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openai-apps-mcp▌

jezweb/claude-skills · updated Apr 8, 2026

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$npx skills add https://github.com/jezweb/claude-skills --skill openai-apps-mcp
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summary

Build ChatGPT apps with MCP servers on Cloudflare Workers, extending ChatGPT with custom tools and interactive HTML/JS widgets.

  • ›Implements JSON-RPC 2.0 MCP protocol with tool registration, execution, and widget resource serving via ASSETS binding
  • ›Requires CORS allowlist for https://chatgpt.com , ui://widget/ URI prefix for widgets, and text/html+skybridge MIME type
  • ›Prevents 14 documented issues including CORS blocks, widget 404s, MIME type misconfigurations, SSE timeouts, and Next.js
skill.md

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/[email protected], [email protected], [email protected], [email protected]

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/[email protected] [email protected] [email protected]
npm install -D @cloudflare/[email protected] [email protected]

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 14 documented issues:

Issue #1: CORS Policy Blocks MCP Endpoint

Error: Access to fetch blocked by CORS policy Fix: app.use('/mcp/*', cors({ origin:

how to use openai-apps-mcp

How to use openai-apps-mcp on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • ›Cursor installed and configured on your development machine
  • ›Node.js version 16.0+ with npm package manager (verify with node --version)
  • ›Active project directory or workspace where you want to add openai-apps-mcp
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/jezweb/claude-skills --skill openai-apps-mcp

The skills CLI fetches openai-apps-mcp from GitHub repository jezweb/claude-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/openai-apps-mcp

Reload or restart Cursor to activate openai-apps-mcp. Access the skill through slash commands (e.g., /openai-apps-mcp) or your agent's skill management interface.

⚠

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases▌

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

✓

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

✓

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

✓

Improve work quality by 30-40% with less effort

Implementation Guide▌

Prerequisites

  • ›Claude Desktop or compatible AI client with skill support
  • ›Clear understanding of task or problem to solve
  • ›Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • ⚠Expecting perfect results without iteration
  • ⚠Not providing enough context in prompts
  • ⚠Using skill for tasks outside its intended scope
  • ⚠Accepting outputs without review and validation

Best Practices▌

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • −Don't use without understanding skill limitations
  • −Don't skip validation of outputs
  • −Don't share sensitive information in prompts
  • −Don't expect skill to replace human judgment

💡 Pro Tips

  • ★Be specific about desired format and style
  • ★Ask for multiple options to choose from
  • ★Request explanations to understand reasoning
  • ★Combine AI efficiency with human expertise

When to Use This▌

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path▌

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.6★★★★★45 reviews
  • ★★★★★Sakshi Patil· Dec 28, 2024

    openai-apps-mcp reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Fatima Gupta· Dec 28, 2024

    Registry listing for openai-apps-mcp matched our evaluation — installs cleanly and behaves as described in the markdown.

  • ★★★★★Arya Menon· Dec 20, 2024

    openai-apps-mcp has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • ★★★★★Maya Thompson· Dec 8, 2024

    Useful defaults in openai-apps-mcp — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • ★★★★★Fatima Wang· Nov 27, 2024

    openai-apps-mcp is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • ★★★★★Yusuf Nasser· Nov 11, 2024

    openai-apps-mcp fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • ★★★★★Maya Patel· Oct 18, 2024

    Keeps context tight: openai-apps-mcp is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • ★★★★★Yuki Liu· Oct 2, 2024

    We added openai-apps-mcp from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • ★★★★★Mia Mensah· Sep 21, 2024

    Useful defaults in openai-apps-mcp — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • ★★★★★Mia Diallo· Sep 13, 2024

    Keeps context tight: openai-apps-mcp is the kind of skill you can hand to a new teammate without a long onboarding doc.

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