For AI Tools (llms.txt)
LinkForty provides machine-readable documentation files designed for AI coding assistants. If you're using tools like Claude, ChatGPT, Cursor, Windsurf, GitHub Copilot, or any LLM-powered coding tool, these files give your AI everything it needs to integrate LinkForty into your project.
The LinkForty MCP server connects your AI assistant directly to your workspace with 20 tools for managing links, querying analytics, and more — no copy-pasting needed. The files below are for giving AI tools read-only reference material; the MCP server gives them live, authenticated access to your data.
Available Files
Both documentation files are generated from these docs on every build, in the same order as the sidebar, so they always match what you can read here. They are never edited by hand.
llms.txt — Quick Reference
A table of contents with a link to every documentation page and a one-line description of each.
URL: https://docs.linkforty.com/llms.txt
Best for: AI tools that can follow links, or when you want to point your assistant to a specific topic.
Size: ~15 KB
llms-full.txt — Complete Documentation
The full text of every documentation page in one file: getting started, every guide, the API reference, all SDK guides, the SDK specification, tutorials, and reference pages.
URL: https://docs.linkforty.com/llms-full.txt
Best for: Indexing into a retrieval system, adding to a project context file, or giving an agent that can search a large file everything at once. It is large; for a chat paste, pick the pages you need from llms.txt instead.
Size: ~750 KB
linkforty.com/llms.txt — Product Overview
The product site has its own file at https://linkforty.com/llms.txt. It answers product questions rather than integration ones: what LinkForty is, who it is for, pricing, the feature pages, free tools, and articles, with pointers back to the files above. A companion llms-full.txt on the same domain carries the full text of the feature pages and articles.
How to Use These Files
With Claude Code or Cursor
Add the full reference to your project context so the AI always has it available:
# Download to your project
curl -o LINKFORTY.md https://docs.linkforty.com/llms-full.txt
Then reference it in your .cursorrules, CLAUDE.md, or equivalent project instructions file:
When integrating LinkForty, refer to LINKFORTY.md for the complete API
and SDK reference.
With ChatGPT, Claude Chat, or Similar
Copy the contents of llms-full.txt and paste it into your conversation, then ask the AI to integrate LinkForty. For example:
Here is the LinkForty integration reference: [paste llms-full.txt]
I'm building a React Native app with Expo Router. Add LinkForty deep linking so that shared product links open the product screen.
With GitHub Copilot
Add the file to your repository so Copilot indexes it:
curl -o docs/LINKFORTY_REFERENCE.md https://docs.linkforty.com/llms-full.txt
In CI/CD or Automation
Fetch the latest reference programmatically:
curl -s https://docs.linkforty.com/llms-full.txt
What's Inside llms-full.txt
The file mirrors the documentation sidebar, section by section:
| Section | What It Contains |
|---|---|
| Getting Started | Why LinkForty, introduction, quick start, self-hosting, Cloud vs self-hosted, production checklist |
| Guides | Links (custom domains, templates, UTM, QR codes, shared links, social previews), mobile attribution, analytics (campaigns, events, app and web analytics, export), team, and advanced features (webhooks, integrations, email link tracking, MCP server, link safety) |
| API Reference | Authentication, rate limits, errors, and every documented endpoint with request and response examples |
| SDKs | React Native, Expo, iOS, Android, Flutter, the web pixel, and the SDK specification |
| Learn and Tutorials | Deep linking and attribution fundamentals, implementation guides |
| Reference | Glossary, environment variables, this page |
Each page starts with its source URL, so an assistant can cite where an answer came from.
About the llms.txt Standard
llms.txt is an emerging convention for providing LLM-friendly documentation, similar to how robots.txt guides web crawlers and sitemap.xml guides search engines. It places structured, AI-optimized content at a well-known URL so that AI tools and agents can discover and use it.