What it is
One connector to 400+ structured sources: LinkedIn companies, people and posts, Capterra, G2, Reddit, YouTube, Companies House, SEC, job boards, maps. Five universal calls and a cache cover what used to take a scraper per site.
Lists are cheap. A directory hands you a thousand company names in an hour. The expensive part comes after: real headcount, real HQ, what the company actually does, who works there, what its customers said on the way out of a competitor. Anysite fills that in, and a row of names turns into something you can segment.
The comparison everyone actually makes is against the enrichment platforms, so here's mine. The way I read Clay's pricing, you are paying a markup on data it buys from somebody else, and the markup is roughly an order of magnitude. Apollo's usable plans start around $150 a month and land at $350 to $400 in practice, with credits that cover one or two thousand enriched contacts at most. Pulling the same fields yourself comes out ten to twenty times cheaper. My working anchor is about $10 for an average base of a thousand contacts, and LinkedIn is the expensive part of that number. I could be off on the exact figure. Not by much.
My workflow
Search broad, clean during enrichment. Precision at the search stage is wasted effort. Search filters read the text on a company's website, and websites oversell. I pull wide and cheap, then let the structured data do the qualifying.
On one run, 10–20% of companies "from the UK" turned out to be headquartered in the US, India or Turkey. Enrichment caught it. No search filter could have. Headcount is worse. On a base of 1,339 companies, 755 came back with a spread above 30% between what the website claims, what LinkedIn reports and what the search engine guessed. I keep all three in separate columns now and let the disagreement stay visible.
Same preference in the research work. Structured fields, not summaries. A review with "switched from" as its own column is battlecard input the moment it lands.
Pull once, slice forever. A paid pull lands in a cache that lives seven days. Every question against that cache is free, so I pull wide and ask afterwards.
Setup
- 1Start the Anysite trial
- 2Connect the MCP to your agent
- 3Send your first data request
Connect the MCP in your agent's connectors menu and authorize. After that, "Connect to Anysite and enrich these 40 companies with real headcount and HQ" is a sentence you type. For thousand-row batches and scheduled pipelines, hand the agent an API key and let it work through the API instead.
Cost: the MCP is a flat plan, entry tier around $30/month, with $99 and $199 tiers above it. The REST API is a separate line. Credit plans for it start at $49/month, and requests run roughly $2.90 per 1,000 on top of that. For scale: pulling top posts on a keyword costs about 20 credits a post, so roughly two cents.
Top 3 use cases
- 01
Enrich a raw company list into something you can segment
Here's my list of [companies / people]. Connect to Anysite and enrich every row with: [real headcount · HQ and office locations · what the company does · founding year · the people in [role]]. Rules: pull in batches once, then work from the cached result. A fresh pull costs money, slicing the cache is free, and it lives seven days. Where a field can't be found, leave it empty and say so; never fill a gap with a guess. Where sources disagree on a number, keep all versions in separate columns and name which one you used. When done: the enriched table, fill rate per column, and how many rows changed segment once the real data landed. Don't report anything you can't point to in an actual result from this session.
- 02
Map a market the way buyers actually see it
Alternatives first, then pain, in customers' own words.
Connect to Anysite and map the market for [category]. Start with alternatives: pull "best tools for [category]" posts on LinkedIn and rank every product they mention. Mentions come back as a structured field, so one broad ask surfaces the 10–28 names practitioners actually weigh against each other, next to the handful of logos on our positioning slide. Then the pain: pull Capterra and G2 reviews for the top names, keeping "switched from" and "why" as their own columns, and add Reddit threads where [buyer role] complains about [the problem]. Cluster the findings into three to five themes with two verbatim quotes each. Quotes stay word for word, with links; paraphrase doesn't count. If a cluster reads like five identical praise comments from different accounts, treat it as paid seeding and skip it. Close with one page: the market as buyers describe it, the gaps competitors' pages skip, and the two themes our positioning should own. Every claim points to a source from this session.
- 03
Watch competitors without opening a single tab
Set it once as a scheduled task and Monday arrives already briefed.
Connect to Anysite and watch [3–5 competitors]: bring me a digest every Monday. Four feeds per company: what they published on LinkedIn and which posts got real engagement; which roles they opened, because hiring is the leakiest signal of where a company is headed next quarter; what changed on their pricing page since the last check; and new reviews, with "switched from" mentions flagged in both directions. Report deltas: what changed since last Monday, one link per line. Where nothing changed, one line saying so. Set this up as a scheduled task, so Monday happens without me.
Gotchas & limits
- The $30 MCP plan is not an API plan. It connects your agent and it does not hand you a REST key. Credit plans for the API start at $49. That gap is the single most common surprise on the pricing page, and I've watched a developer hit it two minutes into an evaluation.
- Discover, then execute, then work the cache. Re-asking the source a question you already have the answer to is the fastest way to spend a balance on nothing.
- Email fields aren't validated at source. Anything you plan to send to goes through a verifier first.
- Enrichment is only as good as the match key. Deduplicate your list before enriching (collapse by domain and by normalized name), or you'll pay to enrich the same company five times under five domains.
- JavaScript-heavy pages are where the generic web parser gives up. On a modern app-style site it can return a success-shaped result with nothing inside. I've had it hand back a site's own error page as a "result." Have the agent show you the first ten rows before anything gets built on top.