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AI access and scraping

How to feed RentalTide docs to AI assistants, scrape them programmatically, and use the per-page AI tools

AI access and scraping

We have intentionally made the RentalTide docs friendly for AI assistants and for anyone scraping them programmatically. If you are using Claude, ChatGPT, Perplexity, Cursor, or any other tool that benefits from large amounts of context, this page is for you.


Per-page tools

Every page on this site has two buttons immediately below the page title:

  • Copy as Markdown — copies the raw Markdown source of the current page to your clipboard. Paste it into any AI assistant to give it the full context of the page.
  • Open in AI — opens the current page in Claude, ChatGPT, or Perplexity with the page content already in the prompt — no web-browsing tool required. For short pages the full content is inlined into the chat input. For longer pages, the content is copied to your clipboard and the assistant is prompted to ask you to paste it. Also exposes a "View raw Markdown" option that opens the source .md file in a new tab.

These buttons are designed to make it trivial to bring a docs page into whatever AI workflow you already use, even when the assistant cannot fetch URLs.


Raw Markdown URLs

Every page on the site is available as raw Markdown by prefixing the path with /raw and appending .md:

Rendered pageRaw Markdown
https://docs.rentaltide.com/getting-started/caching/https://docs.rentaltide.com/raw/getting-started/caching.md
https://docs.rentaltide.com/fleet/inventory/https://docs.rentaltide.com/raw/fleet/inventory.md
https://docs.rentaltide.com/api-reference/bookings/https://docs.rentaltide.com/raw/api-reference/bookings.md

The raw files are plain UTF-8 Markdown with YAML frontmatter at the top. They are regenerated on every build, so they always match what is on the rendered page.


llms.txt and llms-full.txt

We follow the llms.txt convention for AI-friendly site indexing.

  • llms.txt — a structured site index with links to every section of the docs, plus key concept summaries and pricing. Intended as a small, high-signal entry point that an LLM can read in one pass.
  • llms-full.txt — a single file containing the concatenated Markdown of every page on the site. Use this if you want to feed the entire docs corpus to an LLM in one shot. The file is approximately 800 KB and is regenerated on every build.

Both files are linked from our robots.txt using the Llms-txt and Llms-full-txt extensions.


Crawler policy

We welcome AI crawlers and large language model indexers. The following user agents are explicitly allowed in our robots.txt:

  • GPTBot, ChatGPT-User, OAI-SearchBot (OpenAI)
  • ClaudeBot, Claude-Web, anthropic-ai (Anthropic)
  • PerplexityBot, Perplexity-User (Perplexity)
  • Google-Extended (Google AI training)
  • Applebot-Extended (Apple Intelligence)
  • CCBot (Common Crawl)
  • Bytespider (ByteDance)
  • cohere-ai (Cohere)
  • Meta-ExternalAgent, Meta-ExternalFetcher (Meta)
  • DuckAssistBot (DuckDuckGo)
  • YouBot (You.com)
  • Diffbot

If you operate an AI crawler not on this list and you want to index our docs, you are welcome to do so under the default User-agent: * allow rule.


Sitemap

A complete sitemap.xml lists every page on the site. Use it to discover all available URLs programmatically.


Asking the docs AI

The docs site itself has a built-in AI assistant. Click the "Ask AI" button in the top navigation, type your question, and it will answer using the full docs corpus as context. Useful when you want a quick answer without leaving the page.

If the assistant ever gets something wrong, please use the thumbs-down button on its response — those reports go straight to our team and help improve the bot.


Programmatic API access

For programmatic access to RentalTide data itself (not just docs), see the API reference. Every endpoint is documented with request and response examples that LLMs can use directly to generate working integration code.

If you have feedback on what would make the docs more useful for AI-assisted workflows, reach out to support — we will keep iterating.

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