Apify

To reduce token usage when scraping social media data, use a code-first Apify API wrapper to fetch and filter results in-code before they reach the model context, dramatically cutting token overhead. This replaces token-heavy MCP calls with local dataset filtering.

The best way to scrape Amazon and Google Maps data without exhausting tokens is using a code-first Apify API wrapper that fetches results and filters to a small, high-signal subset in-code before presenting it to the model.

Yes, you can run Apify actors for Twitter and TikTok directly from TypeScript without MCP overhead. The code-first wrapper provides direct TypeScript access to popular actors, executes them, and iterates datasets locally.

Code-first Apify scraping works across Instagram, YouTube, LinkedIn, and Facebook. It provides a catalog of actor wrappers for social platforms, business data, e-commerce, and general web scraping to fetch and filter data locally.

Scraping through MCP consumes many tokens because raw fetched data enters the model context directly. Code-first Apify wrappers solve this by fetching data and applying in-code filtering to return only a high-signal subset, maximizing token efficiency.

To discover and execute an Apify actor, use the code-first wrapper to search the actor catalog, run the selected actor, fetch the dataset results, and apply in-code filtering to extract a small subset before returning it to the model.