Launching today
ZooData
The data layer for AI agents
399 followers
The data layer for AI agents
399 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.
ZooClaw
Hi PH 👋,
I'm Ning from ZooData.
Quick context on why we built this.
If you've built anything with agents, you know the data problem. You scrape a page — with browser-use, Playwright, whatever — and what comes back is raw HTML or "clean" markdown. Either way it's stuffed with nav bars, footers, ads, and boilerplate. For a human reading it, fine. For an LLM, you're burning thousands of tokens on stuff the model has to filter out before it can do anything useful. At scale that's real money, and most of it is waste.
Markdown is the usual fix. But markdown was built for humans to read, not for an agent that has to act on the data. Different reader, different format — an agent doesn't need prose, it needs structure.
ZooData does the extraction step right:
Any URL → structured JSON. No schema to define, no per-site parsers, no selector glue to maintain.
~75% fewer tokens than raw markdown on the same page — roughly 1/5 the cost of other extractors. And you only pay for the fields you actually use; the extraction itself doesn't burn credits.
API, CLI, and MCP server, so it drops into your agent stack without rewriting anything.
Pre-analyzed e-commerce platform intelligence — competitor, market, traffic, and consumer signals your agent can query directly, instead of scraping and stitching it together itself. More platforms coming.
We believe the next bottleneck for AI agents won't be how smart the models get — it will be the quality of the data they rely on.
As AI-generated content floods the web, agents need data that's clean, structured, and verifiable to make reliable decisions. That's the layer we're building, and it compounds: every page we process makes the next request cheaper, faster, and more trustworthy.
ZooData is the foundation the rest of it runs on — we launched ZooClaw (agents for individuals) here not long ago, and ZooWork (the enterprise version) is coming soon.
1,000 free credits, no card. Just tell your agent:
and you're off.
Would love your feedback. And I'm curious — what's the messiest site you've ever had to scrape? 🙏
The data-quality point is the right one, most agent tools skip straight past it to the model.
One thing I'd add from the support side, where we build agents. Clean JSON fixes the token cost, but it quietly adds a different risk. Raw HTML looks messy so you distrust it. A structured field looks authoritative even when the extractor grabbed the wrong element, or the value went stale between the scrape and the moment the agent acts on it. For an agent that only reads, fine. For one that acts on the field, answers a customer or changes a price, a confidently wrong value is worse than a missing one, because nothing tells it to stop.
So the question, does ZooData give the agent anything per field, a confidence score or a freshness timestamp, or is it flat JSON it has to trust fully? On live Amazon/TikTok data I'd expect the real failures to sit there, not in the extraction.
ZooData
@jernej_jan_kocica Good question — and you've split it into the two failures that actually matter: extraction correctness (did we grab the right element) and freshness (has it gone stale before the agent acts). Let me take both honestly.
Confidence (extraction side): no, we don't expose a per-field confidence score today — it's on the roadmap. And your framing is exactly the spec for it: for an acting agent, "a confidently wrong value with nothing telling it to stop" is the failure worth engineering against. A missing value fails safe; a wrong one doesn't. That's the signal we want to give the agent.
Freshness (the side you'd bet the real failures sit on): this is the part we've actually designed hard around, so I can be concrete —
scrape/realtime endpoints force a live fetch on every call. No cache, ever. There's no gap between capture and read — the value is fresh at the moment the agent asks.
analytics/commerce endpoints run on a daily cycle, so every value is explicitly time-bounded rather than pretending to be real-time — the agent knows exactly how old it is.
So interestingly, the live Amazon/TikTok data you flagged as the danger zone is the part we deliberately don't cache — the open gap you've correctly found is extraction-confidence, not staleness. Since you build support agents that act on fields, I'd genuinely like to design that confidence signal with input from your seat rather than guess at it. Happy to swap notes.
Really glad to see an MCP server included from day one. It shows a deep understanding of current developer workflows and makes integrating this JSON extraction much more appealing.
ZooData
@glsm11117961978 Thanks! One design detail worth sharing: our MCP tools and REST API are generated from the same codebase — every endpoint's schema, description, and validation share a single source, so the MCP tools never drift out of sync with the API. The server side is also fully stateless, so any MCP client stays reliable at any scale. If anything feels rough in your integration, tell us — we'll fix it!
Paying only for the fields used makes a lot of sense for data extraction.
ZooData
@ilan1017353 Thanks! You've hit the spirit of it — pay for signal, not bloat. Concretely: pricing is per call, and each call returns clean, typed JSON with only the meaningful fields (empty ones are dropped). A product lookup costs the same whether the page behind it is 50KB or 5MB — the savings show up downstream, where your agent's context gets fields instead of boilerplate.
ZooData
@grrigore Thanks for flagging — it's not English-only by design; the open-web extraction is language-agnostic. One thing worth knowing: when we encounter a brand-new page type, the first extraction takes extra time (the system first works out the structure for that type of page — after that, similar pages are fast). Please give it another try in a bit; if you still get nothing back, send us the two URLs (here or at support@zoodata.ai) and we'll confirm what's going on — and report back.
@kyle_dong Yes, it works now. It might be helpful to share that explanation with the user as well. Is there a way to automatically scrape "outgoing_links"?
ZooData
@grrigore Great question! Two cases:
① Entity-style pages — outgoing links are already included by default. If the page is about one specific named thing (think a HuggingFace model page, an IMDB movie page, a Goodreads book page — one subject, structured facts around it), the extracted JSON already carries the page's outgoing links, no extra parameters needed.
② Other page types — a dedicated endpoint exists, just not public yet. We've built a dedicated outgoing-links capability internally but haven't opened it up. So: try the default output on your actual pages first. If it covers you, great; if it doesn't, tell us your page types and use case — your scenario is exactly the push we need to release it. Find us here or at support@zoodata.ai.
Getting clean JSON straight from a URL is literally a lifesaver for agent workflows. Skipping the raw HTML mess will save so much time and token costs.
ZooData
@irmakb5o5 Thanks! Everyone counts the token bill, but the "time" part you mentioned is the bigger one — every team that parses HTML themselves ends up babysitting a pile of selectors and cleanup scripts that shatter on every redesign. Outsourcing the "URL → fields" step saves more than tokens; it retires an entire maintenance pipeline. That's exactly the part we want to carry for you. 🙌
Skipping extra extraction credits and just paying for what you use is how all these tools should work. The included CLI makes it very easy to test the data formatting locally.
ZooData
@elifeakekep6oh Thanks! That's a deliberate choice: structured JSON is the default output, not a paid add-on. The way we see it, structure is the product — charging extra for clean fields would be billing twice for the core value. One call, one price, structure included.