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Layers provides multiple ways for AI agents and tools to access documentation and integrate with the platform.

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llms.txt

You can access the documentation in a structured, AI-friendly format at /llms.txt. This file provides a comprehensive overview of the platform and API in a format optimized for large language models. 
const response = await fetch('https://docs.layerci.com/llms.txt');
const content = await response.text();

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Markdown and text URLs

Append .md or .txt to any documentation URL to retrieve the page content in plain text format. This makes it easy to programmatically access and process the documentation. 
// Access any page as markdown
const response = await fetch('https://docs.layerci.com/introduction.md');
const markdown = await response.text();

// Or as plain text
const textResponse = await fetch('https://docs.layerci.com/introduction.txt');
const text = await textResponse.text();
This works for all documentation pages, including:
  • API reference pages
  • Platform guides
  • Integration documentation

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MCP servers

Layers provides two MCP servers that let AI assistants interact directly with your stores:
  • Layers MCP Server — Full store management with 37 tools for catalog, merchandising, search tuning, metrics, and more. Authenticates via OAuth.
  • Storefront MCP Server — Read-only storefront operations for search, browse, and recommendations. Authenticates with your storefront API token.
See the MCP servers guide for connection instructions, available tools, and usage examples.

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Documentation MCP

You can also access Layers documentation through MCP at https://docs.uselayers.com/mcp. This endpoint provides structured access to the docs for AI tools that need to look up feature details or API reference information.

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Use cases

These AI enablement features support various workflows:
  • Store management: Use AI assistants to create merchandising rules, tune search behavior, build sort orders, and analyze metrics through MCP
  • Storefront integration: Build AI-powered product discovery into customer-facing experiences
  • Documentation search: AI agents can quickly find relevant information across the docs
  • Code generation: LLMs can reference accurate API documentation when generating integration code
  • Development tools: Integrate Layers documentation into your IDE or development workflow

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Next steps