Cloudbeds MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Cloudbeds as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Cloudbeds. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Cloudbeds?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Cloudbeds MCP Server
Connect your Cloudbeds property to any AI agent and run your hotel from a single conversation.
LlamaIndex agents combine Cloudbeds tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Reservations — Browse, filter by status, and drill into booking details
- Guests — Search profiles, view stay history and lifetime value
- Rooms & Housekeeping — Real-time room status and cleaning priorities
- Availability — Check open rooms for any date range instantly
- Transactions — Track charges, payments, and guest balances
- Dashboard — Today's KPIs: occupancy, revenue, ADR, check-ins/outs
The Cloudbeds MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Cloudbeds to LlamaIndex via MCP
Follow these steps to integrate the Cloudbeds MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Cloudbeds
Why Use LlamaIndex with the Cloudbeds MCP Server
LlamaIndex provides unique advantages when paired with Cloudbeds through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Cloudbeds tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Cloudbeds tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Cloudbeds, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Cloudbeds tools were called, what data was returned, and how it influenced the final answer
Cloudbeds + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Cloudbeds MCP Server delivers measurable value.
Hybrid search: combine Cloudbeds real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Cloudbeds to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Cloudbeds for fresh data
Analytical workflows: chain Cloudbeds queries with LlamaIndex's data connectors to build multi-source analytical reports
Cloudbeds MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Cloudbeds to LlamaIndex via MCP:
check_availability
Essential for booking inquiries and revenue management. Check room availability
get_dashboard
The GM's morning briefing. Get property dashboard
get_guest
Get guest profile
get_housekeeping
For housekeeping management. Get housekeeping status
get_reservation
Get reservation details
list_reservations
Filter by status: confirmed, checked_in, checked_out, cancelled. Core front-desk tool. List hotel reservations
list_room_types
With max occupancy, amenities, base rate, and room count. List room types
list_rooms
List hotel rooms
list_transactions
Filter by reservation to see a guest's complete financial history. List financial transactions
search_guests
Returns profile, contact, nationality, past stays, preferences, and lifetime value. Search hotel guests
Example Prompts for Cloudbeds in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Cloudbeds immediately.
"What's our occupancy and revenue for today?"
"List dirty rooms pending turnover for the afternoon layout."
"Find the ongoing reservation of Mr. Anderson."
Troubleshooting Cloudbeds MCP Server with LlamaIndex
Common issues when connecting Cloudbeds to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCloudbeds + LlamaIndex FAQ
Common questions about integrating Cloudbeds MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Cloudbeds with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Cloudbeds to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.