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ChatGPT Apps SDK Development Guide

Overview

Create ChatGPT Apps with interactive widgets that render rich UI inside ChatGPT conversations. Apps combine MCP servers (providing tools) with embedded HTML widgets that communicate via the window.openai API.

window.openai API Reference

Widgets communicate with ChatGPT through these APIs:

sendFollowUpMessage (Recommended for Actions)

Send a follow-up prompt to ChatGPT on behalf of the user:

// Trigger a follow-up conversation
if (window.openai?.sendFollowUpMessage) {
  await window.openai.sendFollowUpMessage({
    prompt: 'Summarize this chapter for me'
  });
}

Use for: Action buttons that suggest next steps (summarize, explain, etc.)

toolOutput

Send structured data back from widget interactions:

// Send data back to ChatGPT
if (window.openai?.toolOutput) {
  window.openai.toolOutput({
    action: 'chapter_selected',
    chapter: 1,
    title: 'Introduction'
  });
}

Use for: Selections, form submissions, user choices that feed into tool responses.

callTool

Call another MCP tool from within a widget:

// Call a tool directly
if (window.openai?.callTool) {
  await window.openai.callTool({
    name: 'read-chapter',
    arguments: { chapter: 2 }
  });
}

Use for: Navigation between content, chaining tool calls.

Critical: Button Interactivity Limitations

Important Discovery: Widget buttons may render as static UI elements rather than interactive JavaScript buttons. ChatGPT renders widgets in a sandboxed iframe where some click handlers don't fire reliably.

What Works

• sendFollowUpMessage - Reliably triggers follow-up prompts
• Simple onclick handlers for toolOutput calls
• CSS hover effects and visual feedback

What May Not Work

• Complex interactive JavaScript (selection APIs, etc.)
• Multiple chained tool calls from buttons
• window.getSelection() for text selection features

Recommended Pattern: Suggestion Buttons

Instead of complex interactions, use simple buttons that suggest prompts:

<div class="action-buttons">
  <button class="btn btn-primary" id="summarizeBtn">
    📝 Summarize Chapter
  </button>
  <button class="btn btn-primary" id="explainBtn">
    💡 Explain Key Concepts
  </button>
</div>

<script>
document.getElementById('summarizeBtn')?.addEventListener('click', async () => {
  if (window.openai?.sendFollowUpMessage) {
    await window.openai.sendFollowUpMessage({
      prompt: 'Summarize this chapter for me'
    });
  }
});

document.getElementById('explainBtn')?.addEventListener('click', async () => {
  if (window.openai?.sendFollowUpMessage) {
    await window.openai.sendFollowUpMessage({
      prompt: 'Explain the key concepts from this chapter'
    });
  }
});
</script>

Architecture Summary

┌─────────────────────────────────────────────────────────────────┐
│                        ChatGPT UI                                │
│  ┌─────────────────────────────────────────────────────────────┐│
│  │                    Widget (iframe)                          ││
│  │   HTML + CSS + JS                                          ││
│  │   Calls: window.openai.toolOutput({action: "...", ...})    ││
│  └─────────────────────────────────────────────────────────────┘│
│                              │                                   │
│                              ▼                                   │
│                     ChatGPT Backend                              │
│                              │                                   │
│                              ▼                                   │
│              MCP Server (FastMCP + HTTP)                         │
│              - Tools: open-book, read-chapter, etc.              │
│              - Resources: widget HTML (text/html+skybridge)      │
│              - Response includes: _meta["openai.com/widget"]     │
└─────────────────────────────────────────────────────────────────┘

Quick Start

1. Create MCP server with FastMCP and widget resources
2. Define widget HTML that uses window.openai.toolOutput
3. Add response metadata with _meta["openai.com/widget"]
4. Expose via ngrok for ChatGPT access
5. Register in ChatGPT Developer Mode settings

Widget HTML Requirements

Basic Widget Template

<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>My Widget</title>
  <style>
    * { margin: 0; padding: 0; box-sizing: border-box; }
    body {
      font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif;
      background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
      min-height: 100vh;
      padding: 24px;
      color: white;
    }
    .container { max-width: 600px; margin: 0 auto; }
    .card {
      background: rgba(255,255,255,0.95);
      color: #333;
      padding: 24px;
      border-radius: 16px;
      box-shadow: 0 10px 40px rgba(0,0,0,0.2);
    }
    .btn {
      background: #667eea;
      color: white;
      border: none;
      padding: 12px 24px;
      border-radius: 8px;
      cursor: pointer;
      font-size: 16px;
    }
    .btn:hover { background: #5a6fd6; }
  </style>
</head>
<body>
  <div class="container">
    <div class="card">
      <h1>Widget Title</h1>
      <p>Widget content here</p>
      <button class="btn" onclick="handleAction()">Click Me</button>
    </div>
  </div>
  <script>
    function handleAction() {
      // Communicate back to ChatGPT
      if (window.openai && window.openai.toolOutput) {
        window.openai.toolOutput({
          action: "button_clicked",
          data: { timestamp: Date.now() }
        });
      }
    }
  </script>
</body>
</html>

Key Widget Rules

1. Always check window.openai.toolOutput before calling
2. Use inline styles - external CSS may not load reliably
3. Keep widgets self-contained - all HTML/CSS/JS in one file
4. Test with actual ChatGPT - browser preview won't have window.openai

MCP Server Setup (FastMCP Python)

Project Structure

my_chatgpt_app/
├── main.py              # FastMCP server with widgets
├── requirements.txt     # Dependencies
└── .env                 # Environment variables

requirements.txt

mcp[cli]>=1.9.2
uvicorn>=0.32.0
httpx>=0.28.0
python-dotenv>=1.0.0

main.py Template

import mcp.types as types
from mcp.server.fastmcp import FastMCP

# Widget MIME type for ChatGPT
MIME_TYPE = "text/html+skybridge"

# Define your widget HTML
MY_WIDGET = '''<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <style>
    body { font-family: sans-serif; padding: 20px; }
    .container { max-width: 500px; margin: 0 auto; }
  </style>
</head>
<body>
  <div class="container">
    <h1>Hello from Widget!</h1>
    <p>This content renders inside ChatGPT.</p>
  </div>
</body>
</html>'''

# Widget registry
WIDGETS = {
    "main-widget": {
        "uri": "ui://widget/main.html",
        "html": MY_WIDGET,
        "title": "My Widget",
    },
}

# Create FastMCP server
mcp = FastMCP("My ChatGPT App")


@mcp.resource(
    uri="ui://widget/{widget_name}.html",
    name="Widget Resource",
    mime_type=MIME_TYPE
)
def widget_resource(widget_name: str) -> str:
    """Serve widget HTML."""
    widget_key = f"{widget_name}"
    if widget_key in WIDGETS:
        return WIDGETS[widget_key]["html"]
    return WIDGETS["main-widget"]["html"]


def _embedded_widget_resource(widget_id: str) -> types.EmbeddedResource:
    """Create embedded widget resource for tool response."""