How to Use the Cloudbeds MCP in AutoGen

Debate hotel management decisions in AutoGen using Cloudbeds tools to reach consensus.

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Connect Cloudbeds MCP to AutoGen

Create your Vinkius account to connect Cloudbeds to AutoGen 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 AutoGen

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Perplexity

Multi-agent reservation negotiation

Let your agents debate reservation conflicts using `list_reservations`. One agent argues for overbooking while another prioritizes guest satisfaction based on `search_guests` profiles. They reach a consensus before executing any change. This removes the guesswork from managing high-demand dates.

Automated housekeeping coordination

Assign one agent to monitor `get_housekeeping` while another manages room assignments. They discuss the workflow to optimize turnover times between guest stays. The agents challenge each other's schedules to find the most efficient path. You define the goals, and they negotiate the execution.

Financial status auditing

Use `list_transactions` to let your security agent verify revenue logs. It debates with the operations agent to confirm guest billing is accurate. This ensures that no transaction goes unchecked. The agents converge on a final report that accounts for every dollar.

Setup guide

Set up Cloudbeds MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Cloudbeds tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Cloudbeds_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Cloudbeds data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Cloudbeds_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Cloudbeds data")
    print(result.messages[-1].content)

Get your connection token →

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Common questions about Cloudbeds MCP in AutoGen

You pass the tool adapter to your agents during initialization. The framework automatically converts the server schema so your agents can call tools during their conversations.

Yes, agents communicate their findings to each other. If one agent retrieves a guest profile, the other agents can use that information in their debate.

It supports both stdio and Streamable HTTP. You pick the transport that fits your local or remote agent deployment.

There is no limit. You can give the tool set to as many agents as you need to build your negotiation pipeline.

The server implements a zero-trust architecture. Agents only receive the data they request for a specific task, limiting exposure of guest profile information.

Use it with your favorite AI tools

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