How to Use the Beds24 MCP in LangChain

Run multi-step booking pipelines with LangChain and Beds24 to automate your hospitality operations.

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Connect Beds24 MCP to LangChain

Create your Vinkius account to connect Beds24 to LangChain 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 Beds24 with LangChain

Ask AI about this MCP

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Claude

Perplexity

Verify Room Availability in Your Chains

This Beds24 MCP Server lets your agent inspect real-time occupancy. By calling `get_availability` and `get_calendar`, your chain gets the exact status of any room across your properties without manual lookups. You can feed this live data directly into subsequent chain steps. If a guest asks for a weekend stay, the agent checks the calendar first, then routes the output to draft an immediate booking offer.

Manage Booking Workflows with LangSmith

Keep tabs on every guest reservation with `list_bookings` and `get_booking`. Your agent pulls active reservations, matches guest details, and updates your system of record in a single run. Because every tool call runs through this MCP setup, you can trace latency and token usage inside LangSmith. You'll see exactly when the agent calls the server to pull booking details and how it formats the response.

Audit Properties and Rooms via Beds24 MCP Server

Pull complete structural data using `list_properties`, `list_rooms`, and `get_property`. Your pipelines can map out your entire portfolio, checking which rooms are active and matching them against external databases. Combining these tools with other LangChain integrations makes it easy to sync your property data with external platforms. Your agent decides which endpoint to hit based on the property ID, keeping your records accurate.

Setup guide

Set up Beds24 MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Beds24 tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "beds24-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Beds24 transactions"
    })
    print(result["messages"][-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Beds24. 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.

Why Choose Vinkius

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Common questions about Beds24 MCP in LangChain

Install the adapter package and use the MultiServerMCPClient to pull the tools. Pass the resulting tool list to your agent creator, and you are ready to query your properties.

Yes, you can mix these tools with any of the hundreds of integrations available. Your agent can pull booking info with `get_booking` and then write it directly to your SQL database.

The framework catches tool execution errors and passes them back to the LLM. If `get_availability` fails due to a bad date format, the agent can correct its input and try again.

Yes, LangSmith traces every single call to the server. You can monitor the exact input payloads and output data for tools like `list_rooms` to optimize your prompt spending.

Your account credentials and guest booking details stay isolated within the Vinkius sandboxed environment. We use ephemeral execution environments, meaning your API keys are never exposed to the LLM or stored in plaintext.

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