OpenAI Apps SDK vs MCP: What to Build (and Why)

The comparison people search for is “OpenAI Apps SDK vs MCP”, but the two aren’t on the same axis: OpenAI’s Apps SDK is built on MCP. Announced at DevDay in October 2025, it lets developers build interactive apps that run inside ChatGPT conversations, and it *extends* MCP so a developer designs both the app’s logic (MCP tools) and its interface (UI components ChatGPT renders).

So the real question isn’t “which protocol” — it’s what to build on top of MCP: a plain MCP server (tools and data, no bespoke UI, works in any MCP client) or a ChatGPT app (an MCP server plus a rich in-ChatGPT interface). This guide is about that choice.

If you haven’t connected a server to ChatGPT at all yet, start with the setup mechanics in ChatGPT MCP; this guide picks up where that leaves off, at the build-vs-build decision.

A plain MCP server exposes self-describing tools (and resources and prompts) over the protocol. Any MCP client can discover and call them; results come back as text or structured data that the client renders however it renders tool output. It’s the lowest common denominator — and that’s a feature, because the denominator is now every major assistant.

A ChatGPT app (Apps SDK) is that same MCP tool layer with a presentation layer bolted on: interactive components — forms, maps, carousels, custom widgets — that render inline in the ChatGPT conversation. The launch partners tell you the intended shape: Booking.com, Canva, Coursera, Figma, Expedia, Spotify, Zillow — consumer-scale services where an interactive surface genuinely beats prose.

The decision comes down to what you’re optimizing for — reach or richness:

• Build a plain MCP server when you want to be reachable from every assistant, when agents (not just humans) are consumers, when the value is data and actions rather than visuals, or when you simply want one artifact to maintain. Most business-data integrations live here — the answer is a number or an action, not a widget.
• Build a ChatGPT app when an interactive, visual experience inside ChatGPT genuinely is the product — booking a stay, editing a design, browsing a catalog — and ChatGPT’s enormous consumer audience is your target channel. The UI investment pays off precisely when a text answer would be a worse experience.

For a data platform like ours, the answer is unambiguous: the value is the tools — analytics, CRM, revenue queries an agent calls with no UI required — so we expose a plain MCP server that works identically from ChatGPT, Claude and Gemini. A carousel wouldn’t make “what’s our conversion rate this week” better.

The fair worry about building a ChatGPT app is single-platform lock-in, and the nuanced answer is: partially, but not at the layer that costs the most to build.

Because the Apps SDK sits on MCP, your app’s tool layer — the backend logic, the tool definitions, the auth — is standard MCP and reusable. What’s ChatGPT-specific is the UI components. So a team that builds a ChatGPT app and later wants Claude and Gemini reach can typically expose the same underlying MCP server as a plain server elsewhere; they re-do the presentation, not the plumbing.

The durable takeaway survives the churn: MCP is the foundation either way. Building the MCP server is never the wrong first move — it’s reusable whether you stay plain or add a ChatGPT UI, and it’s what makes you reachable from the rest of the ecosystem. Our what-is-MCP doc and the MCP vs API guide cover that base if you’re deciding where to start.