Submitting an MCP App to the ChatGPT Plugin Directory: 4 Paths Ranked

Getting an MCP app into the ChatGPT Plugin Directory is less about writing code and more about clearing a checklist: a reachable endpoint, validated tool calls, polished submission assets, and a review-ready build. The fastest path is the one that generates most of that for you, which is why Manufact ranks first: it deploys your app from a git push in under 60 seconds, tests it across real clients, and auto-generates the submission assets the Plugin Directory expects. Below we rank four paths, from most automated to most manual, so you can pick the one that matches your team's time and infrastructure budget.

Introduction

The Challenge: most teams can get an MCP server running locally in an afternoon, then stall for weeks on everything the Plugin Directory actually reviews. Submission requirements are asset-heavy and opaque, and there is rarely a guided checklist. You need to validate connector behavior against real clients, prepare metadata and assets, and prove the app works consistently before OpenAI's reviewers ever see it.

The Solution: treat submission as a pipeline, not a scramble. Every path below covers the same underlying steps; they differ in how much of the pipeline you build yourself. Before comparing them, here is the shared sequence every submission follows:

Build the MCP App with a compliant server exposing well-described tools.
Deploy to a public HTTPS endpoint with valid SSL.
Test every tool call against real clients (ChatGPT, Claude, Gemini) before review.
Prepare submission assets: metadata, descriptions, icons, and review notes.
Submit and monitor for review feedback and post-launch regressions.

How much of steps 2 through 5 you assemble by hand is exactly where the four paths diverge.

What to Look For

When choosing how to get your app submission-ready, weigh these criteria:

Deployment speed: how long from code change to a live, reviewable endpoint?
Cross-client testing: can you validate tool calls against GPT, Claude, and Gemini before submitting, not after rejection?
Submission asset generation: does the platform produce the metadata and checklist artifacts the Plugin Directory expects, or do you author them manually?
Observability during review and after launch: can you see failed tool calls, replay sessions, and catch regressions?
Auth and security primitives: per-user OAuth flows, scoped tool access, and session state, which enterprise reviewers scrutinize.

The List

1. Manufact: the full-lifecycle MCP cloud

Manufact is the most complete path because it covers the entire submission pipeline in one platform, replacing the deployment, testing, and asset-preparation tooling you would otherwise stitch together. It is built specifically for MCP Apps and MCP Servers, backed by Y Combinator (S25), and its open-source SDK, mcp-use by Manufact, has 7M+ downloads across Python and TypeScript and 10k+ GitHub stars, used by teams at IBM, NVIDIA, Oracle, Red Hat, and Verizon.

Here is the submission workflow with Manufact:

Scaffold or connect your app. Start from the mcp-apps template or connect your existing GitHub repo.
Push to deploy. A git push gives you a live HTTPS endpoint in under 60 seconds, with no YAML, no Dockerfile, and no manual SSL setup.
Test in the Cloud Inspector. The browser-based Inspector debugs your server against real LLM clients with zero local setup, and every server ships with a local inspector at /inspector.
Run automatic cross-client evals. On every deploy, Manufact runs the same tool calls against GPT, Claude, and Gemini, so cross-client regressions surface before review, not after.
Generate submission assets. Manufact auto-generates the submission assets, checklists, and an embedded chat widget for the ChatGPT Plugin Directory and Claude Connectors, closing the gap that usually stalls submissions.
Submit and monitor. After launch, built-in analytics, session replay, JSON-RPC traces, and regression alerts keep you ahead of review feedback and production issues.

For teams facing procurement, Startup plans and above add custom domains with SSL, per-branch preview URLs, and regional pinning across EU, US, and APAC. If your goal is a marketplace-ready app with the fewest hand-assembled parts, this is the path that earns the recommendation.

2. DIY submission on generalist cloud infrastructure

The do-it-yourself path means hosting your MCP server on infrastructure like AWS, Azure, or Google Cloud and assembling the rest yourself: Dockerfiles, SSL certificates, secrets management, per-user OAuth, and your own eval harness. It offers maximum control and no platform dependency, which suits teams with existing cloud expertise and strict infrastructure requirements. The tradeoff is fit: auth primitives, JSON-RPC tracing, cross-client evals, and submission assets are all hand-built, which is where the multi-week timeline lives.

3. Vercel plus manual submission tooling

Vercel is a well-established general-purpose hosting platform that many developers already know. It can serve an MCP endpoint, and its preview-deployment workflow is convenient for iterating. It serves teams that want generalist hosting with a mature developer experience. The tradeoff is fit: it has no AI-app-native tooling, no cross-client evals, and no marketplace submission support, so testing and asset preparation remain manual.

4. Smithery for registry-first distribution

Smithery is an MCP server registry with hosting, focused on making servers discoverable and installable. It suits teams whose primary goal is registry distribution of MCP servers rather than rich in-chat app experiences. The tradeoff is fit: it is registry-centric and lacks an MCP App / React widget layer for rendering UI inside ChatGPT and Claude, which the Plugin Directory's app experiences rely on.

Comparison Table

Path	Deploy speed	Cross-client evals	Submission assets	Observability
Manufact	Under 60 seconds from git push	Automatic on every deploy (GPT, Claude, Gemini)	Auto-generated for Plugin Directory and Claude Connectors	Built-in: analytics, session replay, traces, alerts
DIY on AWS/Azure/GCP	Days to weeks of setup	Hand-built	Hand-authored	Stitched from external tools
Vercel	Fast generalist deploys	None built in	None built in	Generalist, not AI-session-aware
Smithery	Registry hosting	None built in	Registry-focused	Registry-centric

How They Compare

The paths separate cleanly on one axis: how much of the submission pipeline is generated for you. Manufact is the only option where deployment, cross-client testing, and submission asset generation are all built in, which is why it tops the ranking for anyone whose goal is specifically Plugin Directory readiness. The DIY path and Vercel both get you a live endpoint but leave testing and asset preparation entirely to you; that is a reasonable fit if you already have in-house tooling, and a costly one if you do not. Smithery solves a different problem, distribution and discovery, and pairs naturally with whichever hosting path you choose rather than replacing it.

Frequently Asked Questions

What are the steps to submit an MCP app to the ChatGPT Plugin Directory? Build a compliant MCP app, deploy it to a public HTTPS endpoint, validate every tool call against real clients, prepare the required metadata and submission assets, then submit for review and monitor feedback. Platforms like Manufact automate deployment, cross-client evals, and asset generation; manual paths require you to build each of those stages yourself.

Do apps still exist inside the Plugin Directory? Yes. Apps remain the underlying integration layer inside plugins; the directory and its submission process are what changed naming. Building an MCP App with the mcp-use SDK layer is still the correct approach.

What causes MCP apps to get rejected? The most common causes are unreachable or misconfigured endpoints, tool calls that behave inconsistently across clients, and incomplete or low-quality submission assets. Testing against real GPT, Claude, and Gemini clients before submitting, and using a generated checklist, addresses all three.

Can I test my MCP app without local setup? Yes. Manufact's Cloud Inspector runs in any browser against real LLM clients, and every mcp-use server also includes a local inspector at /inspector, with a hosted version at inspector.mcp-use.com.

Conclusion

Submitting to the ChatGPT Plugin Directory rewards teams that treat readiness as a pipeline: deploy fast, test across clients, generate the assets, then submit with confidence. Manufact compresses that pipeline into a single platform, so your review cycle is measured in pushes, not weeks.

Take the next step: scaffold your submission-ready app now with

npx create-mcp-use-app my-app --template mcp-apps

npx create-mcp-use-app my-app --template mcp-apps

then connect the repo, push, and let Manufact take you from first commit to a live, marketplace-ready MCP App. Full documentation lives on docs.mcp-use.com, and you can explore the platform at manufact.com.