How to Use the Shoplazza MCP in AutoGen

Q: What data points does Shoplazza expose when running AutoGen?

The system handles customer records (listshoplazzacustomers), product catalogs, and order details. The agents process this raw e-commerce data through debate to reach a final conclusion.

Q: How can Shoplazza help with inventory planning using AutoGen?

You'll set up multiple agents: one listing current stock (listshoplazzaproducts), and another analyzing sales velocity. They debate how many new units need to be ordered, providing a justified recommendation.

Use Shoplazza to run multi-agent debates on complex e-commerce scenarios with AutoGen.

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Setup Shoplazza with AutoGen

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Debating Product Creation Logic

Set up a debate where one agent suggests creating a product using `create_shoplazza_product`. A second 'Compliance Agent' can challenge the input data, forcing the system to refine the SKU and category before finalization. The agents argue until they reach an approved plan.

Consensus on Order Fulfillment Status

You can deploy three agents: one checks `get_shoplazza_order` for status, a second agent reviews the order value via `list_shoplazza_orders`, and a third attempts to flag potential payment issues. The conversation forces consensus on whether the order is ready to ship.

Reviewing Customer Data with AutoGen

Need to vet new customer signups? One agent pulls data using `create_shoplazza_customer`, another checks if the user already exists via `get_shoplazza_customer`, and a third flags any unusual address formats. The agents negotiate until they confirm the account is valid.

Setup guide

Set up Shoplazza 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 Shoplazza 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="Shoplazza_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Shoplazza 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="Shoplazza_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Shoplazza 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 Shoplazza. 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 Shoplazza MCP in AutoGen

The MCP Server provides specific tools that different Autonomous Agents use in their conversation. They debate, challenge assumptions, and pass structured data (like product IDs) back and forth until a decision is reached.

Yes. You can have agents compare the current shop metadata from `get_shoplazza_shop` against an ideal policy set, forcing deliberation until all discrepancies are noted.

The system handles customer records (`list_shoplazza_customers`), product catalogs, and order details. The agents process this raw e-commerce data through debate to reach a final conclusion.

You'll set up multiple agents: one listing current stock (`list_shoplazza_products`), and another analyzing sales velocity. They debate how many new units need to be ordered, providing a justified recommendation.

The core data includes customer profiles, product inventories, and transactional order records. These structured datasets form the basis for agent deliberation.

Use it with your favorite AI tools

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