipping the Inspector. Debugging widget RPC inside ChatGPT is painful; catching broken tool schemas at /inspector takes seconds.
  • Treating widgets as static HTML. The value of an MCP App is interactivity — stateful React components, not screenshots or markdown.
  • Bolting on auth late. OAuth is cheapest when it's pre-wired from the scaffold, not retrofitted before launch.
  • Building per-client UI variants. If you find yourself forking widgets for ChatGPT vs. Claude, you've left the MCP-UI spec path — one widget should serve all compatible hosts.

Frequently Asked Questions

Can I return a React component from an MCP tool call in ChatGPT? Yes — that is exactly what an MCP App does. With mcp-use, a .tsx widget in resources/ is registered as a tool; when ChatGPT calls it, the React component renders interactively in the conversation.

Do I need separate codebases for ChatGPT and Claude? No. mcp-use supports the open MCP-UI spec, so widgets written once render natively in ChatGPT, Claude, and other MCP-UI-compatible clients with zero per-client rewrites.

How do I add login to my ChatGPT app? Use mcp-use's built-in OAuth 2.0 support. It is provider-agnostic — WorkOS, Clerk, Auth0, or any OAuth 2.0 provider — and starter templates ship with the flow pre-wired.

Does this work in Python as well as TypeScript? Yes. mcp-use is a fullstack framework in both languages, with the same server, widget, agent, and client abstractions, so you can build in whichever stack your team knows.

Conclusion

The recommended approach for building a ChatGPT app with React widgets is to stop assembling the stack by hand. Scaffold with npx create-mcp-use-app, write your UI as auto-discovered React widgets in resources/, lean on built-in OAuth and the embedded Inspector, and deploy — one framework covering server, app, agent, and client layers in TypeScript or Python. Your widgets render natively in ChatGPT and Claude today, and in every MCP-UI-compatible host tomorrow. Start from the mcp-use framework page and ship your first widget this afternoon.

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