How to Use the Mews MCP in LangChain

Run multi-step hotel operations chains by connecting your LangChain agents directly to Mews PMS data.

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Connect Mews MCP to LangChain

Create your Vinkius account to connect Mews 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 Mews with LangChain

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Claude

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Chain reservation lookups with LangChain agents

The `get_reservations` tool pulls live booking windows directly into your LangChain decision chains. We feed these booking records into your next chain link so your LangChain agent can instantly calculate property occupancy or flag late checkouts. By passing these live Mews records into LangChain runnables, your agent decides whether to pull guest histories or check room statuses next. You get complete execution transparency through LangSmith tracing to watch how your pipeline handles complex booking scenarios.

Build multi-step Mews billing audits with LangChain

The `list_bills` tool retrieves complete financial histories for any guest profile inside your LangChain workflow. Your LangChain agent uses this output to feed a downstream verification chain that compares outstanding balances against processed payments. This setup lets your LangChain agent run autonomous billing audits by matching `list_payments` data with active Mews guest accounts. You don't have to write custom loop logic because the LangChain framework handles the sequential tool calling natively.

Automate guest profiling using this MCP Server

The `search_guests` tool finds matching profiles, contact details, and past stay preferences to power your LangChain guest relations pipelines. Your LangChain agent grabs these customer files to instantly personalize check-in sequences or update room service preferences. Connecting this Mews MCP Server to your LangChain setup means your agent can cross-reference guest profiles with active room blocks in one run. It turns static Mews records into active context for your custom LangChain customer service chains.

Setup guide

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

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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 Mews. 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 Mews MCP in LangChain

You don't need to manage API keys inside your LangChain code. The Vinkius platform handles the Mews connection securely, leaving you with a single endpoint token to pass into your MultiServerMCPClient setup.

Yes, your LangChain agent uses ReAct loops to determine the best tool order. It might call get_reservations first, check room availability with list_rooms, and then pull guest details using get_guest to resolve a booking conflict.

LangSmith logs every single tool call, payload, and response latency from the Mews MCP Server. You can inspect exactly what data list_payments returned and see how your LangChain agent parsed that payload.

Absolutely. LangChain lets you mix these hotel tools with external databases or communication APIs in the same execution chain.

Yes, all billing histories, guest profiles, and transaction records remain safe inside the Vinkius V8 Isolate Sandbox. The server operates in an ephemeral environment, meaning no guest data is stored or cached after your LangChain run finishes.

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