How to Use the Wine Pairing & Sommelier MCP in LangChain

Build multi-step sommelier pipelines with LangChain; your AI client handles complex Wine Pairing & Sommelier tasks.

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Connect Wine Pairing & Sommelier MCP to LangChain

Create your Vinkius account to connect Wine Pairing & Sommelier 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 Wine Pairing & Sommelier with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Find the perfect dish for any wine

You can build a chain that first identifies a specific wine type, then passes that output to `get_dish_for_wine`. This sequence tells your agent exactly what pairing recommendations are available. It's useful when you know the wine but need an idea of dinner.

Get expert pairings from a dish

Need a bottle for salmon? Start by calling `get_wine_pairing` with 'salmon'. The resulting structured data includes specific product names, ratings, and prices. You can then take the suggested wine name and run it through `recommend_wines` to check current stock or price fluctuations.

Research wines by type and product

The agent handles complex research flow when you ask about a wine's history. You can combine `get_wine_description` for background context, then use `recommend_wines` to pull current market data like ratings and prices. This creates a full report from one single chain execution.

Setup guide

Set up Wine Pairing & Sommelier 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 Wine Pairing & Sommelier 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({
    "wine-pairing-sommelier-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 Wine Pairing & Sommelier transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Wine Pairing & Sommelier MCP in LangChain

You set up the entire logic flow as a multi-step agent. For example, your first step might be calling `get_wine_pairing` based on user input (e.g., 'steak'). The output of that tool then becomes the necessary context for subsequent steps in the chain.

Yep. It handles it by letting your agent decide which tools to call and when. You're not limited to a single function; you can build a decision tree that guides the AI through finding the perfect match.

The primary data types are wine pairings (dish/ingredient), detailed wine descriptions, specific product names, associated ratings, and current prices. This allows your agent to give concrete advice.

The final output is a structured result compiled from multiple tool calls—it's not just one single answer. You get product suggestions with prices, and detailed pairing logic that shows how the decision was reached.

Absolutely. Because it allows you to trace every step in the process via LangSmith, you can debug exactly why a certain wine or dish pairing was recommended at any point in the chain.

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