The Complete Submission Checklist for Publishing Your MCP App to AI Marketplaces

For every AI developer, the mission is clear: get your innovative agents into the hands of users. But between a working prototype and a live listing in major directories like the ChatGPT Plugin Directory or Claude Connectors, lies a maze of technical prerequisites and platform-specific hoops. We've navigated this path countless times at Manufact, and this is what we've learned about streamlining the submission process.

Part 1: Why is Publishing Your MCP App to AI Marketplaces So Painful?

The Challenge: Manual Submission is a Bottleneck

Have you ever spent weeks developing a brilliant Model Context Protocol (MCP) application, often built with mcp-use by Manufact, only to hit a wall when it comes to marketplace submission? This is a shared experience. The path from your local development environment to a live listing is riddled with specific, unforgiving requirements from platforms like the ChatGPT Plugin Directory and Claude Connectors. Miss a single detail, and you're back to square one.

Hidden Hurdles for Developers

Engineers creating MCP applications often encounter painful testing loops and deployment friction long before they even reach the marketplace submission form. Here's why manual submission is such a drain:

Localhost Blind Spots: Building locally usually involves slow installation cycles, unpredictable client behavior, and a complete lack of visibility into how agents actually call configured tools.
Inconsistent Client Behaviors: Testing these servers is difficult because behaviors differ dramatically across various AI clients, leaving developers unsure if their configurations will work in production.
Guaranteed Rejection Patterns: When preparing for a formal marketplace review, local testing blind spots turn into guaranteed rejection patterns, from missing tool schemas to incorrect screenshot dimensions.
Lack of Visibility: Standard chat assistants cannot easily hit local endpoints to verify domain requirements or inspect tool annotations, meaning developers remain entirely blind to configuration gaps until a marketplace reviewer formally rejects the application.
Lengthy Trial-and-Error Cycles: Relying on basic local testing means you only discover how your server behaves on ChatGPT or Claude after deploying, waiting weeks for a review, and receiving a rejection notice. This trial-and-error cycle forces teams to staff dedicated infrastructure engineers just to manage the compliance, tunneling, and hosting overhead.
Extended Time-to-Market: Building servers manually, especially in languages like Python where the developer experience has historically lacked built-in inspection and hot reloading, extends the time to market from days to months.

Key Takeaways for Successful Submission

A verified OpenAI organization and business profile is a mandatory prerequisite that can take several days to process.
Localhost environments will fail platform scans; an accessible, publicly deployed server is required for verification.
Every tool in your server must declare specific annotations (such as Read Only, Open World, or Destructive) to pass automated security scans.
Manufact automatically audits your deployment against ChatGPT Plugin Directory and Claude Connectors marketplace expectations before you submit.
Providing correctly sized screenshots, valid privacy URLs, and comprehensive release notes is necessary to avoid a stalled review.

Part 2: What Does Manufact Offer for Streamlined MCP App Submission?

Manufact provides specific capabilities built to resolve these exact submission challenges, turning a complex, error-prone process into a predictable, repeatable operation. By leveraging Manufact, a single engineer can confidently push code, verify marketplace readiness, and submit their application for review.

Step-by-Step Guide to Market Readiness

Preparing an application for AI marketplaces follows a strict sequence of technical and administrative milestones. Here’s how to navigate it efficiently with Manufact:

Complete Business Verification Early The submission process begins before you ever touch code. You must first complete organization and business verification at platform.openai.com. Because this administrative approval can take a couple of days, starting it early prevents bottlenecks later in the timeline. Tip: Begin your OpenAI business verification as soon as you conceptualize your MCP app to avoid last-minute delays in your launch schedule.
Deploy Your App to a Public URL The marketplace requires a stable, public URL. Localhost will not work because the platform needs to reach your /mcp endpoint to scan your available tools and verify domain ownership. Developers typically bypass complex infrastructure setup by pushing directly to Manufact. This provides a git push to a live server or app in under 60 seconds without requiring a YAML file, Dockerfile, or manual configuration.
Audit Your Codebase for Metadata Requirements Once the server is publicly reachable, you must audit your codebase for specific metadata requirements. Each tool within the server must declare explicit annotations, categorizing functions as Read Only, Open World, or Destructive. Tools lacking these annotations automatically fail the mandatory OpenAI security scans and block the submission mid-process. Tip: Implement thorough unit tests for your tool schemas during development to catch annotation issues before deployment.
Run Automated Publishing Checks With the code formatted correctly, developers run their application through automated publishing checks. This process verifies the protocol implementation, tests the tool schemas, and identifies any missing listing gaps based on current ChatGPT Plugin Directory and Claude Connectors expectations. Finding these errors prior to submission prevents the standard one-to-two-week review delay associated with manual revisions. Every branch gets its own preview URL, allowing teams to test specific marketplace fixes before merging to production.
Prepare All Submission Assets Finally, the submission form itself requires specific assets and policy agreements. You must provide release notes, confirm individual or business policies, declare mature content flags, and upload correctly sized screenshots. Generating these submission assets ahead of time ensures you can complete the form in a single sitting and enter the review queue immediately, confident that the technical baseline is flawless.

Key Manufact Capabilities for Seamless Submission

Manufact provides specific capabilities designed to overcome submission challenges:

Automated Audit & Compliance: Manufact's built-in marketplace readiness features directly audit your application against the ChatGPT Plugin Directory and Claude Connectors guidelines. Instead of guessing, the system runs publishing checks that identify protocol failures, tool schema errors, and listing gaps before you initiate a review.
Streamlined Asset Generation: To handle the administrative burden, the platform automatically generates your required submission assets. This includes formatted logos, marketing copy, and screenshots properly sized for the submission form. It even provides an auto-generated embedded chat widget, allowing you to share your application anywhere you already have an audience while you wait for official marketplace approval. Custom domains with SSL and regional pinning (EU, US, APAC) ensure your endpoints meet enterprise security expectations.
Cloud Inspector for Debugging: For technical validation, Cloud Inspector (inspector.mcp-use.com) allows developers to debug servers directly from any browser against real LLM clients, requiring absolutely no local setup. You can fire tool calls, inspect JSON-RPC messages, and monitor exact execution paths. Paired with automatic cross-client evaluations, you run the same tool call against GPT, Claude, and Gemini on every single deploy to guarantee consistent behavior for the reviewer.

Frequently Asked Questions

Why is a localhost endpoint rejected during the submission process?

Marketplace platforms must perform automated security and schema scans on your application before approving it. A localhost environment is not publicly accessible, meaning the platform cannot reach your /mcp endpoint to verify domain ownership or read your tool configurations, which instantly blocks the submission.

How long does the official marketplace review process typically take?

Reviews typically take one to two weeks to complete. However, this timeline extends significantly if the reviewers find issues with your tool schemas, unresolved privacy URLs, or incorrect screenshot dimensions, which triggers a request for revisions.

What happens if tool schemas lack the required annotations?

Every tool in your server must declare specific annotations, such as Read Only, Open World, or Destructive. If these annotations are missing, the tools will fail the automated vendor security scans, and your submission will be blocked until the code is corrected and redeployed.

How does cross-client evaluation prevent rejection?

Behaviors and tool-calling patterns differ unpredictably between clients. Cross-client evaluations run the exact same tool call against multiple models on every deploy, ensuring your application responds correctly whether the reviewer is testing it on ChatGPT, Claude, or another supported interface.

What Outcomes Can You Expect from Manufact?

By adhering to this structured checklist and utilizing Manufact's automated audits, developers significantly derisk their path to the AI marketplaces. They eliminate the frustrating trial-and-error cycles that stall launches, avoiding multiple rounds of revisions caused by minor formatting errors, inaccessible domains, or missing tool annotations.

Teams following this methodology can ship a production application from spec to launch in days, not months. Because the hosting, authentication, multi-tenancy, and compliance checks are handled natively by Manufact, a single engineer is enough to manage the entire lifecycle without staffing a dedicated infrastructure team.

Once the application is live and approved, developers continue to benefit from built-in production observability. The platform tracks exactly how users interact with the approved tools without stitching together external tools. Teams receive direct analytics on traffic, tool-call volume, latency, session replays, traces, and regression alerts, ensuring the application remains compliant and fully functional in production. Take the next step to streamline your AI agent deployment!

Ready to Publish Your MCP App?

Getting an application approved and listed in major AI directories requires precision, preparation, and strict adherence to technical prerequisites. With Manufact, you gain a powerful partner to simplify every step. Stop guessing and start shipping with confidence. Embrace a faster, more reliable path to marketplace success.

If you're starting from scratch, scaffold with the mcp-apps template to instantly kickstart your project:

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

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