# How does Model Context Protocol (MCP) standardize data for LLMs?

The [Model Context Protocol (MCP)](/products/web-scraper-api/solutions-for-ai-workflows/model-context-protocol-mcp.md) optimizes LLM data delivery by structuring information hierarchically, employing metadata tagging and semantic chunking to reduce redundancy while preserving contextual relationships.

The protocol improves retrieval efficiency by up to 45% over standard prompting methods, enabling more effective parameter allocation during inference.

🛠️ MCP is a built-in feature of [Web Scraper API](https://oxylabs.io/products/scraper-api/web) 🛠️

MCP enables Web Scraper API to deliver structured and context-rich HTML data directly to your model, eliminating manual preformatting and improving LLM output accuracy.

#### How it works <a href="#h_fe5f95459b" id="h_fe5f95459b"></a>

* Web Scraper API automatically generates MCP-compliant outputs.
* In the output, you can tailor metadata, instructions, and disclaimers to suit specific needs.
* If you are a Web Scraper API user, you can use the API as-is or opt-in for MCP with minimal adjustments.

🧠 See our [documentation](/products/web-scraper-api/solutions-for-ai-workflows/model-context-protocol-mcp.md) and [GitHub](https://github.com/oxylabs/oxylabs-mcp) for setup scenarios.


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