WebScraping.AI

@WebScraping.AI fetch the plain text from https://example.com/pricing so we can compare their tier structure to ours

Retrieved 847 words of plain text from the pricing page. The content includes three subscription tiers (Starter at $29/mo, Professional at $99/mo, Enterprise custom pricing), each with bullet-pointed feature lists. Text extraction stripped navigation, footers, and promotional banners, leaving only the core pricing table and feature descriptions. This format is ready for direct comparison or LLM analysis without HTML noise.

This example uses the Get Text tool, which strips all markup and returns only readable content. Ideal for feeding pricing pages, blog posts, or documentation into analysis prompts. Note that heavily JavaScript-dependent sites may require the rendered HTML endpoint instead—check the page source first if results seem incomplete.

@WebScraping.AI get the fully rendered HTML from https://dashboard.competitor.com/stats including all JavaScript-loaded charts

Fetched 12.3 KB of rendered HTML after executing client-side JavaScript for approximately 3 seconds. The response includes dynamically loaded chart data (previously hidden in the initial page load) and React-rendered dashboard widgets. All interactive elements are now present in the static HTML snapshot, though event handlers and scripts themselves are inert. This output can be parsed for data extraction or visual layout analysis.

This example demonstrates the Get Rendered HTML tool, which waits for JavaScript execution before returning content. Essential for single-page apps or dashboards where data loads asynchronously. Be aware this consumes more API quota per request than raw HTML fetching, and the 3-second render wait is a default—complex pages may need custom timeout settings.

@WebScraping.AI check our account usage so I know if we're close to hitting this month's quota before running a batch scrape

Account status retrieved: 8,420 of 10,000 monthly API calls used (84% consumed). Quota resets in 9 days on April 1st. Current subscription tier is Professional, which includes JavaScript rendering and proxy rotation. Based on remaining quota, you have approximately 1,580 requests available—enough for a moderate batch job, but large-scale scraping should wait until next billing cycle to avoid throttling.

This example uses the Get account usage and quota tool to surface rate-limit awareness before expensive operations. Critical for teams running automated scraping workflows—hitting quota mid-job can break pipelines. Always check usage before batch operations, and note that rendered HTML requests count the same as standard fetches despite higher computational cost.

When WebScraping.AI beats manual price checks for a 3-person team

A three-person e-commerce team needs to track competitor pricing on 20 SKUs twice a week. WebScraping.AI's Get Rendered HTML tool handles JS-heavy product pages that curl can't parse, and the quota check tool keeps the team from burning through their API limit mid-sprint. The win is speed: what took 90 minutes of manual screenshots now runs in 8 minutes via a shared Switchy prompt. The threshold: if you're scraping more than 200 pages per week, you'll hit quota constraints on the starter tier and need to budget for overages. If your competitors use aggressive bot detection, this MCP will fail and you'll need residential proxies. For small-batch competitive intel where time matters more than cost, this is the call.

Using plain-text scraping to build internal help docs from vendor sites

A five-person support team at a SaaS reseller needs to pull FAQ content from eight vendor sites into a shared knowledge base. WebScraping.AI's Get Text tool strips navigation and ads, returning only the prose they need to paste into Notion. The API key setup takes 10 minutes, and the team shares one Switchy workspace to run extractions on demand. The trade-off: this MCP doesn't handle authentication, so if the vendor docs are gated behind login, you're stuck. It also won't parse structured data like tables or lists into JSON—you get raw text and have to format it yourself. If your vendors publish open docs and you need quick text grabs without writing scrapers, this works. If you need structured output or auth, look elsewhere.

When this MCP is overkill for a content team's research workflow

A two-person content team wants to pull intro paragraphs from 15 industry blogs every Monday for a newsletter roundup. WebScraping.AI can do it, but the Retrieve HTML Content tool returns full page markup, and the team still has to parse out the excerpt manually. The quota check is useful for staying under the free tier, but the workflow is clunky: they're paying for JS rendering they don't need, and the output requires post-processing that eats 20 minutes per run. The honest call: if the blogs have RSS feeds, use an RSS reader. If they don't and you need JS rendering for paywalled previews, this MCP justifies the setup. For straightforward HTML scraping where speed isn't critical, a simpler tool or manual copy-paste is faster.