Connect a web scraper to an MCP AI workflow using open-source tools using CyberYozh Data

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Turn any website into structured, MCP-ready data—automatically.

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CyberYozh Data

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CyberYozh Data is an open-source crawler and scraper that maps full websites, extracts structured JSON, and integrates natively with MCP-driven AI workflows for resilient, proxy-aware web data collection.

mcp crawlerstealth scraperllm-assisted parsingweb scrapingdata extractiondeveloper toolsai agentsopen source

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What to expect from an ideal product

1. CyberYozh Data crawls entire websites and outputs clean JSON, so your MCP workflow gets structured data without manual parsing or cleanup steps.
2. Built-in proxy rotation keeps your scraper running even when target sites block requests, reducing failed jobs and data gaps in production pipelines.
3. Because CyberYozh Data is open-source, you can audit the code, self-host it, and plug it directly into your existing MCP AI stack without vendor lock-in.
4. The crawler maps full site architecture before extracting content, which means your AI agent receives complete, context-rich data rather than isolated page snippets.
5. Teams replacing brittle custom scrapers with CyberYozh Data cut setup time and get MCP-ready JSON feeds that update automatically as source websites change.

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