ChatGPT Apps: A New Frontier for AI Developers

Introduction

OpenAI's recent introduction of Apps in ChatGPT marks a significant evolution in how artificial intelligence can be integrated with real-world applications. This new feature allows developers to build fully functional applications—called Apps—that run directly inside ChatGPT conversations, leveraging the power of large language models (LLMs) alongside external data sources and services.

How ChatGPT Apps Work

At its core, ChatGPT Apps use a developer framework called the Apps SDK, built on OpenAI's open standard protocol called Model Context Protocol (MCP). MCP enables ChatGPT to securely connect to external APIs or services by understanding the inputs and outputs of exposed tools or "actions" within these apps. Through this connection, ChatGPT can invoke external functionality dynamically during a conversation, interact with structured data, and render UI elements such as maps, slides, or playlists right inside the chat interface.

A user's request like "Spotify, make a playlist for my party this Friday" signals ChatGPT to invoke the Spotify app directly, which processes the request using its APIs and returns the playlist embedded within the chat. This seamless interaction removes the need for users to leave the conversational flow, providing a unified AI-powered experience.

The Apps SDK: Empowering Developers

The Apps SDK provides a standardized set of tools, protocols, and libraries that developers use to build, deploy, and manage their apps for ChatGPT. Developers create an MCP server: a backend service that declares its available tools (with JSON schemas defining the input parameters and expected output) and optionally supplies lightweight UI components to enhance user interaction.

Key Features of the Apps SDK

Secure API Integration: Through a function-calling pattern that formats requests as structured JSON and safely returns results.
UI Embedding: Inside ChatGPT, which allows apps to present custom interactive components without breaking the conversational context.
OAuth Support: For securely handling user authentication within apps.
Developer Tooling: For local testing and deployment, enabling rapid iteration.

This architecture means developers can build complex, domain-specific applications that extend ChatGPT's native language understanding with real-time data and actions.

A Real-World Example: Building a Rental Search Tool

Imagine developing a "rental listings" app for ChatGPT that allows users to find apartments by neighborhood, bedrooms, max price, and proximity to transit. Using the Apps SDK, a developer would:

Define a tool contract in JSON schema that specifies input parameters — e.g., neighborhood (string), bedrooms (integer), max_price (integer), near_transit (boolean).
Implement a backend MCP server exposing an authenticated API endpoint that queries a real estate database and returns matching listings.
Optionally provide a compact UI component to render results with maps and images inside ChatGPT.

During a chat, when a user asks for apartment options, ChatGPT calls the "search_listings" tool with user parameters. The backend returns structured JSON results, and ChatGPT presents them inline, enabling a fluid, interactive home search experience.

Sample Code: MCP Server in Node.js for Rental Search

Below is a simplified example of how such an MCP server might be implemented using Node.js and Express:

MCP Server Implementation

const express = require('express');
const bodyParser = require('body-parser');

const app = express();
app.use(bodyParser.json());

// Tool definition: JSON schema describing input/output
const toolContract = {
  name: "search_listings",
  description: "Search apartment rentals by criteria",
  parameters: {
    type: "object",
    properties: {
      neighborhood: { type: "string" },
      bedrooms: { type: "integer", minimum: 0 },
      max_price: { type: "integer", minimum: 0 },
      near_transit: { type: "boolean" }
    },
    required: ["neighborhood"]
  }
};

// Simulated database query function
function queryRentals(params) {
  // This would normally query a real database with params filters
  return [
    {
      id: 1,
      title: "Modern 2-bedroom apartment",
      neighborhood: params.neighborhood,
      price: 1500,
      bedrooms: 2,
      near_transit: true,
      url: "https://example.com/listing/1"
    },
    {
      id: 2,
      title: "Cozy studio near downtown",
      neighborhood: params.neighborhood,
      price: 1000,
      bedrooms: 0,
      near_transit: false,
      url: "https://example.com/listing/2"
    }
  ].filter(listing => listing.price <= (params.max_price || 10000));
}

// Endpoint serving tool contract and handling calls
app.get('/.well-known/mcp/server-info', (req, res) => {
  res.json({
    tools: [toolContract]
  });
});

app.post('/execute/search_listings', (req, res) => {
  const inputs = req.body.inputs;
  // Basic validation skipped for brevity
  const results = queryRentals(inputs);
  res.json({ results });
});

const port = 3000;
app.listen(port, () => {
  console.log(`MCP server running on port ${port}`);
});

The Developer Opportunity

This example illustrates how straightforward it can be to extend ChatGPT with domain-specific knowledge and functionality. The Apps SDK lowers the barrier for developers to invent powerful AI-powered tools linked directly to live data and third-party services.

Just as the rise of smartphones created a multibillion-dollar app economy, the ChatGPT Apps platform opens a new, growing market for AI-driven applications. Developers proficient in this SDK can create novel interfaces, automate workflows, and deliver personalized digital experiences embedded within conversational AI.

ChatGPT Apps SDK MCP AI Development Apps API Integration