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.
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:
npx skills add SerendipityOneInc/ZooData-Skills
and you're off.
Would love your feedback. And I'm curious — what's the messiest site you've ever had to scrape? 🙏
About ZooData on Product Hunt
“The data layer for AI agents”
ZooData launched on Product Hunt on July 18th, 2026 and earned 621 upvotes and 84 comments, earning #1 Product of the Day. 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.
On the analytics side, ZooData competes within Developer Tools, Artificial Intelligence and E-Commerce — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how ZooData performed against the three products that launched closest to it on the same day.
Who hunted ZooData?
ZooData was hunted by Justin Jincaid. 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.
For a complete overview of ZooData including community comment highlights and product details, visit the product overview.
Justin Jincaid
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? 🙏