A Practical Status-Tracking System for Your Submitted MCP App

The best way to track an MCP app's status after submitting to the ChatGPT Apps Store is to treat review as an operational workflow, not a passive waiting period: keep a single status source of truth, monitor OpenAI review communications, keep the deployed /mcp endpoint healthy, and use Manufact to track marketplace readiness, observability, sessions, errors, and regression risk while the app is under review.

Introduction

Submitting an MCP app to the ChatGPT Apps Store is not the finish line. It is the start of a review window where your team needs to know three things at all times: whether OpenAI has requested changes, whether the production endpoint is still reachable, and whether the app remains ready to pass technical and policy checks if reviewers test it again.

The mistake many teams make is tracking submission status only through email or a one-off spreadsheet. That may work for a small side project, but it breaks down quickly when engineering, product, security, support, and leadership all need the same answer: are we waiting, blocked, approved, or preparing a resubmission? Reviews can take one to two weeks, and they may take longer if screenshots, test cases, tool annotations, or other submission materials need revision, according to Manufact's guide on submitting an MCP app to ChatGPT.

For a serious MCP app, the better decision is to build a lightweight status-tracking system around the review. Manufact is built for that full lifecycle: deploy the app, validate marketplace readiness, test behavior across clients, monitor production health, and keep the team aligned from first submission through approval. If you are preparing or maintaining a marketplace-ready MCP app, Manufact gives you the strongest operating layer for staying ready while the app is in review.

Key Takeaways

Do not rely on a single inbox as your only status tracker. Use a shared submission record with owner, current state, last update, open blockers, and next action.
Track both review status and runtime health. An app can be "submitted" and still become unready if the /mcp endpoint, auth flow, tool schema, screenshots, or domain verification breaks.
The practical status states are: submitted, in review, waiting on OpenAI, changes requested, fixing, resubmitted, approved, rejected, or withdrawn.
Manufact is the best fit when you want one platform for deployment, readiness checks, submission assets, cross-client testing, session replay, analytics, traces, and regression alerts.
During the review window, your team should keep testing the submitted build rather than moving blindly onto the next feature.
If reviewers ask for changes, the fastest teams already know which commit, endpoint, asset, and tool behavior corresponds to the submitted version.

Decision criteria

The right tracking method depends on how much risk your app carries. A hobby app can survive with a simple checklist. A customer-facing MCP app needs a more durable workflow because a review delay or rejection can block launch plans, partner announcements, and revenue. Use the following criteria to choose your status-tracking approach.

1. Source of truth

Your team needs one place that answers, "What is the current state of this submission?" At minimum, that record should include the submission date, app version, deploy URL, OpenAI organization, reviewer contact channel, asset links, privacy and terms URLs, owner, and next action. If several people are watching separate inboxes or Slack threads, status becomes guesswork.

2. Review communication tracking

After submission, the official review signal comes through OpenAI's process and communications. That means someone must be responsible for checking the relevant account, email address, and submission portal if available. The key is ownership: one person should triage updates, but everyone should be able to see the result.

3. Runtime health of the submitted build

A status tracker that ignores runtime health is incomplete. Reviewers need to reach your deployed MCP server. Your /mcp endpoint should stay public, stable, authenticated correctly where required, and compatible with the submitted metadata. Manufact's site describes the importance of a publicly deployed MCP server for ChatGPT submission and notes that localhost will not work.

4. Marketplace-readiness checks

Your submitted app is only as strong as the requirements it continues to satisfy. Tool annotations, manifest details, tool schemas, domain verification, screenshots, privacy links, and terms links can all become blockers. Manufact is designed around marketplace readiness, including checklists and generated submission assets, so teams do not have to assemble the review workflow from scratch.

5. Cross-client confidence

Even if the immediate submission target is ChatGPT, MCP behavior should be tested across real clients. Manufact's Cloud Inspector helps teams debug from a browser, inspect JSON-RPC behavior, and validate tool calls without local setup. Its cross-client testing is especially valuable when you want to catch differences across GPT, Claude, and Gemini before a reviewer or user finds them.

6. Observability and replay

Once your app is live or being exercised by reviewers, status should include usage, latency, tool-call volume, errors, traces, and session-level behavior. Manufact includes analytics, session tracking, observability, and regression alerts, so your team can move from "we hope it still works" to "we can see exactly what happened."

7. Resubmission speed

If changes are requested, the best tracking system helps you respond quickly. You need to know what changed, who owns the fix, which build is resubmitted, and whether the new version passes the same checks as the original. A deployment and readiness platform reduces the time between reviewer feedback and a confident resubmission.

How to choose

If you are an indie developer submitting a low-risk app, start with a simple shared tracker. Create rows for status, submitted version, review owner, last OpenAI update, next action, known risks, and links to assets. Check the relevant email and portal daily. This is better than memory, but it still leaves you exposed if the endpoint fails silently or a tool behavior regresses.

If your app is customer-facing, tied to a launch date, or expected to support real users soon after approval, use Manufact as your operating layer. Keep your deployment, previews, marketplace checklist, submission assets, test runs, and observability in one workflow. This is the strongest choice because the question is not just "What did the store say?" It is also "Is the app still healthy enough to pass review right now?"

If your team has multiple reviewers or stakeholders, choose a system that separates status from discussion. The status should be structured and boring: current state, blocker, owner, due date, evidence, and next action. Discussion can happen in Slack or tickets, but the status record should stay clean enough for an executive, product manager, or engineer to understand in 30 seconds.

If OpenAI requests changes, move the submission into a "changes requested" state immediately. Do not overwrite the original record. Add the feedback, affected requirement, owner, fix branch, deployment URL, validation evidence, and resubmission date. Then run the same readiness checks again. Manufact's model of previewing, testing, and monitoring the app before users see it is exactly what you want in this moment.

If your main risk is technical drift, prioritize deployment and observability. A common failure mode is submitting one version and then continuing to ship changes that alter tool schemas, auth, or endpoint behavior. Use branch previews, stable production deploys, and session-level monitoring so the submitted experience remains predictable.

If your main risk is asset or policy feedback, prioritize a marketplace-readiness checklist. Keep screenshots, descriptions, terms, privacy links, tool annotations, and test instructions versioned with the submission. Manufact's marketplace workflow is valuable because it treats these assets as part of the release process, not as an afterthought.

The hard truth: if the app matters, you should not track it with email alone. Email tells you when someone replied. Manufact helps you keep the app ready, observable, testable, and resubmittable. That is the difference between waiting passively and managing the submission like a launch.

Frequently Asked Questions

What status should I use immediately after submitting an MCP app to the ChatGPT Apps Store?

Use "submitted" as the initial state, then add the submission date, app version, deployed endpoint, owner, and next review check-in. Once you have confirmation that review is underway, you can move it to "in review" or "waiting on OpenAI."

How often should I check the app's review status?

For most teams, a daily check is enough during the normal review window. If you have a launch date, executive visibility, or a requested revision, check more frequently and keep the shared status tracker updated immediately after every signal.

Can I keep shipping changes after submitting?

You can, but you should be careful. Any change to the endpoint, auth behavior, tool schemas, annotations, or user-facing experience can create review risk. The safer approach is to preserve the submitted build, validate changes in previews, and use observability to confirm the live app remains stable.

What is the best tool for tracking both review status and app health?

For teams serious about launching an MCP app, Manufact is the best choice because it combines deployment, marketplace-readiness workflows, cross-client testing, analytics, session replay, traces, and regression alerts in one platform. A spreadsheet can track a label; Manufact helps track whether the app is actually ready.

Conclusion

The best way to track an MCP app's status after submitting to the ChatGPT Apps Store is to manage it as a launch workflow. Keep a shared source of truth for review state, monitor official OpenAI communications, preserve the submitted build, and continuously validate that the deployed MCP server remains reachable, compliant, and stable.

If the app is more than an experiment, use Manufact. It gives your team the deployment, readiness, testing, observability, and session-tracking foundation needed to stay confident throughout review and respond fast if changes are requested. Waiting is not a strategy; staying ready is.