ZooData
The data layer for AI agents
833 followers
The data layer for AI agents
833 followers
ZooData turns any URL into agent-ready JSON, so AI agents can work with structured data instead of raw HTML or bloated markdown. Use ~75% fewer LLM tokens, pay only for the fields you use, and skip extra extraction credits.Beyond extraction, ZooData gives agents pre-analyzed e-commerce intelligence — competitor, market, traffic, and consumer insights — live for Amazon and TikTok. API, CLI, and MCP server included. Start with 1,000 free credits, no card required.
Dealing with bloated markdown has always been annoying when building agents. Having an MCP server included from the start is genuinely useful.
ZooData
@filizutou Thanks! To us these are two halves of the same problem: feeding agents markdown treats them like human readers, and hand-wiring HTTP integrations treats them like human developers. So we made both ends agent-native — typed JSON fields on the data side, MCP tools out of the box on the interface side. Agents shouldn't read webpages, and they shouldn't read API docs either.
The fact that it comes with a CLI and API ready to go is super helpful for developers trying to integrate this quickly.
ZooData
@fatmasavluhstl Thanks! Developer hours belong in your own product, not in "getting data in" — one key, one call, structured fields back; and for agent workflows you can skip integration code entirely via MCP. Any snags while trying it, shout!
Any tool that helps agents work with structured data instead of messy web scrapes is a win. The included CLI is a nice touch for those of us who prefer working from the terminal.
ZooData
@bayramblrn Thanks! The "win" comes down to this: text is something an agent has to read and guess at, fields are something it can act on — and every wrong guess is a hallucination or a retry. Our entire job is deleting the guessing from your workflow: URL in, named and typed fields out. Verify it from your terminal on real pages — and find us here or at support@zoodata.ai if anything's off.
The silent leak is the one that got us. What worked was hashing the sorted key set we hydrate against and storing that hash next to the extracted row, so when the shape changes the hash moves and something fails loudly instead of quietly gaining a field nobody reads. Cheap to add, and it turned a class of invisible drift into a diff I could actually look at.
ZooData
@dipankar_sarkar "Turn invisible drift into a diff" — that's exactly the right goal, and the sorted-key-set hash is a nice lightweight way to get there on the consumer side.
We attack the same problem from the producer side: every response field belongs to a typed contract, and the pipeline keeps an explicit registry of upstream keys we've either adopted or deliberately excluded. So when a source starts emitting a new field, it can't quietly slip into the response — it surfaces as a decision someone has to make, and our tests fail if the column list and the mapper ever drift apart. Same philosophy as yours: a shape change should be an event, not a surprise you find three weeks later in a field nobody reads.
Honestly might steal the hash trick for our own eval harness 😄 Thanks for sharing the war story.
NexaSDK for Mobile
Been testing this for a few weeks with my research agent.
The token savings are real: product pages that used to eat half the context window now come back as compact JSON. My LLM bill noticed before I did. Congrats on the launch! 🎉
ZooData
@power_valsha Weeks of real usage before a comment — that's the feedback that actually means something. Thank you. The context-window savings compounding into real bill savings is exactly the problem we set out to kill. Would love to hear what you're building with it.
Mom Clock
Hey Product Hunt! 👋
I am very happy to hunt ZooData today.
If you’ve spent any time building AI agents, you already know the massive pain point they are solving: raw HTML and heavy markdown burn through LLM tokens like crazy, and maintaining custom scrapers is a constant headache.
Personally, what impressed me is how effortlessly it turns any URL into clean, structured JSON—saving up to 75% on tokens. It’s built specifically for the agent era (with an MCP server right out of the box), meaning you only pay for the data fields you actually use instead of bloated prose.
Huge congrats to Ning and the team on the launch! Please give them your support if you find the product useful, test out their 1,000 free credits, and drop your feedback in the comments below!
ZooData
@justin2025 Thanks for the amazing hunt! 🙌
You nailed it — agents don't need prose, they need fields. Any URL in, clean typed JSON out, plus an MCP server out of the box so Claude/Cursor can pull live Amazon & TikTok Shop data as native tools.
Grab the 1,000 free credits and tell us what breaks — I'll be in the comments all day!
CapCut AI Suite
Congrats on the launch!
Clean JSON by default is such an obvious idea in hindsight. I can't believe I've been feeding my agents markdown soup this whole time 😅
ZooData
@lavana_cricko Haha, that's literally how this started — we got fed up feeding our own agents markdown soup 😅 The loop was always: fetch → clean → write parsers → page changes → everything breaks. The realization: agents never wanted "text that reads like a webpage," they wanted fields they can act on. That's ZooData. Treat your agents to the 1,000 free credits — they deserve better than soup!