How many properties use Cloudbeds?

Cloudbeds serves 20,000+ properties in 150+ countries — hotels, hostels, vacation rentals, B&Bs, and hotel groups. It's the most widely used all-in-one hospitality platform.

Can I use the AI to manage the cleaning schedule?

Yes! The MCP communicates directly with the Cloudbeds Housekeeping module. You can query dirty rooms, assign staff, and update statuses like 'Inspected' conversationally.

Is the dashboard data delayed?

No, it pulls strictly real-time numbers natively routed from the Cloudbeds API covering exact metrics on ADR, current Occupancy, and live Check-in progress.

Cloudbeds MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Cloudbeds
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Cloudbeds through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Cloudbeds "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Cloudbeds?"
    )
    print(result.data)

asyncio.run(main())

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* 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.

Pydantic AI validates every Cloudbeds tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the Cloudbeds MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

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 with type-safe schemas

Why Use Pydantic AI with the Cloudbeds MCP Server

Pydantic AI provides unique advantages when paired with Cloudbeds through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Cloudbeds integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Cloudbeds connection logic from agent behavior for testable, maintainable code

Cloudbeds + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Cloudbeds MCP Server delivers measurable value.

Type-safe data pipelines: query Cloudbeds with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Cloudbeds tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Cloudbeds and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Cloudbeds responses and write comprehensive agent tests

Cloudbeds MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Cloudbeds to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

Common issues when connecting Cloudbeds to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Cloudbeds + Pydantic AI FAQ

Common questions about integrating Cloudbeds MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Cloudbeds MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.