How to Use the Youzan / 有赞 MCP in AutoGen

Q: What kind of data privacy can I expect when using Youzan / 有赞 with AutoGen?

The server touches sensitive types like Customer ID (from listcustomers) and Order Total (from getorder). The agents' debate process requires handling these raw data types.

Run debates on Youzan / 有赞 systems using AutoGen agents.

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Connect Youzan / 有赞 MCP to AutoGen

Create your Vinkius account to connect Youzan / 有赞 to AutoGen 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 AutoGen

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Claude

Perplexity

Consensus-driven reporting

Need a full sales report? Don't just call `get_sales_stats`. Set up an agent debate: one agent calls the tool, while another challenges it on data completeness. A third agent arbitrates to produce the final, agreed-upon number. This process forces multiple perspectives on single results. The result isn't determined by a single function call; it's reached through negotiation among specialized agents.

Complex fulfillment planning

A shipment requires checking inventory and customer status. One agent uses `get_inventory` to confirm stock. A second agent runs `list_customers` to verify shipping eligibility. They debate the viability of the order until a final, actionable plan is reached. This multi-agent system handles ambiguity. The agents talk through dependencies—for example, if inventory is low, they discuss alternatives instead of just failing.

Product data validation

Before listing a product, you need to validate its details. An agent pulls item specifics via `get_item`. Another compares those specs against general shop rules using `get_shop` and flags inconsistencies. The debate ensures the final output is vetted from multiple angles. You're building systems that don't assume perfect inputs or outcomes.

Setup guide

Set up Youzan / 有赞 MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Youzan / 有赞 tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Youzan / 有赞_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Youzan / 有赞 data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Youzan / 有赞_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Youzan / 有赞 data")
    print(result.messages[-1].content)

Get your connection token →

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 AutoGen

AutoGen handles conflict through debate. If two specialized agents pull different data points (e.g., one agent uses `get_order` and another uses `list_orders`), they discuss the discrepancy until a consensus is reached.

You can build systems where the answer isn't obvious. For example, you can make agents debate whether an item listed via `list_items` meets current inventory requirements set by `get_inventory`.

The server exposes multiple datasets. Agents can debate based on customer details (`get_customer`), order history (`list_orders`), and product metadata (`get_item`).

The goal of AutoGen is reliability. Because multiple agents challenge each other's conclusions, the final decision converges on a robust conclusion, making it highly dependable.

The server touches sensitive types like Customer ID (from `list_customers`) and Order Total (from `get_order`). The agents' debate process requires handling these raw data types.

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