I built 8 pay-per-use scraping APIs that AI agents can call directly (Google Maps, TikTok, Instagram, YouTube, LinkedIn) — here's what I learned

#apify #webscraping #mcp #ai

A few weeks ago I published a set of Actors on Apify Store. Today they're all AI-agent ready: any LLM agent (Claude, GPT, Cursor, LangChain, n8n) can discover and call them through the Apify MCP server — no custom integration code needed.

This post is the full playbook: what the tools do, how the pay-per-event monetization works, the bugs I hit, and how AI agents actually consume them.

The toolbox

Actor	What it extracts	Price (pay-per-event)
Google Maps Business Scraper	Names, phones, websites, ratings, reviews, GPS	$0.005 / business
TikTok Profile & Video Scraper	Followers, likes, bio, per-video stats	$0.01 / profile
Instagram Profile Scraper	Followers, bio, verified, engagement	$0.01 / profile
YouTube Video & Channel Scraper	Views, likes, subscribers, search results	$0.002 / video
LinkedIn Profile Scraper	Headlines, companies, skills, experience	$0.02 / profile
RAG Web Browser	Clean Markdown from any URL + Google search	$0.003 / page
Fuel Prices France API	Real-time prices, 9,800 stations, GPS	FREE
Hotel Rate Monitoring	Competitor rates, parity checks	FREE

The two free ones are deliberate lead magnets — more on that below.

Why "AI-agent ready" changes everything

The old model: a human finds your scraper on the store, reads the docs, clicks buttons.

The new model: an AI agent gets a task ("find me 50 plumbers in Austin with their phone numbers"), searches the Apify Store via MCP, reads the actor's README and input schema, and calls it — end to end, no human.

For that to work, three things must be true:

Your README is written for an LLM, not just humans. Mine now all start with a "Use this tool when..." section — that's what the agent pattern-matches against the user's request.
Your input schema has a description on every field. The agent constructs the JSON input from those descriptions. No description = hallucinated parameters = failed runs = no revenue.
Your output is documented field by field. The agent needs to know what it gets back to reason over it.

Here's the actual flow with the Apify MCP server (https://mcp.apify.com — add it to Claude Desktop or Cursor in 30 seconds):

User: "Get me the follower counts of these 5 TikTok creators"
Agent: → search-actors("tiktok profile")
       → fetch-actor-details (reads README + input schema)
       → call-actor(travelmonitorlab/tiktok-scraper,
                    {"profiles": [...], "maxVideosPerProfile": 0})
       → returns structured JSON

Or skip MCP entirely — every actor is a single synchronous HTTP call:

curl -X POST "https://api.apify.com/v2/acts/travelmonitorlab~google-maps-scraper/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"queries": ["plumbers Austin TX"], "maxResults": 50}'

Monetization: pay-per-event (and the trap that cost me hours)

Apify offers several pricing models. For new actors, PRICE_PER_DATASET_ITEM is rejected — you must use PAY_PER_EVENT. The model is better anyway: you define events (e.g. business-scraped at $0.005) and charge explicitly in code:

await Actor.charge(event_name="business-scraped")
await Actor.push_data(item)

The trap: put the charge AFTER crawler.run() and your event loop may already be closed — the charge silently vanishes, you deliver data for free. Charge inside the handler, right before pushing data. Always verify with chargedEventCounts in the run object after a test run.

Setting pricing is pure API:

PUT /v2/acts/{actorId}
{"pricingInfos": [{
  "pricingModel": "PAY_PER_EVENT",
  "reasonForChange": "Launch pricing",
  "pricingPerEvent": {"actorChargeEvents": {
    "business-scraped": {
      "eventTitle": "Business scraped",
      "eventDescription": "One Google Maps business record",
      "eventPriceUsd": 0.005
    }}}}
]}

Apify takes 20%. Compute is paid by the user; you pocket the event fees.

Battle scars (so you don't get them)

Crawlee 1.8 breaking changes: purge_on_start and navigation_timeout_secs are no longer valid kwargs — use page.set_default_navigation_timeout() in a pre_navigation_hook.
Google Maps never fires load: analytics keep streaming forever, so navigation always times out. Fix: abort images/fonts/media via page.route() (the handler must be a coroutine, not a lambda).
Residential proxies are mandatory for Google Maps, and you must pass actor_proxy_input= as a named argument to Actor.create_proxy_configuration().
French number formats will crash your floats: "4,8" → replace comma; "1 234" reviews can use \xa0 or \u202f as thousand separator.
A "SUCCEEDED" run can contain zero useful data. Always check itemCount + sample the dataset + read the end of the log.
Don't run 7 queries × 25 results in one run. Split into parallel runs of ≤5 queries × 15 results; retry failed ones sequentially (residential proxy tunnels occasionally die).

The distribution strategy

Publishing on the store is step 0. What actually moves the needle:

Free lead magnets — the fuel-price and hotel-rate actors are 100% free. Free tools get users, ratings, and store ranking; ranked actors surface in MCP search results; MCP visibility drives paying users to the paid actors.
README written for LLMs — agents choose tools whose docs they can parse. Clear "use when", typed inputs, example I/O.
Niche SEO titles — "Google Maps Scraper" is saturated; "Fuel Prices France API" has zero competition on the store.
Dogfooding — I use my own Google Maps actor to build lead lists I sell elsewhere. Every sale is also a demo.

Try them

All 8 actors are live on Apify Store. If you build agents, add https://mcp.apify.com to your MCP client and just ask for the data — the agent will find the tools.

Feedback, bugs, feature requests: open an issue on any actor page, I answer fast.