Developer rewrites 14 web scrapers after AI agent silently returned wrong results

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A developer discovered that connecting his 14 Apify web-scraping Actors to Claude via the Model Context Protocol (MCP) exposed a critical design flaw in late July 2025. While the scrapers worked perfectly when operated manually, AI agents calling them would receive empty datasets or silently incorrect results because the input schemas assumed human context, such as having the target website open nearby. Unlike human users who can troubleshoot and retry, an AI agent has a single shot to interpret inputs, execute a run, and branch on the output, meaning ambiguous results led it confidently down the wrong path. The most costly example involved a software registry Actor that required an internal opaque integer ID only obtainable by inspecting the site's markup, causing agents passing readable class codes to get clean but empty — or worse, plausibly wrong — results. The developer subsequently refactored all 14 Actors to resolve site-specific lookups internally in code, ensuring any caller without prior site knowledge could still produce correct, distinguishable outcomes.

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