How to Use the Cloudbeds MCP in Google ADK

Q: How do I restrict specific Cloudbeds functions in Google ADK?

You can use the toolnames filter within the McpToolset definition to expose only the functions your agent requires. This keeps your agent's scope tight and secure.

Bring Cloudbeds operations into your Google ADK pipelines for enterprise-grade hotel management.

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Connect Cloudbeds MCP to Google ADK

Create your Vinkius account to connect Cloudbeds to Google ADK 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 Google ADK

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Cloudbeds integration for Google ADK agents

Your Google ADK agents gain the ability to query reservation status and financial history using `list_reservations` and `list_transactions`. This lets you build agents that audit guest balances or track revenue trends automatically. The server connects to your agent framework as a native toolset. You define the endpoint in your McpToolset, and your Gemini-powered agent gains direct access to your hotel data.

Sync room housekeeping with Google ADK

Your agents can monitor property maintenance by calling `get_housekeeping`. It retrieves the current status of every room, allowing your agent to flag issues or update staff schedules based on the output. This workflow allows your Google Cloud infrastructure to act on live room data. The agent processes the response and decides the next step based on your predefined logic.

Query Cloudbeds guest profiles using Google ADK

Search through your entire guest database with `search_guests` to identify VIPs or past stay patterns. The agent retrieves profile details like lifetime value and contact info, providing immediate context for your support team. Your agent uses these tools to build a better guest experience. By integrating this server, you keep your data flow consistent across your Google-based agent architecture.

Setup guide

Set up Cloudbeds MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Cloudbeds tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Cloudbeds_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Cloudbeds tools via MCP.",
    tools=mcp_tools,
)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Cloudbeds. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Cloudbeds MCP in Google ADK

Absolutely, your agent can pull reservation data from Cloudbeds and join it with your BigQuery datasets. This allows for complex analysis of your hotel's performance.

It supports both Streamable HTTP and Stdio, making it easy to deploy on Google Cloud runtimes. You just configure the McpToolset to point at your Vinkius endpoint.

You can use the tool_names filter within the McpToolset definition to expose only the functions your agent requires. This keeps your agent's scope tight and secure.

Gemini models can process the large JSON payloads returned by tools like `list_reservations` without issue. This is ideal for agents that need to summarize thousands of bookings.

Your agent consumes the guest profile and transaction data in memory. The connection is encrypted and ephemeral, meaning no Cloudbeds records persist on the host server.

Use it with your favorite AI tools

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