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Haunt
Web extraction for AI agents, pay only for working calls
Haunt is an agent-native web extraction API. Send a URL plus a plain-English prompt, get clean structured JSON or Markdown back. MCP-native (hosted endpoint plus an npm package) and REST. Failed extractions never burn credits: a blocked page returns a clear error and charges nothing; it never fabricates fields. Privacy-first, no page-content retention by default. Free tier: 1,000 credits a month, no card. When a page is genuinely blocked, it says so, instead of returning garbage.
Hey PH, Danny here, founder.
I built Haunt because every agent project I touched had the same glue: fetch, parse, wrangle a headless browser. And every scraping API billed me for the requests that failed.
Two things I'd love your take on:
1. It's MCP-native: a hosted streamable-HTTP endpoint plus a local npm package, so your agent just gets a "read this page, return JSON" tool it can call itself.
2. Failed extractions don't burn credits. A genuinely blocked page returns a clear error, charges zero, and never invents fields, so your agent branches on the failure instead of hallucinating.
Free 1,000 credits a month, no card, plus a no-key demo so you can see the output shape first.
Honest limits: it's not a CAPTCHA-solver or a bypass tool, and heavy app shells can still fail (it tells you when they do).
Would genuinely value feedback from anyone shipping agents.
About Haunt on Product Hunt
“Web extraction for AI agents, pay only for working calls”
Haunt was submitted on Product Hunt and earned 0 upvotes and 1 comments, earning #3 Product of the Day. Haunt is an agent-native web extraction API. Send a URL plus a plain-English prompt, get clean structured JSON or Markdown back. MCP-native (hosted endpoint plus an npm package) and REST. Failed extractions never burn credits: a blocked page returns a clear error and charges nothing; it never fabricates fields. Privacy-first, no page-content retention by default. Free tier: 1,000 credits a month, no card. When a page is genuinely blocked, it says so, instead of returning garbage.
On the analytics side, Haunt competes within Productivity, Developer Tools and Artificial Intelligence — topics that collectively have 1.6M followers on Product Hunt. The dashboard above tracks how Haunt performed against the three products that launched closest to it on the same day.
Who hunted Haunt?
Haunt was hunted by Daniel Cox. 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 Haunt including community comment highlights and product details, visit the product overview.
Daniel Cox