openai apps mcp

Building OpenAI Apps with Stateless MCP Servers

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Install skill "openai apps mcp" with this command: npx skills add jezweb/claude-skills/jezweb-claude-skills-openai-apps-mcp

Building OpenAI Apps with Stateless MCP Servers

Status: Production Ready Last Updated: 2026-01-21 Dependencies: cloudflare-worker-base , hono-routing (optional) Latest Versions: @modelcontextprotocol/sdk@1.25.3, hono@4.11.3, zod@4.3.5, wrangler@4.58.0

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@1.25.3 hono@4.11.3 zod@4.3.5 npm install -D @cloudflare/vite-plugin@1.17.1 vite@7.2.4

  1. 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 } }

  1. 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;

  1. 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>

  1. 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 14 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">&#x3C;/script>

Issue #9: Hono Global Response Override Breaks Next.js (v1.25.0-1.25.2)

Error: No response is returned from route handler (Next.js App Router) Source: GitHub Issue #1369 Affected Versions: v1.25.0 to v1.25.2 Fixed In: v1.25.3 Why It Happens: Hono (MCP SDK dependency) overwrites global.Response , breaking frameworks that extend it (Next.js, Remix, SvelteKit). NextResponse instanceof check fails. Prevention:

  • Upgrade to v1.25.3+ (recommended)

  • Before fix: Use webStandardStreamableHTTPServerTransport instead

  • Or: Run MCP server on separate port from Next.js/Remix/SvelteKit app

// ✅ v1.25.3+ - Fixed const transport = new StreamableHTTPServerTransport({ sessionIdGenerator: undefined, });

// ✅ v1.25.0-1.25.2 - Workaround import { webStandardStreamableHTTPServerTransport } from '@modelcontextprotocol/sdk/server/index.js'; const transport = webStandardStreamableHTTPServerTransport({ sessionIdGenerator: undefined, });

Issue #10: Elicitation (User Input) Fails on Cloudflare Workers

Error: EvalError: Code generation from strings disallowed

Source: GitHub Issue #689 Why It Happens: Internal AJV v6 validator uses prohibited APIs on edge platforms Prevention: Avoid elicitInput() on edge platforms (Cloudflare Workers, Vercel Edge, Deno Deploy)

Workaround:

// ❌ Don't use on Cloudflare Workers const userInput = await server.elicitInput({ prompt: "What is your name?", schema: { type: "string" } });

// ✅ Use tool parameters instead server.setRequestHandler(CallToolRequestSchema, async (request) => { const { name } = request.params.arguments as { name: string }; // User provides via tool call, not elicitation });

Status: Requires MCP SDK v2 to fix properly. Track PR #844.

Issue #11: SSE Transport Statefulness Breaks Serverless Deployments

Error: 400: No transport found for sessionId

Source: GitHub Issue #273 Why It Happens: SSEServerTransport relies on in-memory session storage. In serverless environments (AWS Lambda, Cloudflare Workers), the initial GET /sse request may be handled by Instance A, but subsequent POST /messages requests land on Instance B, which lacks the in-memory state. Prevention: Use Streamable HTTP transport (added in v1.24.0) instead of SSE for serverless deployments Solution: For stateful SSE, deploy to non-serverless environments (VPS, long-running containers)

Official Status: Fixed by introducing Streamable HTTP (v1.24+) - now the recommended standard for serverless.

Issue #12: OAuth Configuration Requires TWO Separate Apps

Source: Cloudflare Remote MCP Server Docs Why It Happens: OAuth providers validate redirect URLs strictly. Localhost and production have different URLs, so they need separate OAuth client registrations. Prevention:

Development OAuth App

Callback URL: http://localhost:8788/callback

Production OAuth App

Callback URL: https://my-mcp-server.workers.dev/callback

Additional Requirements:

  • KV namespace for auth state storage (create manually)

  • COOKIE_ENCRYPTION_KEY env var: openssl rand -hex 32

  • Client restart required after config changes

Issue #13: Widget State Over 4k Tokens Causes Performance Issues (Community-sourced)

Source: OpenAI Apps SDK - ChatGPT UI Why It Happens: Widget state persists only to a single widget instance tied to one conversation message. State is reset when users submit via the main chat composer instead of widget controls. Prevention: Keep state payloads under 4k tokens for optimal performance

// ✅ Good - Lightweight state window.openai.setWidgetState({ selectedId: "item-123", view: "grid" });

// ❌ Bad - Will cause performance issues window.openai.setWidgetState({ items: largeArray, // Don't store full datasets history: conversationLog, // Don't store conversation history cache: expensiveComputation // Don't cache large results });

Best Practice:

  • Store only UI state (selected items, view mode, filters)

  • Fetch data from MCP server on widget mount

  • Use tool calls to persist important data

Issue #14: Widget-Initiated Tool Calls Fail Without Permission Flag (Community-sourced)

Source: OpenAI Apps SDK - ChatGPT UI Why It Happens: Components initiating tool calls via window.openai.callTool() require the tool marked as "able to be initiated by the component" on the MCP server. Without this flag, calls fail silently. Prevention: Mark tools as widgetCallable: true in annotations

// MCP Server - Mark tool as widget-callable server.setRequestHandler(ListToolsRequestSchema, async () => ({ tools: [{ name: 'update_item', description: 'Update an item', inputSchema: { /* ... */ }, annotations: { openai: { outputTemplate: 'ui://widget/item.html', // ✅ Required for widget-initiated calls widgetCallable: true } } }] }));

// Widget - Now allowed to call tool window.openai.callTool({ name: 'update_item', arguments: { id: itemId, status: 'completed' } });

Widget Development Best Practices

File Upload Limitations (Community-sourced)

Source: OpenAI Apps SDK - ChatGPT UI

window.openai.uploadFile() only supports 3 image formats: image/png , image/jpeg , and image/webp . Other formats fail silently.

// ✅ Supported window.openai.uploadFile({ accept: 'image/png,image/jpeg,image/webp' });

// ❌ Not supported (fails silently) window.openai.uploadFile({ accept: 'application/pdf' }); window.openai.uploadFile({ accept: 'text/csv' });

Alternative for Other File Types:

  • Use base64 encoding in tool arguments

  • Request user paste text content

  • Use external upload service (S3, R2) and pass URL

Tool Performance Targets (Community-sourced)

Source: OpenAI Apps SDK - Troubleshooting

Tool calls exceeding "a few hundred milliseconds" cause UI sluggishness in ChatGPT. Official docs recommend profiling backends and implementing caching for slow operations.

Performance Targets:

  • < 200ms: Ideal response time

  • 200-500ms: Acceptable but noticeable

  • 500ms: Sluggish, needs optimization

Optimization Strategies:

// 1. Cache expensive computations const cache = new Map(); if (cache.has(key)) return cache.get(key); const result = await expensiveOperation(); cache.set(key, result);

// 2. Use KV/D1 for pre-computed data const cached = await env.KV.get(result:${id}); if (cached) return JSON.parse(cached);

// 3. Paginate large datasets return { content: [{ type: 'text', text: 'First 20 results...' }], _meta: { hasMore: true, nextPage: 2 } };

// 4. Move slow work to async tasks // Return immediately, update via follow-up

MCP SDK 1.25.x Updates (December 2025)

Breaking Changes from @modelcontextprotocol/sdk@1.24.x → 1.25.x:

  • Removed loose type exports (Prompts, Resources, Roots, Sampling, Tools) - use specific schemas

  • ES2020 target required (previous: ES2018)

  • setRequestHandler is now typesafe - incorrect schemas throw type errors

New Features:

  • Tasks (v1.24.0+): Long-running operations with progress tracking

  • Sampling with Tools (v1.24.0+): Tools can request model sampling

  • OAuth Client Credentials (M2M): Machine-to-machine authentication

Migration: If using loose type imports, update to specific schema imports:

// ❌ Old (removed in 1.25.0) import { Tools } from '@modelcontextprotocol/sdk/types.js';

// ✅ New (1.25.1+) import { ListToolsRequestSchema, CallToolRequestSchema } from '@modelcontextprotocol/sdk/types.js';

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 (not error.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": "^1.25.3", "hono": "^4.11.3", "zod": "^4.3.5" }, "devDependencies": { "@cloudflare/vite-plugin": "^1.17.1", "@cloudflare/workers-types": "^4.20260103.0", "vite": "^7.2.4", "wrangler": "^4.54.0" } }

Official Documentation

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 14 known issues prevented, zero errors in production

Community Resources

Deployment Tools

Cloudflare One-Click Deploy: Deploy MCP servers to Cloudflare Workers with pre-built templates and auto-configured CI/CD. Includes OAuth wrapper and Python support.

Frameworks

Skybridge (Community): React-focused framework with HMR support for widgets and enhanced MCP server helpers. Unofficial but actively maintained.

Note: Community frameworks are not officially supported. Use at your own discretion

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