# Do I have to manually format scraped data for AI workflows?

### Web Scraper API

Only if you want to. Model Context Protocol (MCP) standardizes scraped data in LLM-ready format.

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

Configurable parameters allow you to tailor Web Scraper API outputs to specific AI model requirements with minimal setup changes.

#### Key benefits of MCP <a href="#h_be1adf2f0e" id="h_be1adf2f0e"></a>

* Consistency: ensure reliable input to AI systems with standardized formatting.
* Scalability: easily handle large-scale web scraping for LLMs.
* Cost efficiency: reduce engineering costs for maintaining data pipelines.

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 specific steps to enable MCP for your projects.

You can also use a [Markdown output](/products/web-scraper-api/features/result-processing-and-storage/output-types/markdown-output.md) feature. This provides a clean, structured data of web content that is highly optimized and easy-to-read for LLMs. To enable it, include the `"markdown": true` parameter in your request.

### AI Studio

[AI Studio](https://aistudio.oxylabs.io/) delivers results in LLM-optimized Markdown format. If needed, it can automatically parse results based on your specifications: define your needs through natural language prompts or provide Pydantic/JSON schemas.


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