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Create an llms.txt file at the root of my documentation site.
Place it at: [domain]/llms.txt

Structure it exactly as follows:
Line 1: # [Product Name]
Line 2: Blank line
Line 3: One paragraph summary of what the product does and who it's for.
Line 4: Blank line
Line 5: ## Technical Constraints
- Rate limits
- Authentication requirements
- SDK language support
- Known limitations
Line 6: Blank line
Line 7: ## Key Documentation
- [URL]: [one-line description] (repeat for top 10 most important pages)

My docs are at: [URL]
My product does: [brief description]

Create an llms-full.txt file at the root of my documentation site.
This should contain the entire documentation merged into a single Markdown file.

My documentation is returning excessive HTML to AI agents.
Implement server-side content negotiation in my app.

When a request includes the header 'Accept: text/markdown',
return ONLY the technical content as clean Markdown.

Strip completely: navigation menus, footers, cookie banners,
sidebar links, breadcrumbs, social sharing buttons,
interactive widgets, and any HTML not containing technical content.

The response should contain only:
- Page title as H1
- Section headings as H2/H3
- Body text
- Code blocks with language tags
- Parameter tables
- No HTML tags, no CSS, no scripts

Reduce layout boilerplate (header, footers, scripts) or use Markdown Content Negotiation to strip DOM noise.

No code blocks found to analyze.

No code blocks found to analyze.

Expose an OpenAPI or Swagger specification at a standard path (e.g. /openapi.json) or link to it using the "Link" HTTP response header with rel="openapi".

Embed a <script type="application/ld+json"> block classifying your docs as a TechArticle, Guide, or APIReference, containing semantically enriched metadata.

Configure and expose an MCP server definition at /.well-known/mcp.json or publish guidelines showing AI agents how to interact with your APIs using MCP tool declarations.

Provide a comprehensive table or section listing error codes, their causes, and solutions. This lets agents cross-reference error stacks and resolve integration errors automatically.