Apify

To reduce token usage when scraping social media data with Apify, use a code-first TypeScript API that filters actor results in your execution environment before returning context to the model. This prevents unfiltered data from consuming context window limits.

MCP workflows consume excessive tokens during web scraping because they force models to process large, unfiltered payloads. Replacing token-heavy MCP calls with in-code filtering ensures only targeted data is passed to the model.

Yes, you can filter e-commerce scraping data before it reaches your model by wrapping Apify actors in TypeScript code. This approach intercepts and filters data locally, ensuring only relevant results enter the model context.

You need a TypeScript execution environment to run this skill, as it provides direct code-first access to Apify actors. No external dependencies are required, but runtime safeguards are embedded to ensure safe execution.

The best way to collect business data without overloading model context is using a code-first Apify API that applies local filtering. This method wraps popular actors to return only filtered results, achieving significant token savings.