This product was not featured by Product Hunt yet.
It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Eli BarakMost MCP servers just wrap someone else's API, read-only. Elliot builds the whole app: your own data, real writes, and per-user sign-in so each person sees only their rows. Describe it to your coding agent and get a live MCP URL. Engine is open source.
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About Elliot Cloud on Product Hunt
“MCP ”
Elliot Cloud was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #109 on the daily leaderboard. Most MCP servers just wrap someone else's API, read-only. Elliot builds the whole app: your own data, real writes, and per-user sign-in so each person sees only their rows. Describe it to your coding agent and get a live MCP URL. Engine is open source.
Elliot Cloud was featured in Developer Tools (517.3k followers), Artificial Intelligence (475.6k followers) and Vibe coding (608 followers) on Product Hunt. Together, these topics include over 192.9k products, making this a competitive space to launch in.
Who hunted Elliot Cloud?
Elliot Cloud was hunted by Eli Barak. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Hi Product Hunt! I've built a lot of MCP servers, and I kept hitting the same wall: most of them are thin wrappers around someone else's API. Read-only, token-hungry, errors agents can't recover from, and zero visibility into what the agent actually did with your tools.
So I built one platform that bakes in the best practices I kept re-implementing by hand: tool descriptions that read like contracts, results sized for context windows, errors that tell the agent what to do next, and observability on every call. Token cost, latency, error rate, and which tools agents struggle with, per tool.
Then I took it further. Agents don't just need better wrappers, they need real apps: their own data, write access, and per-user accounts. My wife and I wanted an AI assistant that could run our family paperwork, forms, renewals, deadlines. I described the app to my coding agent, Elliot built it, and now we each sign in through Claude and ChatGPT We use it every day.
Building agentic apps this early is also how I learn how agents actually behave with real tools, and that feedback goes straight back into the platform.
This is an experimental MVP
The engine is fully open source. Ask me anything!