Hi HN, we are Pietro and Luigi, cofounders of Manufact (https://manufact.com), a cloud for MCP apps and servers. We used to be called mcp-use, and still build open source SDKs for MCP under that name: https://github.com/mcp-use/mcp-use. We did a Show HN about that last year: https://news.ycombinator.com/item?id=44747229.

Today we want to tell you about our cloud product, Manufact, which is to mcp-use as Vercel is to Next.js. Manufact is an MCP vertical cloud designed for dev teams putting MCP Apps and servers in production.You can ship, iterate on, test and monitor your MCPs, and get them ready for the store submissions. All with the best developer and agent experience in mind.

Here is a demo video of the product: https://www.youtube.com/watch?v=R2rbr5OT9LI.

We have been working on MCP since April 2025. Our first focus was making it easy to build agents that could use any MCP server, and a lot of people started using our SDKs. Then the harness revolution kicked off: Claude Code, Claude Cowork, ChatGPT, Codex, OpenCode started shipping agent harnesses that made most standalone agent frameworks redundant. That pushed us to the other side of the connection, the servers. If agents were going to consolidate into a few harnesses, then first-class integration with the rest of a company's systems (i.e. MCP) would become the thing that mattered, so we started building up our server SDKs.

Then in succession:

1. Oct 2025. ChatGPT Apps SDK. OpenAI brings app UIs to ChatGPT, built on top of MCP and the work of mcp-ui. 2. Late 2025. The stores open. ChatGPT starts accepting app submissions, Claude grows its connector directory with selected partners. 3. Jan 2026. MCP Apps becomes official. SEP-1865 merges as the first MCP extension (io.modelcontextprotocol/ui): one UI standard any host can render.

Today, all the major clients fully support MCP and are opening marketplaces of reviewed MCPs that can be one click installed. All major tech companies have an MCP server, and many of those are reporting that already 15+% of their usage comes from their MCP, and we start to have a good way to distribute them just now.

MCP can return fully interactive UIs. So companies can (1) display data in more meaningful ways to their users (e.g. analytics, ecommerce) and (2) display their branding in some of the most used products on the planet (ChatGPT, Claude etc). Numbers: an engineer at Amplitude reported that their MCP saw a 2x increase in retention after adding UI to their MCP.

Clients (Claude, ChatGPT, Cursor) are starting to dynamically present MCP servers/apps to users, based on their intent. Products will be organically discovered on the chats!

We feel that MCP is reaching its maturity moment. Now that MCPs are starting to be easy to install and discover, there is going to be a huge incentive for users to use them and for companies to create them:

1 - Most work is already done from AI chats, this is not going to stop, MCP gives you a way to interact with products without manually using their dashboards.

2 - MCP allows you to bring the context together in one place: you can read an email, create a ticket while plugged into the source code of your product, or your knowledge base. Aggregation of products that was not possible before, will happen in the chat, orchestrated by increasingly intelligent models.

If AI apps (Codex, Claude Desktop) are the new browsers, as PG said in a recent tweet https://x.com/paulg/status/2069080429236191504, then MCPs are the new websites.

But there is a catch:

- Submission process on the stores is still quite tricky, manual and takes up valuable time. - Hardly anybody knows how to design a good MCP: most of them are 1:1 proxies of the API and are abandoned, since being one shotted a few months ago. - The MCP Spec advances quickly and it is not easy to keep track of the changes, and what they mean for your server. - Auth is still a mystery for most teams (API key in the URL ???). - Most companies are not even aware that MCPs can return interactive UIs. - Clients still have to consolidate behavior, some do dynamic tool discovery, some don't, some persist authentication properly some don't.

We built Manufact and mcp-use to solve these problems. Our SDKs help them build good MCPs, our inspector helps them test locally, and our cloud helps them ship/publish and monitor them in production.

To deploy on Manufact you just need to connect a Github app, pick the repo, we'll detect the framework you are working with and get you a live MCP url as soon as possible.

In our platform, that live URL will be used to give you a chat where you can try/debug your MCP immediately and share it with your team. If you push an update on a new experimental branch, you'll be able to test that as well thanks to preview deployments.

Once your server is ready to go live, we help you make sure that it does not break. You can configure automated tests that will take your MCP server, install it in ChatGPT and Claude and test it. We do not test the model, we test the client (model + harness). This way you reliably know if your server breaks where people use it.

Since publishing on the store is a major distribution unlock for companies (your MCP can be dynamically discovered and one click installed across Claude products, and ChatGPT), we collected a set of requirements that will keep your submission from being rejected. You check this locally before going through the actual review process.

Once your server is live, you'll want to understand how it is used. Our analytics are designed for MCP, so you'll know how many users are hitting your MCP, how many tool calls you receive, from which client.

You can try out https://manufact.com for free today. We have usage-based pricing and on our free account we give free credits for you to try it out. If you have an MCP already, just connect your Github repo and deploy, if not you can build one using our skill and SDKs pretty simply (we will guide you in the onboarding).

We would love to hear feedback about the product in the comments, and hear thoughts from everyone about MCP. Thanks! :)

user:	keynha
created:	July 19, 2021
karma:	8
about:	I build and write about AI systems, data infrastructure, and the practical tradeoffs behind modern software engineering. https://github.com/KayhanB21

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