ZooData

@Kyle Dong that three-lane breakdown (retryable / don't-retry / content-ok-but-degraded) is actually clearer than most APIs give you even without the granular attribution. makes sense you'd keep the fine-grained block signals internal rather than publish a map of exactly what trips detection. appreciate the detailed answer

Totally fair not to fake a null you can't stand behind. My pain sat one level below the semantics: `price` showing up in one response and vanishing from the next meant every field access in our agent needed a guard, since parsing code leans on a stable shape. If each page type always emitted its full key set with the value simply absent, that alone fixes the shape, and the sourced-vs-not flag you floated with Jernej could carry the did-we-check honesty on top. Does the fixed-per-page-type schema you mentioned to hazy already give me that stable key set, or can fields still drop out per call?

The pay-per-field pricing model is genuinely clever, it means agents only burn credits on what they actually need instead of paying for entire HTML dumps. Nice execution on the MCP server support too.

the auto-escalate-to-full-model-extraction when core fields fail validation is a smart fallback. if a site is mid A/B rollout and flips between old/new layout per request, does that thrash the escalation path back and forth before the new template locks in, or is there a cooldown so you're not eating full-model cost on every call during the rollout window?

the daily-cycle vs no-cache split for analytics vs realtime endpoints is smart, most tools pretend everything's live. does JS-heavy SPA rendering get priced the same as static pages or does that heavier compute cost more per call?

neat framing, agent-ready JSON instead of raw markdown. when a site restructures its page layout, does the schema auto-remap to catch the drift or does it silently break until someone notices the fields are empty?

The 75% token reduction claim makes sense structurally but I'm curious how it holds on pages where the most valuable data is buried in dynamic elements that only render after JavaScript executes , things like live pricing widgets, inventory counters, or seller ratings that load asynchronously. Is the JSON output pulling from the fully rendered DOM or the initial HTML response, because for e-commerce intelligence that difference is where the real data quality gap tends to sit.

Structured input is a better foundation for agents than making the model repeatedly interpret noisy pages. How do you preserve context that doesn’t fit neatly into the extracted schema but might become important to the agent later?

@Kyle Dong that's the right answer honestly, "we don't disguise a block as a success" is the thing that actually matters, most tools I've tried just eat the failure silently and you don't find out until your data looks wrong three steps downstream. does the response include which failure mode it hit, like captcha vs rate limit vs structure change, or is it just a generic fail code?

Great launch! The token math is the part that really matters, at least for agents. Paying to filter boilerplate out of 'clean' markdown is a tax you don't price in until the bill shows up! Wondering - when a site quietly ships a layout change or runs an A/B test, does that learned template keep mapping to the old fields and hand back a confidently wrong value?

curious how this holds up against anti-bot layers - a lot of the messiest sites to scrape aren't messy because of markup, they're messy because they actively try to block automated requests. does the per-field pricing still apply if a site just serves you a captcha wall instead of html, or is that a separate failure case

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.

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.

Paying only for the fields used makes a lot of sense for data extraction.

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.

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.

The fact that it comes with a CLI and API ready to go is super helpful for developers trying to integrate this quickly.

is this restricted to English? I've tried two .ro websites and got nothing back.

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:npx skills add SerendipityOneInc/ZooData-Skillsand you're off.Would love your feedback. And I'm curious — what's the messiest site you've ever had to scrape? 🙏