chatgpt-mcp-apps-kit

Name: chatgpt-mcp-apps-kit
Rating: 75
Author: AndurilCode

by AndurilCode

A TypeScript framework for building interactive MCP applications that work seamlessly with both MCP Apps and ChatGPT (OpenAI Apps SDK) from a single codebase.

⭐ 11🍴 1📅 Jan 24, 2026

ai ai-agents chatgpt-apps expressjs mcp mcp-apps mcp-ui npm

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SKILL.md

ChatGPT Apps Builder

Overview

This skill provides comprehensive guidance for implementing ChatGPT Apps using the OpenAI Apps SDK - a framework that enables MCP servers to deliver rich, interactive UI experiences directly within ChatGPT conversations.

Use this skill when:

Building MCP servers that integrate with ChatGPT's widget runtime
Creating interactive UI components that render inside ChatGPT conversations
Implementing OAuth 2.1 authentication for user-specific data access
Managing widget state and bidirectional communication with MCP servers
Deploying and submitting apps to the ChatGPT platform
Migrating from other approaches to the OpenAI Apps SDK framework

Core Concepts

What are ChatGPT Apps?

ChatGPT Apps are MCP-based applications with three key components:

MCP Server: Defines tools, enforces auth, returns data, and links tools to UI templates
Widget/UI Bundle: Renders inside ChatGPT's iframe using the window.openai runtime
ChatGPT Model: Decides when to call tools and narrates the experience using structured data

User prompt
   ↓
ChatGPT model ──► MCP tool call ──► Your server ──► Tool response
   │                                     │
   │                                     ├─ structuredContent (model reads)
   │                                     ├─ content (narration)
   │                                     └─ _meta (widget-only data)
   │                                        │
   └───── renders narration ◄──── widget iframe ◄──────┘
                              (HTML template + window.openai)

Architecture Components

MCP Server:

Registers UI templates as resources with MIME type text/html+skybridge
Defines tools with _meta["openai/outputTemplate"] pointing to UI resources
Returns three data payloads: structuredContent, content, and _meta
Handles authentication and authorization

Widget Runtime (window.openai):

Provides toolInput, toolOutput, toolResponseMetadata, widgetState
Enables bidirectional communication: callTool, sendFollowUpMessage
File handling: uploadFile, getFileDownloadUrl
Layout controls: requestDisplayMode, requestModal, notifyIntrinsicHeight
Context signals: theme, displayMode, locale, userAgent

Security Model:

Mandatory iframe sandboxing with Content Security Policy
openai/widgetCSP configuration for allowed domains
OAuth 2.1 with PKCE for user authentication
Token verification on every MCP request

Implementation Workflow

Step 1: Set Up Your Project

Project structure:

your-chatgpt-app/
├─ server/
│  └─ src/index.ts          # MCP server + tool handlers
├─ web/
│  ├─ src/component.tsx     # React widget
│  └─ dist/app.{js,css}     # Bundled assets
└─ package.json

Install dependencies:

# MCP SDK
npm install @modelcontextprotocol/sdk zod

# React widget
cd web
npm install react@^18 react-dom@^18
npm install -D vite esbuild typescript

Step 2: Register UI Templates

UI templates are MCP resources with text/html+skybridge MIME type:

import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { readFileSync } from "node:fs";

const server = new McpServer({ name: "my-app", version: "1.0.0" });

// Read bundled widget assets
const JS = readFileSync("web/dist/app.js", "utf8");
const CSS = readFileSync("web/dist/app.css", "utf8");

// Register widget template
server.registerResource(
  "app-widget",
  "ui://widget/app.html",
  {},
  async () => ({
    contents: [
      {
        uri: "ui://widget/app.html",
        mimeType: "text/html+skybridge",
        text: `
<!DOCTYPE html>
<html lang="en">
  <head>
    <meta charset="UTF-8" />
    <title>My App</title>
    <style>${CSS}</style>
  </head>
  <body>
    <div id="root"></div>
    <script type="module">${JS}</script>
  </body>
</html>
        `.trim(),
        _meta: {
          "openai/widgetPrefersBorder": true,
          "openai/widgetDomain": "https://chatgpt.com",
          "openai/widgetCSP": {
            connect_domains: ["https://api.yourservice.com"],
            resource_domains: ["https://*.oaistatic.com"],
            // Optional: redirect_domains, frame_domains
          },
        },
      },
    ],
  })
);

Template metadata:

openai/widgetPrefersBorder: Show visual border around widget
openai/widgetDomain: Dedicated origin for widget (enables fullscreen button)
openai/widgetCSP: Content Security Policy configuration
- connect_domains: APIs the widget can fetch from
- resource_domains: CDNs for static assets (images, fonts, scripts)
- redirect_domains: Hosts for openExternal without safe-link modal
- frame_domains: Allowed iframe origins (discouraged, requires review)

Cache busting: When changing widget HTML/JS/CSS in breaking ways, use new URI or filename so ChatGPT loads fresh bundle.

Step 3: Define Tools

Tools are the contract the ChatGPT model reasons about:

import { z } from "zod";

server.registerTool(
  "search_restaurants",
  {
    title: "Search Restaurants",
    description: "Find restaurants matching criteria",
    inputSchema: {
      type: "object",
      properties: {
        location: { type: "string" },
        cuisine: { type: "string" },
      },
      required: ["location"],
    },
    _meta: {
      "openai/outputTemplate": "ui://widget/app.html",
      "openai/toolInvocation/invoking": "Searching restaurants…",
      "openai/toolInvocation/invoked": "Found restaurants",
      "openai/widgetAccessible": true, // Allow widget to call this tool
      "openai/visibility": "public", // "public" or "private"
    },
  },
  async ({ location, cuisine }) => {
    const restaurants = await searchRestaurants(location, cuisine);
    
    return {
      // Concise JSON for the model to read
      structuredContent: {
        count: restaurants.length,
        topResults: restaurants.slice(0, 3).map(r => ({
          name: r.name,
          rating: r.rating,
        })),
      },
      
      // Optional narration for the model's response
      content: [
        { 
          type: "text", 
          text: `Found ${restaurants.length} restaurants in ${location}` 
        }
      ],
      
      // Large/sensitive data exclusively for the widget
      _meta: {
        allRestaurants: restaurants,
        searchTimestamp: new Date().toISOString(),
      },
    };
  }
);

Tool metadata:

openai/outputTemplate: URI of the widget to render
openai/toolInvocation/invoking: Status message while tool executes
openai/toolInvocation/invoked: Status message when complete
openai/widgetAccessible: Enable window.openai.callTool for this tool
openai/visibility: "public" (model + widget) or "private" (widget only)

Data payloads:

structuredContent: Concise JSON the model reads (keep under 4k tokens)
content: Optional Markdown/plaintext narration
_meta: Widget-exclusive data (never sent to model)

Design for idempotency: Model may retry calls, so handlers should be safe to execute multiple times.

React widget with window.openai:

// web/src/app.tsx
import { createRoot } from "react-dom/client";
import { useEffect, useState, useSyncExternalStore } from "react";

// Helper hook to read window.openai globals
function useOpenAiGlobal<K extends keyof typeof window.openai>(
  key: K
): typeof window.openai[K] {
  return useSyncExternalStore(
    (onChange) => {
      const handler = () => onChange();
      window.addEventListener("openai:set_globals", handler);
      return () => window.removeEventListener("openai:set_globals", handler);
    },
    () => window.openai[key]
  );
}

// Helper hook for widget state persistence
function useWidgetState<T>(defaultValue: T) {
  const savedState = useOpenAiGlobal("widgetState") as T;
  const [state, setState] = useState<T>(savedState ?? defaultValue);
  
  useEffect(() => {
    if (savedState) setState(savedState);
  }, [savedState]);
  
  const setWidgetState = (newState: T | ((prev: T) => T)) => {
    setState((prev) => {
      const updated = typeof newState === "function" 
        ? (newState as (prev: T) => T)(prev)
        : newState;
      window.openai.setWidgetState(updated);
      return updated;
    });
  };
  
  return [state, setWidgetState] as const;
}

function RestaurantList() {
  const toolOutput = useOpenAiGlobal("toolOutput");
  const theme = useOpenAiGlobal("theme");
  const [selectedId, setSelectedId] = useWidgetState<string | null>(null);
  
  const restaurants = toolOutput?._meta?.allRestaurants ?? [];
  
  const handleRefresh = async () => {
    await window.openai.callTool("search_restaurants", {
      location: toolOutput?.location,
    });
  };
  
  const handleFollowUp = async (restaurantName: string) => {
    await window.openai.sendFollowUpMessage({
      prompt: `Tell me more about ${restaurantName}`,
    });
  };
  
  return (
    <div className={`app ${theme}`}>
      <h2>Restaurants</h2>
      <button onClick={handleRefresh}>Refresh</button>
      
      {restaurants.map((r) => (
        <div 
          key={r.id}
          className={selectedId === r.id ? "selected" : ""}
          onClick={() => setSelectedId(r.id)}
        >
          <h3>{r.name}</h3>
          <p>Rating: {r.rating}⭐</p>
          <button onClick={() => handleFollowUp(r.name)}>
            Learn More
          </button>
        </div>
      ))}
    </div>
  );
}

// Mount the app
const root = document.getElementById("root");
if (root) {
  createRoot(root).render(<RestaurantList />);
}

Vanilla JavaScript version:

// web/src/app.ts
const toolOutput = window.openai.toolOutput;
const restaurants = toolOutput?._meta?.allRestaurants ?? [];

const root = document.getElementById("root");
root.innerHTML = `
  <div class="restaurants">
    <h2>Restaurants</h2>
    ${restaurants.map(r => `
      <div class="card">
        <h3>${r.name}</h3>
        <p>Rating: ${r.rating}⭐</p>
      </div>
    `).join("")}
  </div>
`;

// Subscribe to theme changes
window.addEventListener("openai:set_globals", () => {
  document.body.className = window.openai.theme;
});

Step 5: Apply Theming

Use CSS variables that ChatGPT injects:

:root {
  /* Fallback defaults */
  --color-background-primary: light-dark(#ffffff, #171717);
  --color-text-primary: light-dark(#171717, #fafafa);
  --color-border: light-dark(#e5e5e5, #404040);
  --font-sans: system-ui, -apple-system, sans-serif;
  --border-radius-md: 8px;
  --spacing-sm: 8px;
  --spacing-md: 16px;
}

.app {
  background: var(--color-background-primary);
  color: var(--color-text-primary);
  font-family: var(--font-sans);
  padding: var(--spacing-md);
}

.card {
  border: 1px solid var(--color-border);
  border-radius: var(--border-radius-md);
  padding: var(--spacing-md);
  margin-bottom: var(--spacing-sm);
}

Optional: Use the Apps SDK UI kit at apps-sdk-ui for ready-made components.

Use Vite or esbuild to create single-file bundle:

// vite.config.ts
import { defineConfig } from "vite";
import { viteSingleFile } from "vite-plugin-singlefile";

export default defineConfig({
  plugins: [viteSingleFile()],
  build: {
    outDir: "dist",
    rollupOptions: {
      input: "index.html"
    }
  }
});

Build command:

cd web
npm run build  # Outputs dist/app.js and dist/app.css

Step 7: Test Locally

Build widget: npm run build in web/
Start MCP server: node dist/index.js
Use MCP Inspector:
- Point to http://localhost:<port>/mcp
- List resources and verify widget HTML
- Call tools and verify widget renders
- Test window.openai APIs in browser devtools

Step 8: Deploy

Requirements:

HTTPS endpoint (ChatGPT requires HTTPS)
During development: ngrok http <port> or similar tunnel
Production: Deploy to Cloudflare Workers, Fly.io, Vercel, AWS, etc.

Test deployment:

# Tunnel localhost
ngrok http 3000
# Use ngrok URL when creating connector in ChatGPT

Authentication & Authorization

OAuth 2.1 Setup

Protected Resource Metadata (/.well-known/oauth-protected-resource):

{
  "resource": "https://your-mcp.example.com",
  "authorization_servers": [
    "https://auth.yourcompany.com"
  ],
  "scopes_supported": ["read", "write"],
  "resource_documentation": "https://yourcompany.com/docs"
}

Authorization Server Metadata (.well-known/oauth-authorization-server):

{
  "issuer": "https://auth.yourcompany.com",
  "authorization_endpoint": "https://auth.yourcompany.com/oauth2/authorize",
  "token_endpoint": "https://auth.yourcompany.com/oauth2/token",
  "registration_endpoint": "https://auth.yourcompany.com/oauth2/register",
  "code_challenge_methods_supported": ["S256"],
  "scopes_supported": ["read", "write"]
}

Tool Security Schemes:

server.registerTool(
  "get_user_data",
  {
    title: "Get User Data",
    inputSchema: { /* ... */ },
    securitySchemes: [
      { type: "oauth2", scopes: ["read"] }
    ],
    _meta: {
      "openai/outputTemplate": "ui://widget/app.html"
    }
  },
  async ({ input }, context) => {
    // Verify token
    if (!context.authorization) {
      return {
        content: [{ type: "text", text: "Authentication required" }],
        _meta: {
          "mcp/www_authenticate": [
            'Bearer resource_metadata="https://your-mcp.example.com/.well-known/oauth-protected-resource", error="insufficient_scope", error_description="Login required"'
          ]
        },
        isError: true
      };
    }
    
    // Verify token, scopes, audience, expiry
    const user = await verifyToken(context.authorization);
    const data = await fetchUserData(user.id);
    
    return {
      structuredContent: { summary: data.summary },
      _meta: { fullData: data }
    };
  }
);

OAuth Flow:

ChatGPT queries protected resource metadata
ChatGPT dynamically registers client with authorization server
User authenticates and grants scopes
ChatGPT exchanges authorization code for access token (with PKCE)
Token attached to MCP requests: Authorization: Bearer <token>
MCP server verifies token on every request

Redirect URIs to allowlist:

Production: https://chatgpt.com/connector_platform_oauth_redirect
Review: https://platform.openai.com/apps-manage/oauth

Advanced Features

Component-Initiated Tool Calls

Enable widgets to call tools directly:

// Tool definition
_meta: {
  "openai/outputTemplate": "ui://widget/app.html",
  "openai/widgetAccessible": true,
  "openai/visibility": "public" // or "private" to hide from model
}

// Widget code
async function refreshData() {
  const result = await window.openai.callTool("search_restaurants", {
    location: "Paris"
  });
  // Widget automatically re-renders with new toolOutput
}

File Handling

Upload files:

// Widget code
async function handleUpload(event: ChangeEvent<HTMLInputElement>) {
  const file = event.target.files?.[0];
  if (!file) return;
  
  const { fileId } = await window.openai.uploadFile(file);
  console.log("Uploaded:", fileId);
}

Download files:

// Widget code
const { downloadUrl } = await window.openai.getFileDownloadUrl({ fileId });
image.src = downloadUrl;

Tool file parameters:

server.registerTool(
  "process_image",
  {
    title: "Process Image",
    inputSchema: {
      type: "object",
      properties: {
        image: {
          type: "object",
          properties: {
            download_url: { type: "string" },
            file_id: { type: "string" }
          }
        }
      }
    },
    _meta: {
      "openai/outputTemplate": "ui://widget/app.html",
      "openai/fileParams": ["image"]
    }
  },
  async ({ image }) => {
    const response = await fetch(image.download_url);
    const buffer = await response.arrayBuffer();
    // Process image...
    return { content: [], structuredContent: {} };
  }
);

Display Modes

Request alternate layouts:

// Widget code
await window.openai.requestDisplayMode({ mode: "fullscreen" });
// Options: "inline", "pip", "fullscreen"
// Note: PiP may coerce to fullscreen on mobile

Modals

Spawn host-controlled overlays:

// Widget code
await window.openai.requestModal({
  title: "Checkout",
  component: "checkout-modal"
});

Use standard routing (React Router):

import { BrowserRouter, Routes, Route, useNavigate } from "react-router-dom";

function App() {
  return (
    <BrowserRouter>
      <Routes>
        <Route path="/" element={<ListView />} />
        <Route path="/detail/:id" element={<DetailView />} />
      </Routes>
    </BrowserRouter>
  );
}

function ListView() {
  const navigate = useNavigate();
  return (
    <button onClick={() => navigate("/detail/123")}>
      View Details
    </button>
  );
}

ChatGPT mirrors iframe history to UI navigation controls.

Localization

Read locale and format accordingly:

// Widget code
const locale = window.openai.locale ?? "en-US";
const formatter = new Intl.DateTimeFormat(locale);
const price = new Intl.NumberFormat(locale, { 
  style: "currency", 
  currency: "USD" 
});

Or use i18n libraries:

import { IntlProvider } from "react-intl";
import en from "./locales/en-US.json";
import es from "./locales/es-ES.json";

const messages = { "en-US": en, "es-ES": es };
const locale = window.openai.locale ?? "en-US";

<IntlProvider locale={locale} messages={messages[locale]}>
  <App />
</IntlProvider>

From widget:

window.openai.requestClose();

From server:

return {
  content: [],
  structuredContent: {},
  _meta: {
    "openai/closeWidget": true
  }
};

Security & Privacy

Principles

Least privilege: Only request needed scopes and permissions
Explicit consent: Users understand what they're granting
Defense in depth: Assume prompt injection and malicious inputs

Data Handling

structuredContent: Include only data needed for current prompt
Storage: Publish retention policy, honor deletion requests
Logging: Redact PII, store correlation IDs only

Network Security

Widgets run in sandboxed iframe with strict CSP
No privileged browser APIs (alert, prompt, clipboard)
fetch allowed only with CSP compliance
Subframes blocked by default (require frame_domains)

Authentication

Use OAuth 2.1 with PKCE and dynamic client registration
Verify token signature, issuer, audience, expiry on every request
Reject expired/malformed tokens with 401 and WWW-Authenticate

Prompt Injection Mitigation

Review tool descriptions regularly
Validate all inputs server-side
Require human confirmation for destructive operations
Test with injection prompts during QA

Testing & Deployment

Local Testing

MCP Inspector: Test tools and widget rendering
- http://localhost:<port>/mcp
- Verify resources list
- Call tools and inspect widget iframe
- Use browser devtools for window.openai debugging
Dogfooding: Test with trusted users before broad rollout
OAuth Testing: Use Inspector's Auth settings to walk through flow

Production Deployment

Hosting options:

Cloudflare Workers
Fly.io
Vercel
AWS Lambda/ECS
Your existing infrastructure

Requirements:

HTTPS endpoint
Low latency (< 500ms tool responses recommended)
OAuth protected resource metadata at /.well-known/oauth-protected-resource
Authorization server with discovery metadata

Monitoring:

Set up alerts for failed auth attempts
Track tool execution times
Monitor CSP violations in widget
Log correlation IDs for debugging

Submission

Pre-submission checklist:

OAuth redirect URIs allowlisted:
- https://chatgpt.com/connector_platform_oauth_redirect
- https://platform.openai.com/apps-manage/oauth
Tool descriptions clear and accurate
Security review complete
CSP correctly configured
Widget tested across light/dark themes
Mobile/desktop responsive design
Localization for supported languages
Error messages user-friendly
Performance under load verified
Documentation complete

Review process:

Apps using frame_domains subject to stricter review
Expect questions about data handling and security
May be rejected for directory if concerns remain

Troubleshooting

Symptom: White screen or no widget appears

Solutions:

Verify mimeType: "text/html+skybridge"
Check bundled JS/CSS URLs resolve correctly
Inspect browser console for CSP violations
Confirm openai/widgetCSP allows necessary domains

`window.openai` Undefined

Symptom: Cannot read property 'toolOutput' of undefined

Solutions:

Only available in text/html+skybridge templates
Check MIME type spelling and format
Verify widget loaded without errors
Use browser devtools to inspect iframe

CSP Violations

Symptom: Network requests blocked, inline styles stripped

Solutions:

Add domains to connect_domains (APIs)
Add domains to resource_domains (static assets)
Avoid inline scripts/styles (bundle them)
Check browser console for specific CSP errors

Symptom: Old widget version keeps loading after deploy

Solutions:

Change template URI: ui://widget/app-v2.html
Change bundled filename: app-20250101.js
Clear ChatGPT cache (hard refresh)
Wait ~5 minutes for CDN propagation

Authentication Loops

Symptom: OAuth flow starts but never completes

Solutions:

Verify redirect URI in allowlist
Check code_challenge_methods_supported includes S256
Confirm resource parameter echoed in tokens
Use MCP Inspector Auth tab to debug flow step-by-step
Check authorization server logs

Large Payloads

Symptom: Slow rendering, model performance degraded

Solutions:

Trim structuredContent to essentials (< 4k tokens)
Move large data to _meta (widget-only)
Paginate or lazy-load data in widget
Compress images before embedding

Examples

Official examples repository: openai-apps-sdk-examples

Pizzaz List

Ranked card list with favorites and CTAs

Tool: Search pizzerias by location
Widget: Sortable list with star ratings
Features: Favorites, external links, follow-up prompts

Pizzaz Carousel

Embla-powered horizontal scroller for media-heavy layouts

Tool: Fetch restaurant galleries
Widget: Image carousel with lazy loading
Features: Swipe navigation, fullscreen viewer

Pizzaz Map

Mapbox integration with fullscreen inspector

Tool: Search restaurants near location
Widget: Interactive map with markers
Features: PiP mode, geolocation, clustering

Pizzaz Album

Stacked gallery for deep dives on single place

Tool: Get restaurant details with photos
Widget: Pinterest-style photo grid
Features: Modal lightbox, share actions

Pizzaz Video

Scripted player with overlays

Tool: Fetch restaurant promotional videos
Widget: Custom video player
Features: Overlay controls, transcript display

Best Practices

Server Design

Idempotent handlers: Model may retry, design for safety
Structured content first: Model reads this, keep it concise
Metadata for widgets: Move large/sensitive data to _meta
Clear tool descriptions: Model chooses tools based on these
Security in depth: Verify tokens, validate inputs, log actions

Theme-aware styling: Use CSS variables, test light/dark
Responsive layouts: Mobile and desktop support
Loading states: Handle async operations gracefully
Error boundaries: Catch and display errors user-friendly
Accessibility: Keyboard navigation, ARIA labels, focus management

State Management

Persist with setWidgetState: Keep state under 4k tokens
Scope to widget instance: State tied to specific message
Clear on new conversations: Fresh widgetId = empty state
Sync with useOpenAiGlobal: Multiple components stay consistent
Validate rehydrated state: Handle corrupted or old state

Performance

Bundle size: Keep under 500KB for fast load
Code splitting: Lazy load features not needed immediately
Image optimization: Compress, resize, use modern formats
API caching: Cache responses when appropriate
Debounce inputs: Avoid excessive tool calls from rapid interactions

Security

Validate inputs: Never trust data from model or user
Redact secrets: No tokens/keys in structuredContent or _meta
CSP allowlist: Only necessary domains, avoid wildcards
Rate limiting: Protect backend from abuse
Audit logs: Track actions for compliance and debugging

Quick Reference

`window.openai` API

Category	Property/Method	Description
Data	`toolInput`	Tool arguments from invocation
Data	`toolOutput`	`structuredContent` from server
Data	`toolResponseMetadata`	`_meta` payload (widget-only)
Data	`widgetState`	Persisted UI state snapshot
Data	`setWidgetState(state)`	Store new state (< 4k tokens)
Tools	`callTool(name, args)`	Invoke MCP tool from widget
Messaging	`sendFollowUpMessage({ prompt })`	Post user-authored message
Files	`uploadFile(file)`	Upload file, receive `fileId`
Files	`getFileDownloadUrl({ fileId })`	Get temp download URL
Layout	`requestDisplayMode({ mode })`	Request inline/PiP/fullscreen
Layout	`requestModal({ ... })`	Spawn host modal overlay
Layout	`notifyIntrinsicHeight(height)`	Report dynamic height
Layout	`requestClose()`	Close the widget
Navigation	`openExternal({ href })`	Open vetted external link
Context	`theme`	"light" or "dark"
Context	`displayMode`	"inline", "pip", or "fullscreen"
Context	`locale`	RFC 4647 locale string
Context	`userAgent`	Client user agent
Context	`maxHeight`	Container max height
Context	`safeArea`	Safe area insets
Context	`view`	View context info

Tool Metadata Reference

Key	Value	Description
`openai/outputTemplate`	`"ui://widget/app.html"`	Widget URI to render
`openai/widgetAccessible`	`true/false`	Enable `callTool` from widget
`openai/visibility`	`"public"/"private"`	Model visibility
`openai/toolInvocation/invoking`	`"Loading…"`	Status while executing
`openai/toolInvocation/invoked`	`"Complete"`	Status when done
`openai/fileParams`	`["image"]`	Fields treated as file params

CSP Configuration

"openai/widgetCSP": {
  connect_domains: ["https://api.example.com"],    // Fetch destinations
  resource_domains: ["https://cdn.example.com"],   // Static assets
  redirect_domains: ["https://checkout.example.com"], // openExternal
  frame_domains: ["https://*.embed.example.com"]   // Subframes (discouraged)
}

OAuth Endpoints

Endpoint	Purpose
`/.well-known/oauth-protected-resource`	Protected resource metadata
`/.well-known/oauth-authorization-server`	OAuth 2.0 discovery
`/.well-known/openid-configuration`	OpenID Connect discovery

Resources

Official Documentation:

Examples & Tools:

Community:

Authentication:

mcp-builder: General MCP server implementation patterns
mcp-mcp-apps-kit: MCP Apps (SEP-1865) for Claude Desktop

Score

Total Score

75/100

Based on repository quality metrics

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SKILL.mdファイルが含まれている

+20

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ライセンスが設定されている

+10

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+10

○人気

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chatgpt-mcp-apps-kit

SKILL.md

name: chatgpt-mcp-apps-kit description: Guide for implementing ChatGPT Apps using OpenAI Apps SDK. Use when building MCP servers with interactive UI components that integrate with ChatGPT, including widget runtime, authentication, state management, and deployment to the ChatGPT platform.

ChatGPT Apps Builder

Overview

Core Concepts

What are ChatGPT Apps?

Key Pattern: Tool → Data → Widget

Architecture Components

Implementation Workflow

Step 1: Set Up Your Project

Step 2: Register UI Templates

Step 3: Define Tools

Step 4: Build the Widget

Step 5: Apply Theming

Step 6: Bundle the Widget

Step 7: Test Locally

Step 8: Deploy

Authentication & Authorization

OAuth 2.1 Setup

Advanced Features

Component-Initiated Tool Calls

File Handling

Display Modes

Modals

Navigation

Localization

Close Widget

Security & Privacy

Principles

Data Handling

Network Security

Authentication

Prompt Injection Mitigation

Testing & Deployment

Local Testing

Production Deployment

Submission

Troubleshooting

Widget Not Rendering

window.openai Undefined

CSP Violations

Stale Widget Cache

Authentication Loops

Large Payloads

Examples

Pizzaz List

Pizzaz Carousel

Pizzaz Map

Pizzaz Album

Pizzaz Video

Best Practices

Server Design

Widget Design

State Management

Performance

Security

Quick Reference

window.openai API

Tool Metadata Reference

CSP Configuration

OAuth Endpoints

Resources

Related Skills

Score

Reviews

Related

Related Skills

create-pr

orpc-contract-first

component-refactoring

web-design-guidelines

frontend-code-review

frontend-testing

`window.openai` Undefined

`window.openai` API