You can use large language models (LLMs) to assist in building integrations with the Cloudbeds Developers platform. We provide tools and best practices if you use LLMs during development.

Documentation Index

Fetch the complete documentation index at:

https://developers.cloudbeds.com/llms.txt

Use this file to discover all available documentation pages before exploring further.

The /llms.txt file provides a machine-readable index of the Cloudbeds Developers documentation. AI agents can use this file to locate documentation pages and retrieve their markdown versions.

This follows the emerging llms.txt standard for making websites and documentation easier for AI agents to consume.

Plain Text Docs

You can access Cloudbeds Developers documentation as plain text markdown files by adding .md to the end of most documentation URLs.

Plain text documentation is designed to be easily consumed by AI tools and agents.

Advantages:

Fewer formatting tokens
Full page content, including content hidden in UI components
Clear hierarchy through markdown headings
Easy copy/paste into prompts or AI assistants

AI agents can combine .md documentation pages with the /llms.txt index to efficiently explore the entire Cloudbeds Developers documentation set.

Model Context Protocol (MCP) Server

The Cloudbeds Developers Model Context Protocol (MCP) server connects AI agents directly to Cloudbeds APIs and documentation.

AI agents can retrieve documentation, generate integration code, and guide developers through building Cloudbeds integrations — with more accurate results and fewer hallucinations from outdated information.

What is MCP?

Model Context Protocol (MCP) is an open standard that allows AI agents to securely access external data sources and tools.

With the Cloudbeds Developers MCP server, AI agents can:

Discover APIs across Cloudbeds Developers endpoints
Search documentation across Cloudbeds Developers docs
Generate integration code and provide guidance
Offer context-aware assistance when building integrations

Tools like Cursor, Windsurf, Claude, and VS Code can then act as Cloudbeds-aware AI agents during development.

Connect Your AI Tools

Here are some ways you can connect to the Cloudbeds Developers MCP server.

Navigate to the Connectors page in the Claude settings.
Click Add custom connector.
Enter the MCP server name (for example cloudbeds-developers) and URL:

https://developers.cloudbeds.com/mcp

Select Add.
When using Claude, click the attachments button (the + icon).
Click your MCP server.

Testing Your MCP Setup

Once configured, test your MCP connection:

Open your AI editor (Cursor, Windsurf, Claude Desktop, etc.)
Start a new chat session.
Ask questions such as:

"How do I create a reservation using the Cloudbeds API?"
"Show me a Node.js example for retrieving property information."
"Generate an integration example for syncing reservations."

If configured correctly, the AI assistant will retrieve documentation using the Cloudbeds Developers MCP server.

Build with LLMs