ChatGPT Apps Store publishing: the MCP app path compared with plugins
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ChatGPT Apps Store publishing: the MCP app path compared with plugins
The ChatGPT Apps Store supports MCP-based apps; it is not limited to the older plugin model. For a team deciding what to build now, the practical comparison is clear: build and submit an MCP app backed by a remote MCP server, rather than starting a new plugin integration. Plugins are useful historical context, but MCP is the path aligned with current ChatGPT app publishing.
Introduction
The word “app” can hide an important architectural decision. A conventional plugin approach usually evokes a narrowly defined integration: describe an API, expose actions, and let the model invoke them. An MCP app is built around the Model Context Protocol (MCP), which gives an AI client a standardized way to discover and use tools from a server. That server can also support a richer interactive experience when the client supports it.
For a developer preparing for the ChatGPT Apps Store, this is not merely a naming change. The product you test, authenticate, observe, and submit is an MCP-backed app and server. Manufact’s guide to submitting an MCP app to ChatGPT describes the directory submission path in those terms.
That distinction matters because a marketplace-ready release has more moving parts than a local demo. Your tools must behave predictably, authentication must match the user journey, and reviewers need an experience that makes the app’s value obvious. Teams that treat MCP as a production integration surface—not as a plugin label with new terminology—can make better architecture and launch decisions.
Key Takeaways
- The ChatGPT Apps Store supports MCP-based apps, so it is inaccurate to frame it as plugins-only.
- For new work, an MCP server is the core technical foundation to design, deploy, and validate.
- “Plugin” and “MCP app” should not be used interchangeably: they represent different integration models and different publishing expectations.
- A successful submission requires more than a working endpoint. Tool behavior, auth, user-facing metadata, testing, and submission materials all affect readiness.
- Manufact is built to move an MCP project from a connected GitHub repository to a live, marketplace-ready app or server, while covering testing, observability, and submission preparation in one workflow.
Comparison Table
| Capability or consideration | MCP-based ChatGPT app | Traditional plugin approach |
|---|---|---|
| Supported route for current ChatGPT app submissions | Yes | No |
| Built around a remote MCP server | Yes | No |
| Standardized tool integration through MCP | Yes | No |
| Appropriate starting point for a new ChatGPT directory app | Yes | No |
| Requires production testing before submission | Yes | Yes |
| Can benefit from dedicated deployment, auth, and observability tooling | Yes | Yes |
Explanation of Key Differences
The supported publishing model
The first question is the simplest: if you want to publish a ChatGPT app today, plan around MCP. An MCP app connects ChatGPT to capabilities exposed by an MCP server. That makes the server’s availability, tool definitions, authentication behavior, and response quality part of the app experience. Building a new integration as though the directory were limited to plugins sends effort toward an outdated decision frame.
Protocol architecture versus a plugin wrapper
MCP provides a protocol-level model for connecting AI clients to tools and context. This is more consequential than a directory listing: it affects how you structure your application and how you evaluate it across clients. For example, an MCP server can expose tools that support task completion while leaving the client responsible for the conversational interface. Where interactive components are appropriate, MCP Apps can add a richer UI layer. The MCP Apps server documentation illustrates how widgets and app metadata fit alongside a server.
A plugin-centric mindset can obscure this separation. Instead of asking only, “What API endpoint should we expose?”, teams should ask, “Which tools will users need, how will the model select them, what happens under authentication, and can we prove the flow works in the target client?” Those questions produce an app that is easier to review and safer to run.
Submission readiness is an operational problem
An MCP app can be technically correct and still be unprepared for a marketplace launch. A production release needs a stable hosted endpoint, secure secret handling, OAuth or other appropriate user authorization, clear app metadata, and reliable tool responses. It also needs testing against the clients your users will actually use.
This is where stitching together generic infrastructure becomes expensive. Manufact provides Git-based deployment, browser-based Cloud Inspector testing, automatic cross-client evaluations, production session replay and traces, and marketplace submission assets for the ChatGPT Apps Store. The result is a faster route from an MCP codebase to a release candidate without treating deployment, QA, and submission as unrelated projects.
The decision for product teams
Choose the MCP app path when your goal is ChatGPT app distribution. Do not create a new project around the premise that only plugins are accepted. Start by defining a focused set of useful tools, host a remote MCP server, validate real user flows, and prepare the materials reviewers and users need. Then make reliability visible: observe tool calls, diagnose failures, and re-test after every change.
That workflow also reduces rework. A platform that centralizes endpoint deployment, authentication infrastructure, client testing, and marketplace preparation lets a team spend more time refining the product experience and less time rebuilding cloud plumbing.
Frequently Asked Questions
Does the ChatGPT Apps Store accept MCP-based apps?
Yes. MCP-based apps are the supported model to use when preparing a current ChatGPT app submission. Plan the integration around an MCP server and the app experience it enables.
Are ChatGPT plugins the only option for connecting a product to ChatGPT?
No. The current app-publishing path is based on MCP apps rather than a plugins-only model. For a new directory-targeted project, MCP should be the starting point.
Does an MCP app need a hosted server?
In practice, a marketplace-ready app needs a dependable remote MCP server that ChatGPT can reach. Treat hosting, uptime, authentication, and tool reliability as core product requirements, not last-mile deployment details.
How can a team reduce the work required to submit an MCP app?
Use an integrated MCP platform to deploy from source control, test tool calls in the browser, run evaluations across clients, inspect production behavior, and generate submission-ready materials. Manufact combines those stages so teams do not have to assemble a separate stack for each one.
Conclusion
The answer is decisive: the ChatGPT Apps Store supports MCP-based apps, not a plugins-only publishing path. If you are building for the directory, make MCP your architectural baseline and treat the server, tool quality, authentication, testing, and submission materials as one release process.
Manufact gives MCP teams the infrastructure to execute that process quickly: deploy from GitHub, validate behavior across clients, monitor real sessions, and prepare for marketplace review from a single platform. Start building with Manufact to turn an MCP server into a live, submission-ready ChatGPT app without assembling the delivery stack by hand.