How to Use the Youzan / 有赞 MCP in LangChain

Build complex e-commerce chains for Youzan / 有赞 with LangChain.

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Connect Youzan / 有赞 MCP to LangChain

Create your Vinkius account to connect Youzan / 有赞 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 Youzan / 有赞 with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Multi-step data retrieval

Need to know if a customer can buy an item? Start by finding the user using `list_customers`. Then, use that ID to fetch their specific details with `get_customer`. Finally, check the stock level for the product they want using `get_inventory`. This sequence of calls lets your agent build a full picture. It’s not just calling tools; it's chaining them together so the output from one step feeds directly into the next.

Analyzing order performance

Want to understand recent sales trends? The system lets you get a list of sold trades via `list_orders`. Next, pass those orders to calculate overall results using `get_sales_stats`. You can even cross-reference the items sold by calling `get_item` for specific SKUs. Your agent decides which stats are needed and runs them in order. The whole thing is visible through LangSmith tracing, so you know exactly when things slow down or if a tool call fails.

Shop setup and product listing

Setting up a new campaign? First, get the overall shop info using `get_shop`. Then, list all available products with `list_items` to see what's on sale. You can also refine this by getting a list of item categories using `list_categories`. This process helps your agent map out dependencies across different parts of the store setup. It’s powerful because it handles multiple, unrelated tool calls in one single run.

Setup guide

Set up Youzan / 有赞 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 Youzan / 有赞 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({
    "youzan-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 Youzan / 有赞 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 Youzan / 有赞. 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 Youzan / 有赞 MCP in LangChain

LangChain lets you build multi-step chains that process orders. For example, your agent can call `get_order` to fetch details and then use those details as input to run `get_sales_stats`. This means the workflow is dynamic, not fixed.

LangChain focuses more on execution flow than indexing. While you can trace all inputs and outputs via LangSmith for observability, the core function is connecting tools in a chain rather than building a searchable knowledge base.

The server exposes various e-commerce datasets. Specifically, you can get customer details via `get_customer`, inventory levels through `get_inventory`, and historical transactions by calling `list_orders`.

The tool calls themselves define the boundaries. For instance, to list customers, you must call `list_customers` and provide search criteria; the agent cannot invent customer IDs.

The server touches several types of private data, including Customer ID (from `get_customer`) and Order Total (from `get_order`). Always manage your token endpoints securely.

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