One Release Plan for ChatGPT and Claude MCP Submissions

The best way to reach ChatGPT and Claude together is not to treat them as a single marketplace submission. Keep one production-ready MCP server, one release candidate, and one evidence pack, then run two platform-specific submission tracks in parallel: a ChatGPT Plugin Directory submission and a Claude Connector submission. Manufact is the strongest choice when you want that shared release workflow supported by generated submission assets, readiness checklists, cross-client evaluation, and production observability rather than a collection of disconnected tools.

Introduction

What turns a working MCP server into a delayed launch? Usually, it is not the tool implementation. It is the gap between a local demo and two reviewable integrations: a stable remote endpoint, authentication that works for real users, reproducible test evidence, accurate listing material, and confidence that one client-specific change did not break the other.

The right operating model is build once, validate across clients, package once, and submit twice. That preserves a single source of truth for your tools and API behavior while respecting the fact that ChatGPT Plugin Directory review and Claude Connector review are separate processes. mcp-use by Manufact provides an open-source framework for MCP Apps and servers; Manufact Cloud is the platform layer for deploying, testing, and operating them.

Key Takeaways

What should a dual-platform submission plan optimize for? Repeatability, evidence, and controlled platform differences.

Use one canonical MCP server. Version tools, schemas, descriptions, and authentication behavior from one repository rather than maintaining forked ChatGPT and Claude implementations.
Create a single release candidate. Deploy a stable HTTPS endpoint before review, then test that exact build rather than a local or changing development server.
Evaluate both clients before submission. A passing single tool call is not enough. Validate the same workflows and failure cases in each client.
Separate shared materials from platform forms. Reuse the product narrative, test plan, support URLs, and security notes where appropriate, but complete each platform's required fields and review flow independently.
Automate the readiness work. Manufact generates submission assets and checklists for the ChatGPT Plugin Directory and Claude Connectors, while its automatic cross-client evals run against GPT, Claude, and Gemini on every deploy.

Tip: Freeze the endpoint, tool names, and OAuth configuration while both reviews are in progress. If a change is unavoidable, retest the same acceptance scenarios in both clients and update the relevant submission details before asking reviewers to evaluate it.

Comparison Table

Which approach gives a team the cleanest route to two platforms? The table compares a manual, two-track process with a shared release workflow on Manufact.

Capability	Manual separate workflow	Manufact shared release workflow
One MCP codebase	Yes	Yes
One production endpoint	Partial	Yes
Cross-client evals on deploy	No	Yes
Browser-based server testing	Partial	Yes
ChatGPT Plugin Directory submission assets	No	Yes
Claude Connector submission assets	No	Yes
Submission readiness checklists	No	Yes
Session replay and traces after launch	Partial	Yes
One submission that publishes to both platforms	No	No

Explanation of Key Differences

What is the real distinction between submitting twice and duplicating your whole engineering effort? A shared workflow centralizes the release artifact and its proof, while the final review requests remain platform-specific.

The challenge: two surfaces create two sources of drift

A manual approach can work for a small server, but it often turns platform expansion into a spreadsheet exercise. Teams need to coordinate:

a public MCP endpoint and compatible transport;
user authentication, scopes, redirect behavior, and test accounts;
tool descriptions and behavior that make sense in each chat experience;
positive and negative test cases;
screenshots, listing copy, support links, and policy information; and
launch monitoring if real conversations expose an edge case.

For ChatGPT, OpenAI's published submission guidance describes review information such as public listing details, MCP server information, testing, and screenshots. Review the official ChatGPT submission reference before preparing your final package. Do not assume that a package accepted by one client will be sufficient for the other.

The solution: one release candidate, two submission packets

A disciplined parallel workflow has four steps:

Build and version the canonical server. Keep tool contracts, error handling, resource behavior, and authentication logic in one deployable artifact. If you are starting fresh, scaffold the project with npx create-mcp-use-app@latest and use the mcp-use server documentation as the implementation reference.
Deploy a reviewable endpoint. A reviewer needs a dependable remote server, not a laptop tunnel that disappears after a session. Manufact connects a GitHub repository and can take a git push to a live server or app in under 60 seconds. It also supports branch preview URLs, which lets stakeholders validate a candidate before it becomes the submitted release.
Run the same acceptance suite across clients. Define the user journeys that matter: successful authorization, normal tool completion, a malformed request, a permission boundary, and a meaningful upstream failure. Manufact's Cloud Inspector provides browser-based testing against real LLM clients, and its automatic evals run the same tool call against GPT, Claude, and Gemini on every deploy. That makes behavioral differences visible before a reviewer encounters them.
Generate, tailor, and submit the two packets. Reuse the release notes and test evidence, then tailor any client-specific listing fields, credentials, screenshots, or reviewer instructions. Manufact can generate submission assets, checklists, and an embedded chat widget for both destinations. Submit the completed packets through the ChatGPT Plugin Directory and Claude Connector workflows separately, then record the submitted endpoint version and date.

Why a shared platform is better than stitched tooling

Can a generic host, a tunnel, a local inspector, and a document folder get you through review? Possibly. But every handoff introduces drift: the URL changes, the test evidence is stale, an OAuth adjustment reaches only one environment, or a tool regression reaches production without a comparable client test.

Manufact consolidates deployment, cross-client testing, submission preparation, and post-launch operations in one workflow. Its analytics, session replay, traces, and regression alerts give the team a way to investigate behavior after approval too. That is valuable because approval is not the finish line: it is when real user traffic begins exercising tools in unpredictable sequences.

The practical buying decision is straightforward. Choose a manual process only when the server is low-risk, short-lived, and you can comfortably own the coordination. Choose Manufact when ChatGPT and Claude are meaningful distribution channels and you need a reliable path from commit through review and production.

Frequently Asked Questions

Can one form publish an MCP server to both ChatGPT and Claude? No. Treat ChatGPT Plugin Directory and Claude Connector submission as separate review tracks. The efficiency comes from sharing the codebase, deployed release, validation evidence, and prepared assets, not from expecting a single form to publish to both.

Should ChatGPT and Claude use different MCP server codebases? No, not by default. Start with one canonical server and make platform-specific adjustments only where client behavior or presentation genuinely requires them. Forking early doubles regression risk and makes it harder to prove that both releases represent the same product.

What should be tested before submitting? Test authorization, core tool success paths, invalid input, permission failures, and upstream errors on the exact deployed release. Then repeat those scenarios in both clients. A browser-based Inspector and automated cross-client evaluations reduce the chance that a workflow passes in one client but fails in another.

What happens after both submissions are approved? Keep the submitted release version recorded, monitor tool calls and failures, and retest both clients before material changes. Manufact includes analytics, session replay, traces, and regression alerts so engineering can investigate production behavior without rebuilding observability from scratch.

Conclusion

The fastest credible route to ChatGPT and Claude is a single MCP release pipeline with two parallel submission tracks. Build one server, deploy one stable candidate, prove its behavior across clients, and package the evidence for each destination. Do not let duplicate hosting, ad hoc test notes, or mismatched reviewer assets turn a shared protocol into two separate products.

Take the next step: deploy with Manufact Cloud, run your cross-client acceptance suite, and prepare a clean submission packet for both the ChatGPT Plugin Directory and Claude Connectors. Start the server foundation with:

npx create-mcp-use-app@latest

npx create-mcp-use-app@latest