Mahmoud Zalt

Posted on Oct 29

A Strategic Guide to Building ChatGPT Apps with the OpenAI Apps SDK

#chatgpt #ai #programming #news

🚀 Why This Matters

The OpenAI Apps SDK and Model Context Protocol (MCP) are transforming software distribution.
Instead of users opening websites or mobile apps, your logic now executes inside ChatGPT — inline, at the exact moment of need.

This post shows you how to build a Crypto Tracker App that fetches live cryptocurrency data and displays it directly in the chat using OpenAI’s new conversational app model.

You’ll learn how to:

Define an MCP tool schema
Build an MCP server in TypeScript
Return structured data + inline UI
Deploy securely and measure performance

🧠 How It Works

When a user says:

"Check Bitcoin price and Ethereum trend"

ChatGPT:

Detects intent (crypto data retrieval)
Calls your Crypto Tracker tool
Your MCP server fetches live data (via API)
Returns structured content + inline card UI
ChatGPT renders it directly in the thread

No websites. No context switches.

🧩 Step 1: Define Your Tool Schema

Every MCP app exposes tools with clear contracts using JSON Schema.

{
  "name": "crypto_tracker",
  "description": "Fetch live cryptocurrency prices and trends",
  "input_schema": {
    "type": "object",
    "properties": {
      "symbol": {
        "type": "string",
        "description": "Crypto symbol like BTC, ETH, SOL"
      }
    },
    "required": ["symbol"]
  }
}

This definition tells the model exactly what the tool does and what input it expects.

⚙️ Step 2: Build the MCP Server (TypeScript)

Let’s build the Crypto Tracker using the official SDK.

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

const API_URL = "https://api.coingecko.com/api/v3/simple/price";
const server = new McpServer({ name: "crypto-tracker", version: "1.0.0" });

server.registerTool(
  "get-crypto-price",
  {
    title: "Get Crypto Price",
    inputSchema: { symbol: z.string() },
    _meta: {
      "openai/outputTemplate": "https://api.yourapp.com/templates/crypto-card",
      "openai/toolInvocation/invoking": "Fetching price...",
      "openai/toolInvocation/invoked": "Price fetched"
    }
  },
  async ({ symbol }) => {
    try {
      const response = await fetch(`${API_URL}?ids=${symbol.toLowerCase()}&vs_currencies=usd`);
      const data = await response.json();
      const price = data[symbol.toLowerCase()]?.usd ?? null;

      if (!price) throw new Error("Invalid symbol or data not available.");

      return {
        structuredContent: { symbol, price, currency: "USD" },
        content: [
          { type: "text", text: `${symbol.toUpperCase()} is trading at $${price} USD.` }
        ],
        _meta: {
          updatedAt: new Date().toISOString(),
          source: "CoinGecko"
        }
      };
    } catch (error) {
      return {
        content: [{ type: "text", text: `Error fetching data: ${error.message}` }],
        structuredContent: {},
        _meta: { status: "error" }
      };
    }
  }
);

server.listen(8080);

✅ Runs locally with:

node index.js

💡 Step 3: Add an Inline UI Component

The _meta["openai/outputTemplate"] points to a hosted HTML component rendered directly in ChatGPT.

<div class="crypto-card">
  <h3>BTC/USD</h3>
  <p>Price: $64,213.00</p>
  <small>Last updated: just now</small>
</div>

Serve this component with MIME type text/html+skybridge.

🧱 Step 4: Local Testing

Start your MCP server:

node index.js

Run MCP Inspector:

npx @modelcontextprotocol/inspector http://localhost:8080/mcp

Trigger the tool manually using test data:

curl -X POST http://localhost:8080/mcp/call_tool -d '{"name": "get-crypto-price", "arguments": {"symbol": "btc"}}'

🔐 Step 5: Secure Deployment

Recommended platforms:

Fly.io / Render / Railway for fast HTTPS containers
Cloud Run for scale-to-zero deployments

Security best practices:

Enforce HTTPS
Add strict CSP (connect_domains, resource_domains)
Use OAuth 2.1 for authentication
Log latency, invocation rate, and response errors

Example .env file:

API_URL=https://api.coingecko.com/api/v3/simple/price
NODE_ENV=production
PORT=8080

📊 Observability & Ranking

Once your app is live, ChatGPT learns from performance:

Metric	Meaning
Invocation Rate	How often ChatGPT selects your tool
Latency (p95)	Response speed influences ranking
Resolution Rate	% of successful, complete answers
User Trust	Repeat invocations per session

Fast, deterministic responses earn higher model trust.

💸 Monetization Ideas

SaaS Entitlement: Authenticate users and show premium data tiers.
Affiliate Flow: Redirect to exchanges or crypto products with tracking.
Lead Capture: Offer reports or consultations post-analysis.
Brand Utility: Build a free, reliable tool to earn recurring invocation.

The goal isn’t traffic. It’s trust. Every resolved request earns another invocation.

🧭 Final Thoughts

Apps inside ChatGPT aren’t just a trend — they’re the next distribution layer of software.

The OpenAI Apps SDK and Model Context Protocol (MCP) make your product callable the moment user intent appears.

Instead of chasing traffic, you’re building trust — one resolved conversation at a time.

Build small. Resolve fast. Iterate relentlessly. Every successful invocation is a signal that the model should call you again.

The Crypto Tracker App is just one use case. Whether you’re in finance, education, e-commerce, or automation, the same playbook applies:

structure your capabilities clearly, deliver instant results, and design for in-flow experiences that feel effortless.

👉 Continue Reading: The Full Strategic Playbook

Want to go beyond the code?

Dive into the complete guide for:

Discovery and ranking mechanics inside ChatGPT 🔍
Design rules for conversational UX that converts 💬
Deeper MCP implementation examples ⚙️
KPIs for measuring intent-driven success 📊
Proven monetization frameworks 💡
The first industries this shift will transform 🏁

➡️ Read the full article @ zalt.me/blog/chatgpt-apps-playbook

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