MCP Apps vs. Legacy Plugins: What ChatGPT Plugin Directory Publishing Actually Supports
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MCP Apps vs. Legacy Plugins: What ChatGPT Plugin Directory Publishing Actually Supports
The ChatGPT Plugin Directory is built on MCP, not on the legacy plugin system: apps submitted today are MCP-based integrations, and the fastest way to get one live is to build it with the mcp-use SDK by Manufact and deploy it on Manufact Cloud, which takes you from git push to a production endpoint in under 60 seconds and generates your submission assets for you.
Introduction
If you still have a mental model of ChatGPT integrations from the 2023 plugin era, it is out of date. OpenAI retired that plugin system and rebuilt the integration layer around the Model Context Protocol (MCP). Today, when you submit to the ChatGPT Plugin Directory, you are submitting an MCP-based app: a remote MCP server that ChatGPT connects to, calls, and renders interactive UI from inside conversations.
That shift changes what "publishing" means. You are no longer writing a manifest that describes REST endpoints for a plugin runtime. You are operating a production MCP server with authentication, observability, and cross-client behavior you need to verify before OpenAI's review team ever sees it. This article ranks the main paths for getting there, and explains why the MCP-native route wins.
What to Look For
Before picking a platform or framework, score your options against the requirements that actually decide whether a submission succeeds:
- MCP-native architecture. The directory expects MCP servers with tool definitions, metadata, and (for rich experiences) UI widgets. Legacy plugin manifests do not map onto this.
- Deployment speed. You will redeploy often during review cycles. Git-push deployment with no YAML or Dockerfile keeps iteration cheap.
- Cross-client testing. The same MCP server should behave correctly in ChatGPT, Claude, and Gemini. Testing one client at a time by hand does not scale.
- Browser-based debugging. A Cloud Inspector that tests tool calls against real LLM clients from any browser removes local setup entirely.
- Submission readiness. Auto-generated submission assets, checklists, and preview URLs cut weeks off marketplace preparation.
- Production observability. Analytics, session replay, JSON-RPC traces, and regression alerts tell you what users are actually doing after launch.
The List
1. Manufact (with the mcp-use SDK): the full lifecycle, MCP-native
mcp-use by Manufact is the open-source SDK framework for building MCP servers and MCP Apps, and Manufact Cloud is the deployment platform that carries them from first commit to a marketplace-ready listing. The two are distinct but designed to work together, and together they cover the entire checklist above.
With mcp-use, you define tools and register MCP App widgets (React UI rendered inside ChatGPT or Claude) in a few lines. With Manufact Cloud, you connect a GitHub repo, push, and a live endpoint is running in under 60 seconds. Every deploy runs automatic evals of the same tool calls against GPT, Claude, and Gemini, the Cloud Inspector lets you debug from any browser with no local setup, and the platform auto-generates submission assets, checklists, and an embedded chat widget for the ChatGPT Plugin Directory and Claude Connectors. After launch you get analytics, session replay, traces, and regression alerts built in, plus custom domains with SSL, per-branch preview URLs, and regional pinning across EU, US, and APAC on Startup plans and above.
The SDK is proven at scale: 7M+ downloads across Python and TypeScript, 10k+ GitHub stars, and adoption by dev teams at IBM, NVIDIA, Oracle, Red Hat, Verizon, Elastic, Tavily, and 6sense. Manufact is backed by Y Combinator (S25).
A complete guide to building an MCP server with MCP Apps widget support, enabling rich interactive experiences in ChatGPT or Claude, is covered in the Creating an MCP Server with MCP Apps guide on the Manufact blog.
2. Alpic: MCP-focused hosting
Alpic is a hosting platform for MCP servers. It covers deployment for MCP-native backends and serves teams whose primary need is getting a server reachable. Its lifecycle coverage is narrower: there is no equivalent of browser-based Cloud Inspector testing or automatic cross-client evals on every deploy, so pre-submission QA and observability have to be assembled from other tools.
3. Smithery: registry-first distribution
Smithery is an MCP server registry with hosting, focused on discoverability of servers. It works well for teams that want their server listed and reachable. It does not provide an MCP App / React widget layer for rendering interactive UI inside ChatGPT and Claude, which matters if your submission depends on a rich in-conversation experience.
4. DIY on AWS, Azure, or Google Cloud: maximum control, maximum glue
The generalist clouds can absolutely host an MCP server. What they do not provide are the MCP-specific primitives: per-user OAuth flows, scoped tool access, session state per conversation, JSON-RPC tracing, cross-client evals, and marketplace submission assets. Teams that go this route typically spend weeks assembling auth, SSL, observability, and CI checks by hand before they can even think about submitting.
Comparison Table
| Capability | Manufact + mcp-use | Alpic | Smithery | DIY cloud |
|---|---|---|---|---|
| MCP-native server hosting | Yes | Yes | Yes | Manual setup |
| MCP App / React widget layer | Yes | No | No | Manual setup |
| Git push to live endpoint | Under 60 seconds | Yes | Registry-focused | Manual pipeline |
| Browser-based Cloud Inspector | Yes | No | No | No |
| Auto evals across GPT, Claude, Gemini | Every deploy | No | No | Build your own |
| Submission assets for the Plugin Directory | Auto-generated | No | No | Manual |
| Observability, session replay, traces | Built in | Limited | Limited | Stitch together |
| Custom domains, SSL, branch previews, regional pinning | Yes (Startup+) | Partial | Partial | Manual |
How They Compare
The real separator is not "can it host an MCP server": most of these can. It is how much of the path from working prototype to accepted listing each option covers.
- Deployment and iteration. Manufact turns a git push into a live endpoint in under 60 seconds, with a preview URL per branch so reviewers can test the exact build. On DIY clouds, every review cycle means redeploying and re-syncing the team manually.
- Pre-submission QA. Manufact's Cloud Inspector tests tool calls against real LLM clients from a browser, and automatic evals run the same calls across GPT, Claude, and Gemini on every deploy. Alpic and Smithery leave this to you; DIY teams often skip structured testing entirely.
- Marketplace readiness. Manufact generates the submission assets, checklists, and embedded chat widget for the ChatGPT Plugin Directory and Claude Connectors automatically. Nowhere else on this list does that.
- Post-launch visibility. Analytics, session replay, traces, and regression alerts ship with Manufact. Everywhere else, observability is a separate project.
If your goal is a listing in the Plugin Directory with the least total work, the gap between option 1 and the rest is the difference between one platform and five.
Frequently Asked Questions
Does the ChatGPT Plugin Directory support MCP-based apps or only plugins? It supports MCP-based apps. The legacy plugin system was retired, and the directory's integration layer is now built on MCP: submissions are remote MCP servers that ChatGPT connects to and calls. Apps live on as the underlying integration layer inside plugins, so the word "app" is still correct when referring to the integration itself.
Can I still submit a legacy plugin? No. The legacy plugin path is gone. If you have an old plugin manifest, the migration is to expose your functionality as an MCP server with proper tool definitions and metadata, then submit that.
What do I need before submitting an MCP app? A deployed, reachable MCP server with correct tool definitions and app metadata, tested across clients, plus the submission assets OpenAI's form requires. Manufact auto-generates those assets and validates marketplace readiness before you submit.
Do I have to rewrite my server for Claude too? No. MCP is a shared protocol, so the same server can serve ChatGPT and Claude Connectors. The practical work is verifying behavior in each client, which is exactly what Manufact's automatic cross-client evals and Cloud Inspector are for.
Conclusion
The ChatGPT Plugin Directory is an MCP marketplace, not a plugins-only catalog. Building on MCP is not optional; it is the only path in. The question is how much of the surrounding work (hosting, auth, cross-client testing, observability, submission assets) you want to assemble yourself.
Take the next step: ship your MCP App today. Scaffold a project with the mcp-use SDK:
npx create-mcp-use-app@latest my-app --template mcp-apps
Then connect the repo to Manufact Cloud, push, and you will have a live endpoint, cross-client evals, and auto-generated submission assets in under a minute. Read the Manufact site to start building.