Chatgpt Apps

ChatGPT Apps Builder

Complete workflow for building, testing, and deploying ChatGPT Apps from concept to production.

Commands

Table of Contents

1. Create New App 2. Add MCP Tool 3. Add Widget 4. Add Authentication 5. Add Database 6. Generate Golden Prompts 7. Validate App 8. Test App 9. Deploy App 10. Resume App

1. Create New App

Purpose: Create a new ChatGPT App from concept to working code.

Workflow

#### Phase 1: Conceptualization

1. Ask for the app idea "What ChatGPT App would you like to build? Describe what it does and the problem it solves."

2. Analyze against UX Principles - Conversational Leverage: What can users accomplish through natural language? - Native Fit: How does this integrate with ChatGPT's conversational flow? - Composability: Can tools work independently and combine with other apps?

3. Check for Anti-Patterns - Static website content display - Complex multi-step workflows requiring external tabs - Duplicating ChatGPT's native capabilities - Ads or upsells

4. Define Use Cases Create 3-5 primary use cases with user stories.

#### Phase 2: Design

1. Tool Topology - Query tools (readOnlyHint: true) - Mutation tools (destructiveHint: false) - Destructive tools (destructiveHint: true) - Widget tools (return UI with _meta) - External API tools (openWorldHint: true)

2. Widget Design For each widget: - id - unique identifier (kebab-case) - name - display name - description - what it shows - mockData - sample data for preview

3. Data Model Design entities and relationships.

4. Auth Requirements - Single-user (no auth needed) - Multi-user (Auth0 or Supabase Auth)

#### Phase 3: Implementation

Generate complete application with this structure:

{app-name}/
├── package.json
├── tsconfig.server.json
├── setup.sh
├── START.sh
├── .env.example
├── .gitignore
└── server/
    └── index.ts

Critical Requirements:

#### Phase 4: Testing

#### Phase 5: Deployment

2. Add MCP Tool

Purpose: Add a new MCP tool to your ChatGPT App.

Workflow

1. Gather Information - What does this tool do? - What inputs does it need? - What does it return?

2. Classify Tool Type - Query (readOnlyHint: true) - Fetches data - Mutation (destructiveHint: false) - Creates/updates data - Destructive (destructiveHint: true) - Deletes data - Widget - Returns UI content - External (openWorldHint: true) - Calls external APIs

3. Design Input Schema Create Zod schema with appropriate types and descriptions.

4. Generate Tool Handler Use chatgpt-mcp-generator agent to create: - Tool handler in server/tools/ - Zod schema export - Type exports - Database queries (if needed)

5. Register Tool Update server/index.ts with metadata:

   {
     name: "my-tool",
     _meta: {
       "openai/toolInvocation/invoking": "Loading...",
       "openai/toolInvocation/invoked": "Done",
       "openai/outputTemplate": "ui://widget/my-widget.html", // if widget
     }
   }
   

6. Update State Add tool to .chatgpt-app/state.json.

Tool Naming

Annotations Guide

| Scenario | readOnlyHint | destructiveHint | openWorldHint | |----------|--------------|-----------------|---------------| | List/Get | true | false | false | | Create/Update | false | false | false | | Delete | false | true | false | | External API | varies | varies | true |

3. Add Widget

Purpose: Add inline HTML widgets with HTML/CSS/JS and Apps SDK integration.

5 Widget Patterns

1. Card Grid - Multiple items in grid 2. Stats Dashboard - Key metrics display 3. Table - Tabular data 4. Bar Chart - Simple visualizations 5. Detail Widget - Single item details

Workflow

1. Gather Information - Widget purpose and data - Visual design (cards, table, chart, etc.) - Interactivity needs

2. Define Data Shape Document expected structure with TypeScript interface.

3. Add Widget Config

   const widgets: WidgetConfig[] = [
     {
       id: "my-widget",
       name: "My Widget",
       description: "Displays data",
       templateUri: "ui://widget/my-widget.html",
       invoking: "Loading...",
       invoked: "Ready",
       mockData: { /* sample */ },
     },
   ];
   

4. Add Widget HTML Generate HTML with: - Preview mode support (window.PREVIEW_DATA) - OpenAI Apps SDK integration (window.openai.toolOutput) - Event listeners (openai:set_globals) - Polling fallback (100ms, 10s timeout)

5. Create/Update Tool Link tool to widget via widgetId.

6. Test Widget Preview at /preview/{widget-id} with mock data.

Widget HTML Structure

(function() {
  let rendered = false;
  function render(data) {
    if (rendered || !data) return;
    rendered = true;
    // Render logic
  }
  function tryRender() {
    if (window.PREVIEW_DATA) { render(window.PREVIEW_DATA); return; }
    if (window.openai?.toolOutput) { render(window.openai.toolOutput); }
  }
  window.addEventListener('openai:set_globals', tryRender);
  const poll = setInterval(() => {
    if (window.openai?.toolOutput || window.PREVIEW_DATA) {
      tryRender();
      clearInterval(poll);
    }
  }, 100);
  setTimeout(() => clearInterval(poll), 10000);
  tryRender();
})();

4. Add Authentication

Purpose: Configure authentication using Auth0 or Supabase Auth.

When to Add

Providers

Auth0:

Supabase Auth:

Workflow

1. Choose Provider Ask user preference based on needs.

2. Guide Setup - Auth0: Create application, configure callback URLs, get credentials - Supabase: Already configured with database setup

3. Generate Auth Code Use chatgpt-auth-generator agent to create: - Session management middleware - User subject extraction - Token validation

4. Update Server Add auth middleware to protect routes.

5. Update Environment

   # Auth0
   AUTH0_DOMAIN=your-tenant.auth0.com
   AUTH0_CLIENT_ID=...
   AUTH0_CLIENT_SECRET=...
   
   # Supabase (from database setup)
   SUPABASE_URL=...
   SUPABASE_ANON_KEY=...
   

6. Test Verify login flow and user isolation.

5. Add Database

Purpose: Configure PostgreSQL database using Supabase.

When to Add

Workflow

1. Check Supabase Setup Verify account and project exist.

2. Gather Credentials - Project URL - Anon key (public) - Service role key (server-side)

3. Define Entities For each entity, specify: - Fields and types - Relationships - Indexes

4. Generate Schema Use chatgpt-database-generator agent to create SQL with: - id (UUID primary key) - user_subject (varchar, indexed) - created_at (timestamptz) - updated_at (timestamptz) - RLS policies for user isolation

5. Setup Connection Pool

   import { createClient } from '@supabase/supabase-js';
   
   const supabase = createClient(
     process.env.SUPABASE_URL!,
     process.env.SUPABASE_SERVICE_ROLE_KEY!
   );
   

6. Apply Migrations Run SQL in Supabase dashboard or via migration tool.

Query Pattern

Always filter by user_subject:

const { data } = await supabase
  .from('tasks')
  .select('*')
  .eq('user_subject', userSubject);

6. Generate Golden Prompts

Purpose: Generate test prompts to validate ChatGPT correctly invokes tools.

Why Important

3 Categories

1. Direct Prompts - Explicit tool invocation - "Show me my task list" - "Create a new task called..."

2. Indirect Prompts - Outcome-based, ChatGPT should infer tool - "What do I need to do today?" - "Help me organize my work"

3. Negative Prompts - Should NOT trigger tool - "What is a task?" - "Tell me about project management"

Workflow

1. Analyze Tools Review each tool's purpose and inputs.

2. Generate Prompts For each tool, create: - 5+ direct prompts - 5+ indirect prompts - 3+ negative prompts - 2+ edge case prompts

3. Best Practices - Tool descriptions start with "Use this when..." - State limitations clearly - Include examples in descriptions

4. Save Output Write to .chatgpt-app/golden-prompts.json:

   {
     "toolName": {
       "direct": ["prompt1", "prompt2"],
       "indirect": ["prompt1", "prompt2"],
       "negative": ["prompt1", "prompt2"],
       "edge": ["prompt1", "prompt2"]
     }
   }
   

7. Validate App

Purpose: Validation suite before deployment.

10 Validation Checks

1. Required Files - package.json - tsconfig.server.json - setup.sh (executable) - START.sh (executable) - server/index.ts - .env.example

2. Server Implementation - Uses Server from MCP SDK - Has StreamableHTTPServerTransport - Session management with Map - Correct request handlers

3. Widget Configuration - widgets array exists - Each has id, name, description, templateUri, mockData - URIs match pattern ui://widget/{id}.html

4. Tool Response Format - Returns structuredContent (not just content) - Widget tools have _meta with openai/outputTemplate

5. Resource Handler Format - MIME type: text/html+skybridge - Returns _meta with serialization and CSP

6. Widget HTML Structure - Preview mode support - Event listeners for Apps SDK - Polling fallback - Render guard

7. Endpoint Existence - /health - Health check - /preview - Widget index - /preview/:widgetId - Widget preview - /mcp - MCP endpoint

8. Package.json Scripts - Has build:server - Has start with HTTP_MODE=true - Has dev with watch mode - NO web build scripts (web/, ui/, client/)

9. Annotation Validation - readOnlyHint set correctly - destructiveHint for delete operations - openWorldHint for external APIs

10. Database Validation (if enabled) - Tables have required fields - user_subject indexed - RLS policies enabled

Common Errors

| Error | Fix | |-------|-----| | Missing structuredContent | Add to tool response | | Wrong widget URI | Use ui://widget/{id}.html | | No session management | Add Map | | Missing _meta | Add to tool definition and response | | Wrong MIME type | Use text/html+skybridge |

Critical: Check file existence FIRST before other validations!

8. Test App

Purpose: Run automated tests using MCP Inspector and golden prompts.

4 Test Categories

1. MCP Protocol - Server starts without errors - Handles initialize - Lists tools correctly - Lists resources correctly

2. Schema Validation - Tool schemas are valid Zod - Required fields marked - Types match implementation

3. Widget Tests - All widgets render in preview mode - Mock data loads correctly - No console errors

4. Golden Prompt Tests - Direct prompts trigger correct tools - Indirect prompts work as expected - Negative prompts don't trigger tools

Workflow

1. Start Server in Test Mode

   HTTP_MODE=true NODE_ENV=test npm run dev
   

2. Run MCP Inspector Test protocol compliance: - Initialize connection - List tools - Call each tool with valid inputs - Check responses

3. Schema Validation Verify schemas compile and match implementation.

4. Golden Prompt Tests Use ChatGPT to test prompts: - Record which tool was called - Compare to expected tool - Calculate precision/recall

5. Generate Report

   {
     "passed": 42,
     "failed": 3,
     "categories": {
       "mcp": "✅",
       "schema": "✅",
       "widgets": "✅",
       "prompts": "⚠️ 3 failures"
     },
     "timing": "2.3s"
   }
   

Fixing Failures

For each failure, explain:

9. Deploy App

Purpose: Deploy ChatGPT App to Render with PostgreSQL and health checks.

Prerequisites

Workflow

1. Pre-flight Check - Run validation - Run tests - Check database connection (if enabled)

2. Generate render.yaml

   services:
     - type: web
       name: {app-name}
       runtime: docker
       plan: free
       healthCheckPath: /health
       envVars:
         - key: PORT
           value: 3000
         - key: HTTP_MODE
           value: true
         - key: NODE_ENV
           value: production
         - key: WIDGET_DOMAIN
           generateValue: true
         # Add auth/database vars if needed
   

3. Generate Dockerfile

   FROM node:20-slim
   WORKDIR /app
   COPY package*.json ./
   RUN npm ci --only=production
   COPY dist ./dist
   EXPOSE 3000
   CMD ["node", "dist/server/index.js"]
   

4. Deploy Option A: Automated (if Render MCP available) Use Render MCP agent to deploy. Option B: Manual - Push to GitHub - Connect repo in Render dashboard - Set environment variables - Deploy

5. Verify Deployment - Health check: https://{app}.onrender.com/health - MCP endpoint: https://{app}.onrender.com/mcp - Tool discovery works - Widgets render

6. Configure ChatGPT Connector - URL: https://{app}.onrender.com/mcp - Test in ChatGPT

10. Resume App

Purpose: Resume building an in-progress ChatGPT App.

Workflow

1. Load State Read .chatgpt-app/state.json:

   {
     "appName": "My Task Manager",
     "phase": "Implementation",
     "tools": ["list-tasks", "create-task"],
     "widgets": ["task-list"],
     "auth": false,
     "database": true,
     "validated": false,
     "deployed": false
   }
   

2. Display Progress Show current status: - App name - Current phase - Completed items (tools, widgets) - Pending items (auth, validation, deployment)

3. Offer Next Steps Based on phase: Concept Phase: - "Let's design the tools and widgets" - "Shall we start implementation?" Implementation Phase: - "Add another tool?" - "Add a widget?" - "Set up authentication?" - "Set up database?" Testing Phase: - "Generate golden prompts?" - "Run validation?" - "Run tests?" Deployment Phase: - "Deploy to Render?" - "Configure ChatGPT connector?"

4. Continue Work Based on user's choice, invoke the appropriate workflow section.

Best Practices

1. Always save state after each major step 2. Validate before moving forward (especially before deployment) 3. Use agents for code generation (chatgpt-mcp-generator, chatgpt-auth-generator, etc.) 4. Test at every phase (preview widgets, test tools, run golden prompts) 5. Keep it conversational - guide the user naturally through the workflow 6. Explain trade-offs when offering choices (Auth0 vs Supabase, etc.) 7. Show examples when introducing new concepts

State Management

The .chatgpt-app/state.json file tracks progress:

{
  "appName": "string",
  "description": "string",
  "phase": "Concept" | "Implementation" | "Testing" | "Deployment",
  "tools": ["tool-name"],
  "widgets": ["widget-id"],
  "auth": {
    "enabled": boolean,
    "provider": "auth0" | "supabase" | null
  },
  "database": {
    "enabled": boolean,
    "entities": ["entity-name"]
  },
  "validated": boolean,
  "tested": boolean,
  "deployed": boolean,
  "deploymentUrl": "string | null",
  "goldenPromptsGenerated": boolean,
  "lastUpdated": "ISO timestamp"
}

Command Reference

# Setup
./setup.sh
Development
./START.sh --dev          # Dev mode with watch
./START.sh --preview      # Open preview in browser
./START.sh --stdio        # STDIO mode (testing)
./START.sh                # Production mode
Testing
npm run validate          # Type checking
curl http://localhost:3000/health
Deployment
git push origin main      # Trigger Render deploy

Getting Started

When the user invokes any chatgpt-app command:

1. Check if .chatgpt-app/state.json exists 2. If yes → use Resume App workflow 3. If no → use Create New App workflow

Always guide users through the natural progression: Concept → Implementation → Testing → Deployment

When the user invokes any chatgpt-app command:

1. Check if .chatgpt-app/state.json exists 2. If yes → use Resume App workflow 3. If no → use Create New App workflow

Always guide users through the natural progression: Concept → Implementation → Testing → Deployment

Workflow

1. Pre-flight Check - Run validation - Run tests - Check database connection (if enabled)

2. Generate render.yaml

   services:
     - type: web
       name: {app-name}
       runtime: docker
       plan: free
       healthCheckPath: /health
       envVars:
         - key: PORT
           value: 3000
         - key: HTTP_MODE
           value: true
         - key: NODE_ENV
           value: production
         - key: WIDGET_DOMAIN
           generateValue: true
         # Add auth/database vars if needed
   

3. Generate Dockerfile

   FROM node:20-slim
   WORKDIR /app
   COPY package*.json ./
   RUN npm ci --only=production
   COPY dist ./dist
   EXPOSE 3000
   CMD ["node", "dist/server/index.js"]
   

4. Deploy Option A: Automated (if Render MCP available) Use Render MCP agent to deploy. Option B: Manual - Push to GitHub - Connect repo in Render dashboard - Set environment variables - Deploy

5. Verify Deployment - Health check: https://{app}.onrender.com/health - MCP endpoint: https://{app}.onrender.com/mcp - Tool discovery works - Widgets render

6. Configure ChatGPT Connector - URL: https://{app}.onrender.com/mcp - Test in ChatGPT

1. Always save state after each major step 2. Validate before moving forward (especially before deployment) 3. Use agents for code generation (chatgpt-mcp-generator, chatgpt-auth-generator, etc.) 4. Test at every phase (preview widgets, test tools, run golden prompts) 5. Keep it conversational - guide the user naturally through the workflow 6. Explain trade-offs when offering choices (Auth0 vs Supabase, etc.) 7. Show examples when introducing new concepts