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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 server

Layers provides a Model Context Protocol (MCP) server that allows AI assistants to interact with the documentation and services. The MCP server enables:
  • Real-time access to documentation
  • Structured queries about Layers features
  • Integration with MCP-compatible AI tools
To connect to the MCP server, configure your AI tool with the Layers MCP endpoint https://docs.uselayers.com/mcp. Refer to your AI tool’s documentation for specific connection instructions.

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Use cases

These AI enablement features support various workflows:
  • Documentation search: AI agents can quickly find relevant information across the docs
  • Code generation: LLMs can reference accurate API documentation when generating integration code
  • Automated support: Build chatbots that answer questions using up-to-date Layers documentation
  • Development tools: Integrate Layers documentation into your IDE or development workflow

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Next steps