How to Use the Amenitiz MCP in LangChain

Q: Can a LangChain agent manage booking modifications using Amenitiz?

Yes, the agent uses getreservation to pull the active booking state and verify details. It can then chain this with rate checks to confirm if a modification matches your current house rules.

Run hotel operations by chaining Amenitiz PMS tools directly into your LangChain multi-step reasoning pipelines.

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Connect Amenitiz MCP to LangChain

Create your Vinkius account to connect Amenitiz 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 Amenitiz with LangChain

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Chain Amenitiz PMS updates into LangChain pipelines

The `list_reservations` tool pulls booking records directly into your active run context via this MCP Server, establishing a clean data stream. Your agent immediately feeds these results to `get_reservation` to dig into specific guest requirements without manual intervention. Because LangChain handles multi-step reasoning, your pipeline evaluates room assignments automatically. It matches the booking details with current availability before passing the finalized payload to your internal notifications.

Track real-time room rates using this MCP Server

The `get_rates` tool fetches live pricing structures directly from your property management system to feed your pricing agents. Your LangChain agent can instantly compare these rates with current occupancy trends pulled via `get_availability`. You get full observability through LangSmith tracing to monitor exactly when and why your agent queries property data. This stops runaway API loops before they drive up your token usage or trigger rate limits.

Automate guest lookup and room assignments

The `list_guests` tool retrieves your customer history database to match incoming booking requests against existing profiles. Your LangChain agent uses this data to verify returning guests and flag VIP status before assigning rooms. Once verified, the agent executes `list_room_types` to find the exact room class requested by the customer. The entire sequence executes in a single, observable execution chain using the MCP standard, leaving a clear trace of every decision.

Setup guide

Set up Amenitiz 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 Amenitiz 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({
    "amenitiz-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 Amenitiz transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Amenitiz MCP in LangChain

Install the required adapter package and pass the Vinkius MCP transport URL to your client. You then fetch the tools list and pass them directly to your agent constructor to start querying booking data.

Yes, the agent uses `get_reservation` to pull the active booking state and verify details. It can then chain this with rate checks to confirm if a modification matches your current house rules.

You should configure your LangChain runnable with retry logic to handle potential API limits. The agent will gracefully back off and retry the `get_rates` tool without failing the entire run.

Yes, you can load multiple servers side-by-side in your client. This allows your agent to pull room details from this MCP Server and combine them with external weather or flight APIs in a single decision loop.

Your guest database and reservation details never touch external storage. The server runs inside an ephemeral, zero-trust V8 sandbox that processes guest names and booking IDs in memory, destroying the session immediately after the tool completes.

Use it with your favorite AI tools

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