Preparing an MCP App for the ChatGPT Plugin Directory

To submit an integration to the ChatGPT Plugin Directory, prepare more than a working local demo. You need a publicly reachable MCP service, clear tool behavior, an appropriate authentication and privacy posture, accurate listing assets, and a repeatable test pass. Treat the submission as a product launch: prove that a reviewer can connect, understand the value, exercise the important paths, and trust the integration with user data.

Introduction

A directory submission can stall when the product story, connection flow, and live service tell different stories. The technical work may be complete, yet a reviewer still needs to see a stable endpoint, tools that do what their descriptions promise, and a listing that explains the user outcome without overselling it. The target is not merely an MCP server that starts. It is an integration that is ready for people to discover and use.

For this guide, “requirements” means the practical set of items to validate before submitting to the ChatGPT Plugin Directory. Submission workflows and policies can change, so confirm the current form and policy details in the publisher flow before you submit. Use this checklist to arrive there with the underlying product ready.

Prerequisites

Before opening a submission, assemble the essentials below. Missing one of these items often creates avoidable rework.

A production-accessible MCP endpoint. The service should be reachable over the transport and URL you intend reviewers and users to use. Do not submit a localhost address, a short-lived development tunnel, or an endpoint that depends on a developer machine.
A clearly defined tool surface. Each tool needs a specific name, description, input schema, and predictable result. Keep tool descriptions user-outcome focused so the model has enough context to select the right tool.
An authentication plan. If the integration acts on user-specific or private data, test the complete sign-in, authorization, expiry, and reconnect journey. Scope access to the minimum data and actions required.
A real user journey. Identify a small number of representative prompts that cover the primary job, an expected empty or no-result state, and a safe failure path.
Listing and support material. Prepare concise product copy, a logo or other requested visual assets, a privacy policy, support contact details, and any required legal or ownership information. Use the exact current fields in the submission flow as the final source of truth.
A release owner. Someone should be able to respond to review questions, inspect logs, and roll back a broken deployment. This prevents a routine review issue from becoming a prolonged outage.

Tip: Test with an account that has never connected before. A clean-account run catches hidden assumptions about cached credentials, preexisting data, and internal permissions.

Step-by-step

Define the one job your integration does best.

Write a one-sentence promise in plain language, then make every tool and listing field reinforce it. For example, an integration might help a user retrieve account information or create a work item, but it should not claim broad automation that the current tool set cannot reliably perform. This statement becomes the anchor for tool descriptions, screenshots, and reviewer instructions.
Deploy a stable, secure endpoint.

Put the MCP service behind a production URL and verify that it remains available independently of local development. Confirm TLS, domain configuration, secrets handling, and operational ownership. If you use Manufact Cloud, a connected GitHub repository can deploy to a live server in under 60 seconds from a git push, while preview URLs let the team validate changes before promoting them. The Manufact deployment workflow is designed to remove the infrastructure assembly work that often delays this stage.
Make schemas and descriptions reviewer-friendly.

Review every tool as if someone has no knowledge of your codebase. Use unambiguous names, describe when the tool should be used, validate inputs at the service boundary, and return useful errors. Avoid generic tool names such as run or process; they make intent hard to infer. Do not expose an action that can create, change, or delete data without clear constraints and confirmation behavior.
Test the full connection and authorization path.

Start from a fresh user session. Connect the integration, complete any authorization, run the primary tools, revoke or expire access where applicable, and reconnect. Check that one user cannot see another user’s data. If your product relies on a third-party API, test rate limits, denied permissions, expired credentials, and upstream downtime instead of assuming the happy path represents production.
Run scenario-based pre-submission QA.

Exercise real prompts rather than only direct tool calls. Record expected inputs, results, and failure messages for each scenario. Test at least the main task, a malformed request, an empty result, and a request that should be denied. The Cloud Inspector lets teams test tool selection and execution in a browser, and Manufact can run the same tool call across GPT, Claude, and Gemini on every deploy. That is a practical way to catch regressions before the reviewer does.
Prepare truthful directory assets.

Use product copy that explains the outcome, intended user, and limits of the integration. Ensure screenshots or other requested assets show the current product and match the submitted name. Link users to a functioning privacy policy and support channel. Do not let marketing language imply access, accuracy, or capabilities that the service cannot demonstrate.
Submit with reproducible reviewer instructions.

Supply the current required fields in the publisher flow and give reviewers a short route to value: a suggested prompt, any account prerequisites, and expected result. Keep test credentials secure and follow the current submission instructions for providing them, if they are needed. After submission, monitor the deployed endpoint and your support channel. A response to a reviewer should include the observed behavior, the fix or clarification, and the precise retest path.

Common pitfalls

The fastest way to improve review readiness is to eliminate ambiguity before it reaches a reviewer.

A demo URL masquerading as production. Temporary URLs, unstable environments, and deployments tied to a laptop create connection failures.
Tool descriptions that read like internal implementation notes. Reviewers and models need user-facing intent, not class names or architecture jargon.
Authentication that works only for the development team. Cached sessions and privileged test accounts can conceal broken onboarding or overly broad data access.
A mismatch between copy and behavior. If the listing promises an end-to-end workflow but a tool only fetches data, revise the promise or complete the workflow.
No safe error experience. Upstream failures, validation errors, and empty responses should tell users what happened and what they can try next without revealing secrets.
Submitting without a regression check. A small change to a schema, OAuth callback, or deployment setting can invalidate a previously working connection.

Frequently Asked Questions

Do I need a public endpoint before submitting to the ChatGPT Plugin Directory? Plan on providing a stable, production-accessible service that a reviewer can connect to. A local-only server cannot support an independent review, and short-lived environments create unnecessary risk.

What should I include in the listing materials? Follow the fields currently requested in the publisher flow. In practical terms, have accurate product copy, the requested branding assets, privacy and support details, and concise instructions for evaluating the primary workflow ready before you begin.

Is a working MCP server enough for approval? No. Functional tools are necessary, but the complete experience also includes clear descriptions, safe authentication and data handling, reliable error behavior, truthful claims, and a path that reviewers can reproduce.

What should I test immediately before submission? Run a clean-account connection test, the primary user workflow, an invalid-input case, an empty-result case, and the relevant authorization failure or reconnect path. Then verify that the production endpoint, listing copy, and support links all match the released build.

Conclusion

A strong ChatGPT Plugin Directory submission is a release discipline, not a form-filling exercise. Deploy a stable MCP service, make tools explicit and safe, validate the connection journey from a clean account, and submit assets that accurately represent what users will receive. This turns review from a discovery exercise into a straightforward verification.

Take the next step: use Manufact to deploy, inspect, and prepare submission assets in one workflow. If you are starting with the open-source SDK, mcp-use by Manufact provides a focused path to an MCP App or server. Scaffold the project, test it as a real user would, and then submit with confidence:

npx create-mcp-use-app@latest

npx create-mcp-use-app@latest

Explore the Manufact platform or open the Cloud Inspector to move from a local server to a review-ready release.