How to Use the Cloudbeds MCP in LlamaIndex

Q: Does LlamaIndex support filtering Cloudbeds results?

You can use allowedtools filters to restrict which reservation or guest data enters your index. This keeps your RAG application focused on specific operations.

Index your hotel operations in LlamaIndex by grounding your knowledge base in live Cloudbeds data.

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Connect Cloudbeds MCP to LlamaIndex

Create your Vinkius account to connect Cloudbeds to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Cloudbeds with LlamaIndex

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Vectorize your property data

Use `list_room_types` to build a searchable index of your inventory. LlamaIndex stores this data so your agents can answer occupancy questions based on current records. This prevents the agent from hallucinating room counts or amenities. It queries the actual API state to ground every response.

Sync reservations to knowledge bases

Run `list_reservations` and pipe the results into your vector store. Your agent retrieves past reservation patterns to find trends in your booking data. This turns your static hotel data into a queryable asset. You connect historical guest stays to your current RAG pipeline.

Deep search housekeeping status

Index the output of `get_housekeeping` to track room readiness across your property. Your agents query the index to see which rooms are clean and ready for guests. This data is combined with your internal documents for a unified view. You get answers based on the live state of your property management system.

Setup guide

Set up Cloudbeds MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Cloudbeds MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Cloudbeds tools.",
)
response = await agent.run("List recent Cloudbeds data")

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Common questions about Cloudbeds MCP in LlamaIndex

Configure your tool spec to include resources and pass the output to your agent. The framework then handles the ingestion of API data into your vector store.

You can use allowed_tools filters to restrict which reservation or guest data enters your index. This keeps your RAG application focused on specific operations.

The MCP Server fetches live data every time your agent triggers a tool. You decide the frequency of index updates in your own pipeline logic.

Yes, you can mix API-derived data with your static documents in one unified index. This provides your agents with a complete picture of your operations.

We enforce strict read-only access for financial history tools. Your indexed data contains only the tokens you authorize, ensuring sensitive transactional details remain ephemeral.

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