How to Use the Wine-Searcher MCP in LangChain

Q: Can Wine-Searcher help with market analysis using LangChain?

Absolutely. By chaining marketprice output to other tools, you can perform comparative analyses. For instance, check the price variance for a specific grape variety across different regions.

Q: Is Wine-Searcher reliable for regional data in LangChain?

Yes. The regioninfo tool provides essential terroir education data. Your agent can use this as foundational knowledge before calling other tools like searchwines.

Build complex wine analysis chains using the Wine-Searcher MCP Server with LangChain.

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Connect Wine-Searcher MCP to LangChain

Create your Vinkius account to connect Wine-Searcher 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-Searcher with LangChain

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Multi-Step Analysis with MCP Server

The `wine_check` tool serves as the central lookup for any wine question, pulling together critic scores, grape varieties, and regional data. You can chain this result to use `market_price` next, instantly comparing the retrieved scores against current global merchant pricing. This multi-step approach lets your agent decide the exact sequence of lookups. For example, it first checks a producer using `producer_info`, then uses the resulting region data with `region_info`, finishing by calling `search_wines` for discovery.

Agent-Driven Wine Discovery

Use `grape_info` when your agent needs to educate the user on a specific grape variety, providing background context. The agent then takes this knowledge and calls `search_wines`, filtering results based on the newly established variety criteria. This setup lets you build complex discovery pipelines. If a user asks about a region, the agent first uses `region_info` to define boundaries, which it passes directly into `wine_check` for detailed scoring.

Connecting Data Streams

`market_price` gets global pricing data points. This output can be fed as a variable into the `search_wines` tool, limiting results only to wines within a specific budget range. Alternatively, your agent can use `grape_info` to find variety details and then pass that information to both `market_price` and `producer_info`, allowing for cross-referencing of pricing trends against brand profiles.

Setup guide

Set up Wine-Searcher 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-Searcher 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-searcher-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-Searcher 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 Wine-Searcher. 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 Wine-Searcher MCP in LangChain

You use the MCP Server as a tool within your agent. Your LLM determines which function to call (e.g., `wine_check`) and what parameters it needs, making the process observable via LangSmith tracing.

You get critical wine details like scores, grape varieties, pricing from over 100,000 merchants. The tools provide structured outputs that your chain can process in subsequent steps.

Absolutely. By chaining `market_price` output to other tools, you can perform comparative analyses. For instance, check the price variance for a specific grape variety across different regions.

Yes. The `region_info` tool provides essential terroir education data. Your agent can use this as foundational knowledge before calling other tools like `search_wines`.

This server primarily handles structured e-commerce pricing and product metadata, such as global market pricing and critic scores. It doesn't access personal user accounts or sensitive PII.

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