MCP Company Researcher — One Domain, One JSON Record

Feed a company domain, get back a single JSON record with website meta, tech-stack markers, DNS, SSL, Google News, and Hacker News mentions. Built for Claude/GPT lead-qualification MCP pipelines that need a fast read on a target without setting up six different APIs.

No login, no API key, no quota — uses public endpoints only.

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What this actor actually does (verified against src/main.js)

For a single input domain (e.g. stripe.com), the actor calls 5 public sources and returns one record to the dataset:

#	Source	What is fetched
1	The website itself (https://<domain>)	<title>, meta description, og:title, og:description, og:image
2	Same HTML body (regex match)	Tech-stack markers — exactly 7 patterns checked
3	Google DNS-over-HTTPS (dns.google/resolve)	A-record IPs only
4	TLS handshake on port 443	Certificate issuer, validTo date, isValid flag
5	Google News RSS (news.google.com/rss/search)	Up to 5 latest items: { title, source }
6	Hacker News (Algolia API)	Up to 5 stories matching the domain: { title, points, url }

That's the entire pipeline. ~10–15 sec per domain on a normal run.

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Input

{ "domain": "stripe.com" }

Field	Type	Required	Notes
domain	string	yes	Protocol stripped automatically. https://stripe.com/pricing is normalized to stripe.com.

One domain per run. No batch input array, no CSV upload — if you need 100 domains, run the actor 100 times (Apify SDK / apify.call()) or fork and add a loop.

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Output (one record per run)

{
  "domain": "stripe.com",
  "website": {
    "title": "Stripe | Financial Infrastructure for the Internet",
    "description": "Millions of companies of all sizes use Stripe...",
    "ogTitle": "Stripe | Financial Infrastructure for the Internet",
    "ogDescription": "...",
    "ogImage": "https://images.stripe.com/og-image.png"
  },
  "techStack": ["Next.js", "Stripe", "Google Analytics"],
  "dns": { "ips": ["3.18.12.63", "3.130.192.231"] },
  "ssl": {
    "issuer": "DigiCert Inc",
    "validTo": "Aug 25 23:59:59 2026 GMT",
    "isValid": true
  },
  "recentNews": [
    { "title": "Stripe announces…", "source": "TechCrunch" }
  ],
  "hackerNewsMentions": [
    { "title": "Stripe is hiring…", "points": 312, "url": "https://stripe.com/jobs/..." }
  ],
  "scrapedAt": "2026-04-29T13:30:00.000Z"
}

Tech-stack detection — exactly these 7 regexes

Pattern (substring in HTML body)	Tag emitted
wp-content	WordPress
_next/	Next.js
cdn.shopify	Shopify
googletagmanager	Google Analytics
js.stripe	Stripe
hubspot	HubSpot
intercom	Intercom

If a site uses anything else (Webflow, Squarespace, Cloudflare, Segment, Mixpanel, Amplitude, Sentry…) — it will not appear here. This is a markers list, not a full Wappalyzer fingerprint database.

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What this actor does NOT do (be honest before you buy)

• ❌ No company financials. No revenue, no funding rounds, no Crunchbase/PitchBook lookup.
• ❌ No employee count / LinkedIn data. No headcount, no key-people enrichment.
• ❌ No social-media stats. No Twitter/X followers, no LinkedIn-company size.
• ❌ No WHOIS. Domain registration date / registrant / expiry are NOT fetched (despite what older marketing copy of the actor implied — code only does DNS A-records).
• ❌ No phone/email/address scraping. Contact data is not extracted from the website.
• ❌ No batch input. One run = one domain.
• ❌ No retry/backoff. If Google News or HN fails, the field is returned as [] and the run continues silently.
• ❌ No screenshots, no PDF export, no language detection.
• ❌ No dedup / no cache. Re-running on the same domain will re-fetch everything.

If you need any of the above — that's a custom build (see pricing below).

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Sample MCP/agent use case

# Pseudocode — Claude/GPT agent qualifying a fresh inbound lead
from apify_client import ApifyClient

client = ApifyClient(token=os.getenv("APIFY_TOKEN"))
run = client.actor("knotless_cadence/mcp-company-researcher").call(
    run_input={"domain": "acme-prospect.com"}
)
data = list(client.dataset(run["defaultDatasetId"]).iterate_items())[0]

# Now feed `data` into your LLM as context for: lead score, talk-track, news-hooks

Use it as a fast first-pass enrichment step in an agentic pipeline — not as a replacement for proper sales-intelligence platforms.

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Proof of delivery

23 lifetime runs on this actor — but the broader portfolio is what backs every pilot:

• 31 published / 78 total Apify scrapers across socials, B2B, dev tools.
• Flagship: Trustpilot Review Scraper — 951 lifetime runs, 0 bot-detection failures across 30 days.
• Recent paid series: $150 / 3-article postmortem for a client in the proxy industry (March 2026, delivered).
• Code-honest READMEs: every claim in this readme is verified against src/. No "supports X" without proof.

Pilot pricing locked through May 2026:

• 1 case-study article (1100w+, code blocks): $50
• 3-article series: $150
• Custom build (this actor → your variant: deeper tech-stack regex packs, social-link extraction, lead enrichment from email signatures): from $50 depending on schema delta.

Reply sample to spinov001@gmail.com — get 2 published case-study articles within 24h. No commitment.

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Need more than the public-source defaults?

This actor is a small, focused tool. If you need a custom company-research pipeline that pulls Crunchbase / Apollo / LinkedIn / Twitter / SEC filings / employee count / funding rounds / competitive overlap — that's a build, not an off-the-shelf actor.

Custom builds (we deliver):

Tier	What you get	Price
Pilot	1 actor extending this base (e.g. add 1 paid data source), basic config, 7-day support	$97
Standard	Custom multi-source actor + Slack/email alerts on results, 30-day support	$297
Premium	Custom actor + dashboard + 90-day support + 1 modification round	$797

We've shipped 31 published actors on Apify (78 in total portfolio), including:

• trustpilot-review-scraper — 951 runs in 30d, used by data teams for review monitoring
• reddit-discussion-scraper — Reddit JSON-API scraper at scale
• email-extractor-pro — bulk website email harvester (no Hunter cap)

Contact / sample work:

• Email: spinov001@gmail.com
• Blog & case studies: https://blog.spinov.online
• All actors: https://apify.com/knotless_cadence
• Telegram (scraping/AI tips): https://t.me/scraping_ai

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License

MIT — fork it, modify it, ship it.