Chatgpt App Builder

ChatGPT App Builder

Build production-ready ChatGPT Apps from concept to App Store submission.

Quick Start

New app?           → Start at Phase 1 (Fit Evaluation)
Have app-spec.md?  → Start at Phase 3 (Implementation)
App built?         → Start at Phase 4 (Testing)
Ready to ship?     → Start at Phase 5 (Deployment)

Phase 1: Fit Evaluation

Goal: Determine if a ChatGPT App is right for this product.

Step 1: Gather Context

Ask the user:

1. What does your product do?
2. Who are your users?
3. What API/data does it expose?
4. What actions can users take?

Step 2: Apply Know/Do/Show Framework

Evaluate against three value pillars (see fit_evaluation.md):

Minimum requirement: At least one pillar must be strong.

Step 3: Check Blockers

Review fit_evaluation.md for:

• Prohibited categories (gambling, adult, crypto speculation)
• Data restrictions (no PCI, PHI, SSN, API keys)
• Age requirements (13+ audience)

Step 4: Create Golden Prompt Set

Draft prompts for discovery testing:

• 5 direct prompts: Explicitly name your product ("Show my TaskFlow tasks")
• 5 indirect prompts: Describe intent without naming ("What should I work on?")
• 3 negative prompts: Similar but shouldn't trigger ("Create a reminder")

Step 5: Write app-spec.md

Create the specification file:

# [Product Name] ChatGPT App Spec

## Product Context
- Name: [Product name]
- API Base: [API URL]
- Auth: [Bearer token / OAuth / None]

## Value Proposition
- Know: [What data does it provide?]
- Do: [What actions can it take?]
- Show: [What UI is needed?]

## Golden Prompts
### Direct (should trigger)
1. ...

### Indirect (should trigger)
1. ...

### Negative (should NOT trigger)
1. ...

Output: app-spec.md in project directory

Phase 2: App Design

Goal: Define complete technical specification.

Step 1: Define Tools (2-5)

Follow one-job-per-tool principle. See chatgpt_app_best_practices.md.

For each tool, specify:

name: service_verb_noun  # e.g., taskflow_get_tasks
title: Human Readable Name
description: Use this when the user wants to... [be specific]
annotations:
  readOnlyHint: true/false
  destructiveHint: true/false
  openWorldHint: true/false
inputSchema:
  param1: type (required/optional)
  param2: enum["a", "b", "c"]
outputStructure:
  content: [text summary for model]
  structuredContent: {machine-readable data}
  _meta: {widget-only data}

Step 2: Decide Widget Needs

Does the app need custom UI?

See widget_development.md for patterns.

Step 3: Plan Authentication

If accessing user-specific data or write operations, auth is required.

See oauth_integration.md for:

• Well-known endpoint setup
• Provider-specific guides (Auth0, Stytch)
• Tool-level securitySchemes

Step 4: Update app-spec.md

Add technical specification:

## Tools

### 1. service_get_items
- **Annotations**: readOnlyHint: true
- **Input**: { status?: "active" | "completed", limit?: number }
- **Output**:
  - content: "Found N items"
  - structuredContent: { items: [...] }
  - _meta: { fullData: [...] }

### 2. service_create_item
- **Annotations**: openWorldHint: true
- **Input**: { title: string, description?: string }
- **Output**: { id, title, created_at }

## Widget
- Type: List with action buttons
- Display modes: inline, fullscreen
- State: { selectedId, filter }

## Authentication
- Required: Yes
- Provider: Auth0
- Scopes: read:items, write:items

Output: Updated app-spec.md with full technical spec

Phase 3: Implementation

Goal: Generate complete working project.

Step 1: Initialize Project

Copy from assets and customize:

# Project structure
myapp-chatgpt/
├── package.json
├── tsconfig.json
├── src/
│   ├── index.ts          # MCP server entry
│   ├── tools/            # Tool handlers
│   ├── widget/           # Widget source
│   └── types/            # TypeScript types
└── scripts/
    └── build-widget.ts   # Widget bundler

See node_chatgpt_app.md for complete patterns.

Step 2: Implement MCP Server

Key components (from assets/server/):

1. HTTP server with SSE transport (required for ChatGPT Apps):

import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { SSEServerTransport } from "@modelcontextprotocol/sdk/server/sse.js";

// GET /mcp - SSE stream connection
// POST /mcp/messages - Message handling

2. Tool definitions with JSON Schema:

const tools: Tool[] = [{
  name: "service_get_items",
  title: "Get Items",
  description: "Use this when the user wants to see items...",
  inputSchema: { type: "object", properties: {...} },
  _meta: {
    "openai/outputTemplate": "ui://widget/app.html",
    "openai/widgetAccessible": true
  },
  annotations: { readOnlyHint: true, destructiveHint: false, openWorldHint: false }
}];

3. Handler registration:

server.setRequestHandler(ListToolsRequestSchema, async () => ({ tools }));
server.setRequestHandler(CallToolRequestSchema, async (request) => {
  const { name, arguments: args } = request.params;
  // Handle tool calls...
  return {
    content: [{ type: "text", text: `Found ${items.length} items` }],
    structuredContent: { items: items.slice(0, 10) },
    _meta: { fullItems: items }
  };
});

Step 3: Implement Widget

Key patterns (from assets/widget/):

// Access data
const output = window.openai.toolOutput;
const meta = window.openai.toolResponseMetadata;

// Invoke tools
await window.openai.callTool("service_action", { id: "123" });

// Persist state
window.openai.setWidgetState({ selectedId: "123" });

// Layout control
window.openai.notifyIntrinsicHeight(400);
await window.openai.requestDisplayMode({ mode: "fullscreen" });

See widget_development.md for React hooks and patterns.

Step 4: Build

npm install
npm run build  # Compiles server + bundles widget

Step 5: Implementation Checklist

Before moving to testing, verify:

Widget Requirements

• Uses Apps SDK UI design tokens (see apps_sdk_ui_tokens.md)
• Implements dark mode with CSS variable architecture
• Uses LoadingDots pattern for loading states (see widget_ui_patterns.md)
• Calls notifyIntrinsicHeight() after all DOM changes
• Includes copy button feedback for copyable content
• Has show more/less for long lists (>3 items)
• Works on mobile (test at 375px width)
• Loading UI guards against re-initialization (see widget_loading_patterns.md)
• SVG animations use .style property, not setAttribute() (see widget_development.md)

Security Requirements

• All user input is validated (see security_patterns.md)
• HTML output uses safe DOM methods (textContent, createElement)
• External image URLs are proxied with domain whitelist and size limits (200KB)
• Rate limiting is implemented per session with LRU eviction

Server Requirements

• /.well-known/openai-apps-challenge endpoint returns challenge token
• /privacy endpoint returns HTML privacy policy
• /terms endpoint returns HTML terms of service
• /mcp endpoint handles SSE connections
• /health or / returns health check JSON
• CORS configured for ChatGPT domains only
• Security headers set on all responses
• Session routing uses direct lookup by sessionId (CRITICAL - see troubleshooting.md)
• Response size under 300KB total (remove duplicates, limit images)

Production Readiness (if deploying to production)

• OAuth tokens stored in database, not in-memory (see oauth_integration.md)
• Mutation tools include idempotency checks (see chatgpt_app_best_practices.md)
• Disambiguation pattern for multi-match scenarios (see chatgpt_app_best_practices.md)
• Confirmation receipts for all mutations (see chatgpt_app_best_practices.md)

Output: Complete project in working directory

Phase 4: Testing

Goal: Verify the app works correctly.

Step 1: Local Testing with MCP Inspector

# Terminal 1: Start server
npm run dev
# Server runs at http://localhost:8000

# Terminal 2: Run inspector
npx @modelcontextprotocol/inspector@latest http://localhost:8000/mcp

Verify:

• All tools appear in inspector
• Tool calls return expected structure
• Widget renders without errors

Step 2: Create HTTPS Tunnel

ngrok http 8000
# Copy the https://xxx.ngrok.app URL

Step 3: Create ChatGPT Connector

1. Go to ChatGPT → Settings → Connectors
2. Enable Developer Mode (Settings → Apps & Connectors → Advanced)
3. Create new connector:
   • Name: Your app name
   • Description: From app-spec.md
   • URL: https://xxx.ngrok.app/mcp
4. Click Create and verify tools appear

Step 4: Test Golden Prompts

In a new ChatGPT conversation:

1. Enable your connector (+ button → More → select connector)
2. Test each golden prompt from app-spec.md
3. Verify:
   • Direct prompts trigger correctly
   • Indirect prompts trigger correctly
   • Negative prompts do NOT trigger
   • Widget renders properly
   • Actions work (if applicable)

Step 5: Iterate

If issues found:

1. Fix code
2. Rebuild: npm run build
3. Refresh connector in ChatGPT settings
4. Re-test

See troubleshooting.md for common issues and solutions.

Output: Working app tested in ChatGPT

Phase 5: Deployment & Submission

Goal: Ship to production and App Store.

Step 1: Deploy to Production

Generate deployment configs from assets/deploy/:

Fly.io (recommended):

fly launch
fly deploy

Vercel/Cloudflare: Ensure streaming HTTP support.

Step 2: Update Connector

1. Replace ngrok URL with production URL
2. Verify connection in ChatGPT settings

Step 3: Pre-Submission Checklist

See submission_requirements.md:

Required:

• Organization verified on OpenAI Platform
• All tools have clear descriptions
• Annotations correct (readOnlyHint, destructiveHint, openWorldHint)
• No prohibited content/commerce
• No restricted data collection
• Widget renders on mobile
• Test credentials with sample data prepared

If auth required:

• Well-known endpoints accessible
• Test account credentials documented
• OAuth flow completes successfully

Step 4: Submit

1. Go to platform.openai.com/apps-manage
2. Enter MCP server URL
3. Add OAuth metadata (if applicable)
4. Complete submission form
5. Submit for review

Step 5: Post-Submission

• Monitor email for review status
• Address any reviewer feedback
• Click Publish after approval

Output: App live in ChatGPT App Store

External Resources

• OpenAI Apps SDK Docs
• Apps SDK Examples
• Apps SDK UI Kit
• MCP Protocol Spec