Understanding The ChatGPT Apps SDK: MCP, Components, And Your First Live App

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You can feel the shift the moment a card slides into your chat, not a link, not a wall of text, but a working interface that you can poke, drag, and talk to. That shift has a name: ChatGPT Apps SDK. It turns conversations into launchpads for real software. If you are a builder, this is the fastest route from a sketch to a shippable app that lives where users already spend their attention.

What follows is a practical guide from an engineer’s point of view. We’ll clear the fog around what this thing is, how it differs from plugins, and how to build ChatGPT apps that feel native to conversation. You’ll get the mental model, a step-by-step ChatGPT Apps SDK tutorial, and a complete walkthrough of the on-stage camera-and-lights demo that controlled real hardware in front of a live audience. Bring your curiosity. Leave with a working plan.

1. Plugins Déjà Vu, Why This Is A Leap Forward

If your first reaction is, “Didn’t we already have plugins,” you’re not alone. Plugins did useful work, yet they were mostly text in and text out. The ChatGPT Apps SDK gives you two big upgrades.

• A real UI, inside the chat. Your app renders an interface built with ordinary web tech, so users can scan a Zillow map, scrub a Coursera video, edit a Canva slide, or confirm an action, all without leaving the thread. That tight loop matters. It reduces context hopping and shortens the distance from intent to result.
• An open foundation. ChatGPT Apps SDK is built on the Model Context Protocol. MCP is an open standard for connecting the assistant to external data and tools. Your app speaks MCP, your backend remains yours, and the client knows how to hydrate your UI with structured output. That split of concerns, standard over special case, is what makes this more than a rename.

The punchline: this isn’t a wrapper on top of old APIs. The ChatGPT Apps SDK treats conversation as the operating system, your UI as a first-class view, and MCP as the bus that moves structured intent and data between them.

2. Architecture Of A ChatGPT Apps SDK, The Brain And The Face

The architecture is refreshingly simple once you see it in layers.

2.1 The Brain, Your MCP Server

Think of the brain as your MCP server. It is a normal web service that implements MCP, exposes named tools with JSON-schema inputs, and returns structured results. It enforces auth, talks to your databases and third-party APIs, and decides what comes back. You can write it in Python or TypeScript, ship it on any modern platform, and evolve it like any backend.

Each tool returns three useful payloads:

• structuredContent: the data your UI needs to render.
• content: optional text the model can read and narrate.
• _meta: private values for the UI that the model should not see.

That third channel is where you pass large maps or internal identifiers that would only distract the model.

2.2 The Face, Your Custom Component

The face is your UI. It is an HTML template, CSS, and JavaScript, hydrated by the structuredContent from tool results. ChatGPT renders it inline, in a carousel, fullscreen, or picture-in-picture. You keep the brand accents where the system allows. ChatGPT keeps the chrome, composer, and accessibility.

This split makes testing clean. You can validate your MCP responses with the inspector, see the component render in isolation, then plug it into chat and watch the assistant narrate what the user is seeing.

The flow that ties brain and face together is exactly what the ChatGPT Apps SDK optimizes.

3. Build Your First App, A ChatGPT Apps SDK Tutorial

Time to move from talk to keyboard. The following steps get you from zero to a live card in your chat. The intent is to satisfy the real search you likely typed this morning, how to build ChatGPT apps.

3.1 Prerequisites

• A Plus or Pro account with ChatGPT developer mode enabled.
• Node.js or Python installed.
• An ngrok tunnel or equivalent for HTTPS during local testing.
• Basic familiarity with JSON schema and REST. You do not need a front-end framework, but you can use one.

3.2 Scaffold The MCP Server

Create a minimal MCP server that exposes a single tool, then return both text and structuredContent. In TypeScript, that looks like:

import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { z } from "zod";

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

server.registerTool(
  "hello_world",
  {
    title: "Hello World",
    inputSchema: { name: z.string().default("developer") },
    _meta: { "openai/outputTemplate": "ui://widget/hello.html" }
  },
  async ({ name }) => {
    return {
      content: [{ type: "text", text: `Hi, ${name}. Here is your card.` }],
      structuredContent: { greeting: `Welcome, ${name}!` },
      _meta: { debugId: crypto.randomUUID() }
    };
  }
);

server.listen(2091);

This defines a tool the model can call, attaches a template ID for the UI, and returns structured data the component will render.

3.3 Create The Component UI

Expose a resource for the UI template and a small script to read the injected payload:

const HTML = `
<div id="root" style="padding:12px;font:14px system-ui">
  <h3>Starter Card</h3>
  <p id="g"></p>
</div>
<script>
  const data = window.openai?.toolOutput || {};
  document.getElementById('g').textContent = data.greeting || 'Ready.';
</script>
`.trim();

server.registerResource("hello-widget", "ui://widget/hello.html", {}, async () => ({
  contents: [{ uri: "ui://widget/hello.html", mimeType: "text/html", text: HTML }]
}));

Now the tool and the UI know about each other. The tool sets openai/outputTemplate to point to your template. The template reads window.openai.toolOutput to hydrate the view.

3.4 Run Locally And Expose With Ngrok

Start the server, then run an HTTPS tunnel:

node server.js
ngrok http 2091
# Forwarding: https://<subdomain>.ngrok.app -> http://127.0.0.1:2091

You will use the public URL to connect from ChatGPT.

3.5 Connect In ChatGPT Developer Mode

Open settings, switch to ChatGPT developer mode, add a new app, and paste the ngrok URL to your MCP endpoint. ChatGPT fetches your MCP manifest, discovers tools, and registers your component template. The ChatGPT Apps SDK takes care of the handshakes so you can focus on behavior.

3.6 Test And Iterate

In a new chat, call your app by name or through a suggestion. You should see your card render inline, followed by a short model response that adds context or suggests a next step. If your card needs depth, open fullscreen. If your experience should persist during scrolling, pin it in picture-in-picture.

From here, you can add more tools, return richer structuredContent, and let the assistant do the narration. This is the rhythm you’ll repeat in production.

4. Deconstructing The Live Demo, From A Sketch To A Smart Camera

A live demo either delights a room or exposes weak abstractions. The camera-and-lights demo did the former because it chained small, composable parts the way you would on a real team. Let’s rebuild it with explicit prompts and a clear flow so you can repeat it.

Goal

Control a Sony FR7 camera and stage lights from a chat, switching between a control panel, voice, and direct hardware commands, all while keeping the UI inside ChatGPT.

Plan

• Generate a control UI from a sketch.
• Build a MCP server that exposes tools like move_camera, set_zoom, and recall_preset.
• Add a second MCP service for lights with tools like set_scene and spotlight.
• Use the Realtime voice API so the assistant can translate natural language into tool calls.
• Keep everything discoverable and testable through the ChatGPT Apps SDK surfaces.

Prompts And Workflow

4.1 Create the control panel UI

Prompt, coding agent:

“Create a responsive web control panel with a live camera preview, a joystick for pan and tilt, buttons for zoom in and out, and preset recall. Use accessible HTML and minimal JS. Export as an HTML template I can register as ui://widget/camera.html.”

4.2 Implement camera control tools

Prompt, coding agent:

“Using VISCA over IP for a Sony FR7, write a Node module that exposes move_camera(pan: number, tilt: number), set_zoom(level: number), and recall_preset(id: number). Return structured results with a status string and current position. Package it as MCP tools.”

Minimal tool shape:

server.registerTool(
  "move_camera",
  {
    title: "Move Camera",
    inputSchema: { pan: z.number().min(-1).max(1), tilt: z.number().min(-1).max(1) },
    _meta: { "openai/outputTemplate": "ui://widget/camera.html" }
  },
  async ({ pan, tilt }) => {
    await visca.panTilt(pan, tilt);
    return {
      structuredContent: { position: await visca.getPosition() },
      content: [{ type: "text", text: "Camera moved." }]
    };
  }
);

4.3 Wire the joystick to the tool

In the template’s script, translate pointer movement into small calls to move_camera. Keep the payloads small and throttle updates. The component hydrates from structuredContent.position so the overlay shows actual pan and tilt.

4.4 Add lighting tools

Prompt, coding agent:

“Add a second MCP service named lights-server with tools set_scene(name: string) for scene presets, and spotlight(on: boolean, target?: string) for audience effects. Return structuredContent with the current scene and any active spot.”

Example tool:

server.registerTool(
  "set_scene",
  {
    title: "Set Scene",
    inputSchema: { name: z.enum(["keynote", "demo", "celebrate", "normal"]) }
  },
  async ({ name }) => {
    await lights.applyScene(name);
    return { structuredContent: { scene: name }, content: [{ type: "text", text: `Scene set to ${name}.` }] };
  }
);

4.5 Enable voice control

Prompt, coding agent:

“Integrate the Realtime voice API so verbal requests trigger tool calls. Map ‘pan left a bit’ to small negative pan deltas, ‘zoom in’ to incremental zoom, ‘lights to celebrate’ to set_scene(“celebrate”), and ‘spotlight on the audience’ to spotlight(true, “audience”).”

4.5 Install guardrails and context bridges

Add PII filtering and short status strings so the UI shows “Moving camera” while the tool runs, then “Camera moved.” If your UI exposes what item the user is viewing, report that back to the model with a small context object. This makes “what am I looking at” questions trivial.

Representative Voice Turns

• “What do you see on camera”

The model calls a describe_frame tool or uses vision, then narrates.

• “Shine the lights toward the audience”

The model selects spotlight(true, “audience”). The UI reflects structuredContent.scene = “demo” and spotlight = “audience”.

• “Say hello to everyone watching the livestream”

A text-to-speech step plays a short greeting while the PiP window remains pinned.

• “Back to normal”

One set_scene(“normal”) call. The card shows the new state.

The demo feels magical because each step is small and legible. The ChatGPT Apps SDK stitches them together in one place, so you never leave the conversation to juggle dashboards.

5. From Preview To Production, Distribution And Monetization

You can prototype in an afternoon. The question is how to reach users and keep quality high once you do.

5.1 App Directory And Discovery in ChatGPT Apps SDK

Apps appear when users call them by name or when the assistant suggests them from context. As the directory opens, your listing, descriptions, and tool metadata will drive recall at the right moments. Treat that metadata like a search product. Define crisp verbs. Document parameters. Tie results to real outcomes. Done well, the ChatGPT Apps SDK puts you in front of people who are already asking to do the thing your app does.

5.2 Privacy, Safety, And Guardrails

Ship with a real privacy policy. Request the minimum scopes you need. Explain why. In your server, compartmentalize user data by account, encrypt secrets, and log tool calls with IDs, not raw payloads. Add PII masking early. The Model Context Protocol lets you keep sensitive details out of the model’s view by placing them in _meta. Use that. It reduces drift and limits exposure.

5.3 Observability, Versioning, And Rollouts

• Observability. Record tool latency, error rates, and the size of structured payloads. If a card feels heavy, it is. Trim it.
• Versioning. Cache bust your component templates. If you break a contract, register a new resource URI.
• Rollouts. Start in ChatGPT developer mode with a small cohort, capture golden prompts, and watch discovery precision. If the assistant suggests your app at the wrong time, adjust verbs and descriptions before you blame the model.

The business side will arrive. Discovery improves with usage. Payments will follow. When it does, your clean architecture means less refactoring later and more time to ship.

6. Patterns That Work, And Pitfalls To Avoid

Patterns That Work

1. One clear job per card. The card should answer a single question or complete a single action. Everything else belongs in follow-ups.
2. Small, meaningful payloads. Keep structuredContent tight. Summaries feed the model better than raw tables.
3. Status strings that match reality. Set short invoking and invoked messages in _meta so users feel the app thinking, not hanging.
4. Consistent verbs. If your tool is “create_invoice,” keep it that way. Verb drift confuses both users and the model.
5. Explicit outcomes. Return fields the model can reason about: IDs, timestamps, and status enums. The assistant writes better copy when it has crisp state.

Pitfalls To Avoid

• Treating the UI like an iframe of your site. Inline apps are not landing pages. Remove navigation chrome. Reduce options.
• Overloading the card. No nested scroll regions. No dense tables. If you need depth, open fullscreen.
• Hidden state. If your UI selection matters, publish it to the assistant through context. Then “filter these to three bedrooms with a yard” works exactly once, right away.
• Over-prompting. If you keep adding words to coerce the model, your tool contract is wrong. Fix the schema. Tighten enums. Keep the prompt short and let structure do the heavy lifting.

These habits compound. Teams that follow them ship faster, receive cleaner feedback, and see their apps surface at the right time because the assistant can trust their outputs.

7. Why This Matters For Developers

The reason this model clicks is that it matches how we already work. You sketch a thing, build a thin server that does one job well, compose small tools, and layer a UI that respects context. The ChatGPT Apps SDK keeps that rhythm intact and makes distribution part of the platform, not your side hustle.

It also lowers the perceived bar to “real” apps. Your first version can be a single card that calls one tool and returns a small, correct structure. That is enough to be useful. That is enough to learn. When you discover the sharp edges, you fix the server, not the universe.

The deeper value is philosophical. Conversation is a flexible interface. People ask for outcomes, not endpoints. By grounding those asks in the Model Context Protocol, you meet users where they already think, then return a state the model can explain, refine, and act on. The result feels like software that listens.

The OpenAI Apps SDK documentation and the Model Context Protocol give you the contracts. The rest is your taste and your sense of what matters.

8. Closing Thoughts, And A Challenge

You saw the path. A control panel from a sketch. A MCP server with a handful of tools. A lights service with scene presets. Voice that understands, “Zoom in a touch,” and turns it into a tiny delta that runs right now. All of it, in the same thread where the plan began.

If you’ve been waiting for the moment to jump in, this is it. Start with a single job your users do every day. Build a tool that performs it cleanly. Wrap it in a card that makes the decision obvious. Ship in ChatGPT developer mode, get feedback, tighten the loop, and keep going. The ChatGPT Apps SDK gives you the rails. The OpenAI Apps SDK documentation and the Model Context Protocol give you the contracts. The rest is your taste and your sense of what matters.

Here is the challenge. Recreate the camera-and-lights demo for your own world in a week. Swap the Sony for your internal dashboard, replace lights with a billing scene switch, and wire voice to the three actions everyone asks your tool to do. When you feel that first click in chat, when the card updates and the assistant explains what just happened, you’ll understand why this is not another cycle of hype. It is the fastest way to put working software in front of the next hundred users who ask for it.

Build something small today. Share it with one teammate tomorrow. Publish when it stops surprising you. The best apps will be the ones that respect the user’s time, tell the truth, and make progress obvious. The ChatGPT Apps SDK is ready. So are your users.