How to Use the Shoplazza / 店匠 MCP in AutoGen

Q: How does AutoGen handle complex product data using Shoplazza / 店匠?

The getproduct tool provides detailed records. With AutoGen, your agents debate the best use of that data—for example, debating if a product should be promoted based on its features versus its current low-level inventory count.

Q: Can I track order history for Shoplazza / 店匠 using AutoGen?

Yes. You use listorders to pull all transaction data, and your agents can then process it through getcustomer records. This lets them debate whether a sudden spike in orders means successful marketing or just seasonal fluctuation.

Q: What kind of webhooks information does Shoplazza / 店匠 expose to AutoGen?

The listwebhooks tool shows which external systems are linked. Your agents can review this list and debate whether any critical sales channels need an updated connection point or if a new one should be added.

Q: Does Shoplazza / 店匠 support inventory checks with AutoGen?

It does. You use getinventorylevels to get current stock numbers. Your agents can then debate the safest reorder point, balancing immediate sales needs against supplier lead times.

Run multi-agent debates for complex e-commerce decisions on Shoplazza / 店匠.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers — Included with Plan

Connect Shoplazza / 店匠 MCP to AutoGen

Create your Vinkius account to connect Shoplazza / 店匠 to AutoGen — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

GDPR Included with Plan

Setup Shoplazza / 店匠 with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Determine pricing strategies by debating inventory risks.

The `get_product` tool feeds initial data into the debate. Agents then cross-reference this with real-time stock counts using `get_inventory_levels`. This allows your framework to model scenarios, for instance: what happens if we drop the price now versus holding out until the next seasonal wave? Your agents don't just report numbers; they argue about them. One agent can push for immediate markdowns based on low stock data, while another flags potential brand damage from deep discounting across Shoplazza / 店匠.

Analyze order patterns and predict customer needs.

`list_orders` gives the agents a full history of recent transactions. Combining this with `get_customer` details lets your team debate *why* certain customers buy certain items. For example, one agent might notice that high-value buyers consistently purchase collections listed via `list_collections`, prompting a strategic recommendation for bundling. The AutoGen framework can model the risk: if we wait too long to refresh product listings using `list_products`, will customer interest drop? It simulates the friction points and builds consensus on when—and how—to intervene.

Manage platform settings and troubleshoot integrations.

If you need visibility into system health, start with `get_shop_info` to gather core data. From there, agents can use `list_webhooks` to check which external services are connected and if they're firing correctly. This is huge for keeping a complex e-commerce site running smoothly. It’s perfect for the multi-agent setup because one agent checks the technical logs from `list_webhooks`, while another advises on the business impact of those connection gaps for Shoplazza / 店匠.

Setup guide

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

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

Get your connection token →

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 / 店匠_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Shoplazza / 店匠 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 / 店匠. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Shoplazza / 店匠 now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Shoplazza / 店匠 MCP in AutoGen

The `get_product` tool provides detailed records. With AutoGen, your agents debate the best use of that data—for example, debating if a product should be promoted based on its features versus its current low-level inventory count.

Yes. You use `list_orders` to pull all transaction data, and your agents can then process it through `get_customer` records. This lets them debate whether a sudden spike in orders means successful marketing or just seasonal fluctuation.

The `list_webhooks` tool shows which external systems are linked. Your agents can review this list and debate whether any critical sales channels need an updated connection point or if a new one should be added.

It does. You use `get_inventory_levels` to get current stock numbers. Your agents can then debate the safest reorder point, balancing immediate sales needs against supplier lead times.

This server handles core e-commerce data: product details (via `get_product`), customer profiles (`get_customer`), and transactional records (`list_orders`). All of it is available for your agents to debate.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript