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# MCP App Studio Starter
Starter template for building interactive apps for AI assistants with [MCP App Studio](https://github.com/assistant-ui/assistant-ui/tree/main/packages/mcp-app-studio).
> **Note:** This template is automatically downloaded when you run `npx mcp-app-studio`. You don't need to clone this repo directly.
## Supported Platforms
Build once, deploy anywhere:
- **ChatGPT** — as an MCP Apps host (standard `ui/*` bridge)
- **Claude Desktop** — as an MCP Apps host
- **Any MCP Apps host** — compatible with any MCP-supporting AI assistant
## Quick Start
```bash
# npm (default)
npm install
npm run dev
```
Open http://localhost:3002 — you're in the workbench.
This project also works with pnpm/yarn/bun (use the equivalent install + run commands).
If you switch package managers (e.g. `pnpm` → `npm`), delete `node_modules/` first to avoid confusing the installer.
The MCP server (when `server/` exists) runs at `http://localhost:3001/mcp` by default. If 3001 is already in use, it will select the next available port and write it to `server/.mcp-port`.
The workbench simulates an MCP Apps host in an iframe. It also installs a
`window.openai` shim so you can exercise **ChatGPT-only extensions** during
development (optional, non-standard).
## Agent Workflow
Use `.agent/skills/mcp-app-development/SKILL.md` as the default coding-agent workflow for this repo. It defines the 80/20 capability-slice loop:
- Build UI and real MCP tool(s) in one chunk
- Use TDD (`red -> green`) for parity checks
- Never treat mock-only behavior as done
## Commands
| Command | Description |
| ---------------- | ---------------------------------------- |
| `npm run dev` | Start workbench (Next.js + MCP server) |
| `npm run build` | Production build |
| `npm run export` | Generate widget bundle for deployment |
## Project Structure
```
app/ Next.js pages
components/
├── examples/ Example widgets (POI Map)
├── workbench/ Workbench UI components
└── ui/ Shared UI components
lib/
├── sdk/ SDK exports for production
├── workbench/ React hooks + dev environment
└── export/ Production bundler
server/ MCP server (if included)
```
## Building Your Widget
### 1. Create a component
```tsx
// components/my-widget/index.tsx
import {
useToolInput,
useCallTool,
useTheme,
useCapabilities,
useUpdateModelContext,
useWidgetState,
} from "@/lib/sdk";
export function MyWidget() {
const input = useToolInput<{ query: string }>();
const callTool = useCallTool();
const theme = useTheme();
const capabilities = useCapabilities();
const updateModelContext = useUpdateModelContext();
const [widgetState, setWidgetState] = useWidgetState();
const handleSearch = async () => {
const result = await callTool("search", { query: input.query });
console.log(result.structuredContent);
};
return (
<div
data-theme={theme}
className={theme === "dark" ? "dark bg-background text-foreground" : "bg-background text-foreground"}
style={{ colorScheme: theme }}
>
<p>Query: {input.query}</p>
<button onClick={handleSearch}>Search</button>
{/* Platform-specific features */}
{capabilities.modelContext && (
<button
onClick={() =>
updateModelContext({ structuredContent: { query: input.query } })
}
>
Update model context (host-dependent)
</button>
)}
{capabilities.widgetState && (
<button
onClick={() =>
setWidgetState({
...(widgetState ?? {}),
savedAt: Date.now(),
})
}
>
Save widget state (ChatGPT extensions)
</button>
)}
</div>
);
}
```
### 2. Register in the workbench
Add your component to `lib/workbench/component-registry.tsx`.
### 3. Add mock data
Configure mock tool responses in `lib/workbench/mock-config/`.
### React Hooks Reference
Full documentation: [`lib/workbench/README.md`](lib/workbench/README.md)
#### Universal Hooks (recommended)
These hooks work identically across MCP hosts (including ChatGPT):
| Hook | Description |
| ---- | ----------- |
| `useToolInput<T>()` | Get input arguments from tool call |
| `useTheme()` | Get current theme ("light" or "dark") |
| `useCallTool()` | Call backend tools |
| `useDisplayMode()` | Get/set display mode |
| `useSendMessage()` | Send messages to conversation |
#### Platform Detection (when needed)
| Hook | Description |
| ---- | ----------- |
| `useCapabilities()` | Get full capability object |
| `useFeature(name)` | Check if specific feature is available |
#### Host-Dependent / Extensions (advanced)
These hooks only work on specific platforms. Check availability first:
| Hook | Platform | Description |
| ---- | -------- | ----------- |
| `useWidgetState()` | ChatGPT extensions | Optional OpenAI/ChatGPT host-managed state |
| `useUpdateModelContext()` | Host-dependent | Update model-visible context dynamically |
| `useToolInputPartial()` | Host-dependent | Streaming input during generation |
| `useLog()` | Host-dependent | Structured logging to host |
| `openModal()` helper | ChatGPT extensions (fallback-safe) | Use host modal when available, fallback locally |
`useWidgetState()` is not a standard MCP Apps persistence primitive.
For portable MCP Apps, use app-managed persistence such as localStorage or
server-backed tools.
Tool result metadata (`_meta`) is available via
`readToolResponseMetadata()` when the host exposes
`window.openai.toolResponseMetadata`.
## Platform-Specific Features
MCP App Studio is MCP-first: prefer the MCP Apps bridge (`ui/*`) and feature-detect
optional ChatGPT extensions (`window.openai`) when needed.
| Feature | MCP Apps standard | ChatGPT extensions (optional) |
| ------- | ----------------- | ---------------------------- |
| Tool input | Yes | (alias: `window.openai.toolInput`) |
| Tool result | Yes | (alias: `window.openai.toolOutput`) |
| Tool result metadata (`_meta`) | Yes | Yes (alias: `window.openai.toolResponseMetadata`) |
| Call tool | Yes | (alias: `window.openai.callTool`) |
| Send message | Host-dependent | (alias: `window.openai.sendFollowUpMessage`) |
| Update model context | Host-dependent | (extension: `window.openai.setWidgetState`) |
| Host-managed modal | No | Yes (`window.openai.requestModal`) |
| Widget state persistence | No | Yes (OpenAI/ChatGPT host-managed state) |
| File upload/download | No | Yes |
| Open in app link | No | Yes (`window.openai.setOpenInAppUrl`) |
| Instant checkout | No | Yes (`window.openai.requestCheckout`) *(private beta)* |
Use `useCapabilities()` or `useFeature()` to conditionally enable features.
### Modal Guidance
- Prefer local/in-widget modals for cross-host compatibility.
- Use `window.openai.requestModal()` only when you specifically need a ChatGPT-hosted modal template.
- Always feature-detect and provide a fallback:
```ts
if (typeof window !== "undefined" && window.openai?.requestModal) {
await window.openai.requestModal({ title: "Details", params: { id } });
} else {
// Fallback: local modal state or route navigation
}
```
### Checkout Guidance (ChatGPT beta extension)
- `window.openai.requestCheckout(...)` is currently a ChatGPT private beta extension.
- Treat checkout as optional: feature-detect and provide a fallback (for example, external checkout).
- If you expose a companion deep link in ChatGPT menus, use `window.openai.setOpenInAppUrl({ href })`.
```ts
import { requestCheckout, setOpenInAppUrl } from "@/lib/sdk";
setOpenInAppUrl("https://your-app.com/orders/123");
const outcome = await requestCheckout(
{ id: "checkout_123", payment_mode: "test" },
() => window.open("https://your-app.com/checkout/123", "_blank"),
);
if (outcome.mode === "fallback") {
// Non-ChatGPT host or checkout beta unavailable.
}
```
## Exporting for Production
```bash
npm run export
```
Defaults for `--entry` and `--export-name` are read from `mcp-app-studio.config.json` (written by the CLI when you scaffold a project). You can override them via flags.
Generates:
```
export/
├── widget/
│ └── index.html Self-contained widget
├── manifest.json App manifest
└── README.md Deployment instructions
```
The exported widget uses the `mcp-app-studio` SDK which automatically detects the host platform and uses the appropriate bridge.
### MCP Metadata Defaults (Export)
- `ui.*` metadata is canonical in exported server code.
- Legacy metadata keys from older ChatGPT Apps integrations are not supported in exported MCP metadata.
- Visibility defaults to host default (`["model","app"]`) when no visibility keys are set.
- Widget resources are emitted with MCP Apps MIME type `text/html;profile=mcp-app`.
Widget resource CSP must use MCP-standard keys:
```ts
ui: {
csp: {
connectDomains: ["https://api.example.com"],
resourceDomains: ["https://cdn.example.com"],
frameDomains: ["https://www.youtube.com"],
baseUriDomains: ["https://cdn.example.com"],
},
}
```
## Deploying
### Widget
Deploy `export/widget/` to any static host:
```bash
# Vercel
cd export/widget && vercel deploy
# Netlify
netlify deploy --dir=export/widget
# Or any static host (S3, Cloudflare Pages, etc.)
```
### MCP Server
If you have a `server/` directory:
```bash
cd server
npm run build
# Deploy to Vercel, Railway, Fly.io, etc.
```
### Register with Platform
**For ChatGPT:**
1. Update `manifest.json` with your deployed widget URL
2. Go to [ChatGPT Apps dashboard](https://chatgpt.com/apps)
3. Create a new app and connect your MCP server
4. Test in a new ChatGPT conversation
**For Claude Desktop:**
1. Configure your MCP server in Claude Desktop settings
2. The widget will render when tools with UI are invoked
## Configuration
### SDK Guide (Optional)
The workbench includes an AI-powered SDK guide. To enable:
```bash
cp .env.example .env.local
# then set:
# OPENAI_API_KEY="your-key"
```
### MCP Server CORS
For production, restrict CORS to your widget domain:
```bash
cp server/.env.example server/.env
# then set:
# CORS_ORIGIN=https://your-widget-domain.com
```
### Dark Mode
Exported widgets inherit host theme and token variables. Follow the framework-agnostic contract in `lib/workbench/THEMING_CONTRACT.md`.
At minimum, support `data-theme` / `.dark` and semantic tokens:
```css
.my-element {
background: var(--background);
color: var(--foreground);
border-color: var(--border);
}
```
## Learn More
- [MCP App Studio](https://github.com/assistant-ui/assistant-ui/tree/main/packages/mcp-app-studio) — CLI and SDK documentation
- [MCP Specification](https://modelcontextprotocol.io/specification/) — Model Context Protocol
- [ChatGPT MCP Apps](https://developers.openai.com/apps-sdk/mcp-apps-in-chatgpt) — ChatGPT as an MCP host
