| name | OpenAI Apps MCP |
| description | Build ChatGPT apps with MCP servers on Cloudflare Workers. Extend ChatGPT with custom tools and interactive widgets (HTML/JS UI). Use when: developing ChatGPT extensions, implementing MCP servers, or troubleshooting CORS blocking (allow chatgpt.com), widget 404s (missing ui://widget/), wrong MIME type (text/html+skybridge), or ASSETS binding undefined. |
| allowed-tools | Read, Write, Edit, Bash, Glob, Grep |
Building OpenAI Apps with Stateless MCP Servers
Status: Production Ready
Last Updated: 2025-11-26
Dependencies: cloudflare-worker-base, hono-routing (optional)
Latest Versions: @modelcontextprotocol/sdk@1.23.0, hono@4.10.6, zod@4.1.13, wrangler@4.50.0
Overview
Build ChatGPT Apps using MCP (Model Context Protocol) servers on Cloudflare Workers. Extends ChatGPT with custom tools and interactive widgets (HTML/JS UI rendered in iframe).
Architecture: ChatGPT → MCP endpoint (JSON-RPC 2.0) → Tool handlers → Widget resources (HTML)
Status: Apps available to Business/Enterprise/Edu (GA Nov 13, 2025). MCP Apps Extension (SEP-1865) formalized Nov 21, 2025.
Quick Start
1. Scaffold & Install
npm create cloudflare@latest my-openai-app -- --type hello-world --ts --git --deploy false
cd my-openai-app
npm install @modelcontextprotocol/sdk@1.23.0 hono@4.10.6 zod@4.1.13
npm install -D @cloudflare/vite-plugin@1.15.2 vite@7.2.4
2. Configure wrangler.jsonc
{
"name": "my-openai-app",
"main": "dist/index.js",
"compatibility_flags": ["nodejs_compat"], // Required for MCP SDK
"assets": {
"directory": "dist/client",
"binding": "ASSETS" // Must match TypeScript
}
}
3. Create MCP Server (src/index.ts)
import { Hono } from 'hono';
import { cors } from 'hono/cors';
import { Server } from '@modelcontextprotocol/sdk/server/index.js';
import { ListToolsRequestSchema, CallToolRequestSchema } from '@modelcontextprotocol/sdk/types.js';
const app = new Hono<{ Bindings: { ASSETS: Fetcher } }>();
// CRITICAL: Must allow chatgpt.com
app.use('/mcp/*', cors({ origin: 'https://chatgpt.com' }));
const mcpServer = new Server(
{ name: 'my-app', version: '1.0.0' },
{ capabilities: { tools: {}, resources: {} } }
);
// Tool registration
mcpServer.setRequestHandler(ListToolsRequestSchema, async () => ({
tools: [{
name: 'hello',
description: 'Use this when user wants to see a greeting',
inputSchema: {
type: 'object',
properties: { name: { type: 'string' } },
required: ['name']
},
annotations: {
openai: { outputTemplate: 'ui://widget/hello.html' } // Widget URI
}
}]
}));
// Tool execution
mcpServer.setRequestHandler(CallToolRequestSchema, async (request) => {
if (request.params.name === 'hello') {
const { name } = request.params.arguments as { name: string };
return {
content: [{ type: 'text', text: `Hello, ${name}!` }],
_meta: { initialData: { name } } // Passed to widget
};
}
throw new Error(`Unknown tool: ${request.params.name}`);
});
app.post('/mcp', async (c) => {
const body = await c.req.json();
const response = await mcpServer.handleRequest(body);
return c.json(response);
});
app.get('/widgets/*', async (c) => c.env.ASSETS.fetch(c.req.raw));
export default app;
4. Create Widget (src/widgets/hello.html)
<!DOCTYPE html>
<html>
<head>
<style>
body { margin: 0; padding: 20px; font-family: system-ui; }
</style>
</head>
<body>
<div id="greeting">Loading...</div>
<script>
if (window.openai && window.openai.getInitialData) {
const data = window.openai.getInitialData();
document.getElementById('greeting').textContent = `Hello, ${data.name}! 👋`;
}
</script>
</body>
</html>
5. Deploy
npm run build
npx wrangler deploy
npx @modelcontextprotocol/inspector https://my-app.workers.dev/mcp
Critical Requirements
CORS: Must allow https://chatgpt.com on /mcp/* routes
Widget URI: Must use ui://widget/ prefix (e.g., ui://widget/map.html)
MIME Type: Must be text/html+skybridge for HTML resources
Widget Data: Pass via _meta.initialData (accessed via window.openai.getInitialData())
Tool Descriptions: Action-oriented ("Use this when user wants to...")
ASSETS Binding: Serve widgets from ASSETS, not bundled in worker code
SSE: Send heartbeat every 30s (100s timeout on Workers)
Known Issues Prevention
This skill prevents 8 documented issues:
Issue #1: CORS Policy Blocks MCP Endpoint
Error: Access to fetch blocked by CORS policy
Fix: app.use('/mcp/*', cors({ origin: 'https://chatgpt.com' }))
Issue #2: Widget Returns 404 Not Found
Error: 404 (Not Found) for widget URL
Fix: Use ui://widget/ prefix (not resource:// or /widgets/)
annotations: { openai: { outputTemplate: 'ui://widget/map.html' } }
Issue #3: Widget Displays as Plain Text
Error: HTML source code visible instead of rendered widget
Fix: MIME type must be text/html+skybridge (not text/html)
server.setRequestHandler(ListResourcesRequestSchema, async () => ({
resources: [{ uri: 'ui://widget/map.html', mimeType: 'text/html+skybridge' }]
}));
Issue #4: ASSETS Binding Undefined
Error: TypeError: Cannot read property 'fetch' of undefined
Fix: Binding name in wrangler.jsonc must match TypeScript
{ "assets": { "binding": "ASSETS" } } // wrangler.jsonc
type Bindings = { ASSETS: Fetcher }; // index.ts
Issue #5: SSE Connection Drops After 100 Seconds
Error: SSE stream closes unexpectedly Fix: Send heartbeat every 30s (Workers timeout at 100s inactivity)
const heartbeat = setInterval(async () => {
await stream.writeSSE({ data: JSON.stringify({ type: 'heartbeat' }), event: 'ping' });
}, 30000);
Issue #6: ChatGPT Doesn't Suggest Tool
Error: Tool registered but never appears in suggestions Fix: Use action-oriented descriptions
// ✅ Good: 'Use this when user wants to see a location on a map'
// ❌ Bad: 'Shows a map'
Issue #7: Widget Can't Access Initial Data
Error: window.openai.getInitialData() returns undefined
Fix: Pass data via _meta.initialData
return {
content: [{ type: 'text', text: 'Here is your map' }],
_meta: { initialData: { location: 'SF', zoom: 12 } }
};
Issue #8: Widget Scripts Blocked by CSP
Error: Refused to load script (CSP directive)
Fix: Use inline scripts or same-origin scripts. Third-party CDNs blocked.
<!-- ✅ Works --> <script>console.log('ok');</script>
<!-- ❌ Blocked --> <script src="https://cdn.example.com/lib.js"></script>
Zod 4.0 Migration Notes (MAJOR UPDATE - July 2025)
Breaking Changes from zod@3.x → 4.x:
.default()now expects input type (not output type). Use.prefault()for old behavior.- ZodError:
error.issues(noterror.errors) .merge()and.superRefine()deprecated- Optional properties with defaults now always apply
Performance: 14x faster string parsing, 7x faster arrays, 6.5x faster objects
Migration: Update validation code:
// Zod 4.x
try {
const validated = schema.parse(data);
} catch (error) {
if (error instanceof z.ZodError) {
return { content: [{ type: 'text', text: error.issues.map(e => e.message).join(', ') }] };
}
}
Dependencies
{
"dependencies": {
"@modelcontextprotocol/sdk": "^1.23.0",
"hono": "^4.10.6",
"zod": "^4.1.13"
},
"devDependencies": {
"@cloudflare/vite-plugin": "^1.15.2",
"@cloudflare/workers-types": "^4.20250531.0",
"vite": "^7.2.4",
"wrangler": "^4.50.0"
}
}
Official Documentation
- MCP Specification: https://modelcontextprotocol.io/ (Latest: 2025-11-25)
- MCP SDK: https://github.com/modelcontextprotocol/typescript-sdk
- OpenAI Apps SDK: https://developers.openai.com/apps-sdk
- MCP Apps Extension (SEP-1865): http://blog.modelcontextprotocol.io/posts/2025-11-21-mcp-apps/
- Context7 Library ID: /modelcontextprotocol/typescript-sdk
Production Reference
Open Source Example: https://github.com/jezweb/chatgpt-app-sdk (portfolio carousel widget)
- Live in Production: Rendering in ChatGPT Business
- MCP Server: Full JSON-RPC 2.0 implementation with tools + resources (~310 lines)
- Widget Integration: WordPress API →
window.openai.toolOutput→ React carousel - Database: D1 (SQLite) for contact form submissions
- Stack: Hono 4 + React 19 + Tailwind v4 + Drizzle ORM
- Key Files:
/src/lib/mcp/server.ts- Complete MCP handler/src/server/tools/portfolio.ts- Tool with widget annotations/src/widgets/PortfolioWidget.tsx- Data access pattern
- Verified: All 8 known issues prevented, zero errors in production