How to Use the Shoplazza / 店匠 MCP in Google ADK

Q: How do I use Shoplazza / 店匠 with Google ADK?

You initialize the toolset using McpToolset and pass it to your LlmAgent. The native integration means the agent can act on this data while also interacting with BigQuery.

Q: To review past transactions on Shoplazza / 店匠, which tools work best with Google ADK?

You should use listorders to get the list and then run getorder for specifics. Since you're working in an enterprise setting, this data flow will map perfectly into your existing cloud architecture.

Q: If I need product details for Shoplazza / 店匠, what should I use with Google ADK?

Simply call getproduct to get the necessary fields on a specific item. If you're doing mass data pulls, start with listproducts.

Q: Does Shoplazza / 店匠 touch sensitive customer data when using Google ADK?

Yeah, it touches customer names and email addresses via getcustomer. When working in BigQuery, you need to manage access controls carefully.

Build enterprise e-commerce agents for Shoplazza / 店匠 using the Google ADK and BigQuery.

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Connect Shoplazza / 店匠 MCP to Google ADK

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Setup Shoplazza / 店匠 with Google ADK

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Pull product catalog data

You can pull specific item details with `get_product`, or get a full list of items via `list_products`. This is perfect for feeding structured e-commerce data directly into BigQuery for analysis. Need to know about groupings? Use `list_collections` to map out product categories, and then check the system connections using `list_webhooks`.

Manage customer records

The agent can get a client's info by calling `get_customer`, or pull lists of all registered shoppers with `list_customers`. Because you’re on Google Cloud, this data feeds right into your existing datasets. For sales tracking, use `list_orders` to see the history. If you need deep details on a single purchase, call `get_order`.

Check shop and inventory status

Need an immediate operational snapshot? Use `get_shop_info` for core platform data. For stock levels, the agent runs `get_inventory_levels`. This is crucial when you're modeling fulfillment pipelines in Vertex AI. These calls let your agents know if a product is available and what the basic shop parameters are.

Setup guide

Set up Shoplazza / 店匠 MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Shoplazza / 店匠 tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Shoplazza / 店匠_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Shoplazza / 店匠 tools via MCP.",
    tools=mcp_tools,
)

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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 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.

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Common questions about Shoplazza / 店匠 MCP in Google ADK

You initialize the toolset using `McpToolset` and pass it to your LlmAgent. The native integration means the agent can act on this data while also interacting with BigQuery.

It gives structured e-commerce data: everything from inventory counts (`get_inventory_levels`) to customer details (`get_customer`). This makes it easy to load into a BigQuery table for long-term analysis.

You should use `list_orders` to get the list and then run `get_order` for specifics. Since you're working in an enterprise setting, this data flow will map perfectly into your existing cloud architecture.

Simply call `get_product` to get the necessary fields on a specific item. If you're doing mass data pulls, start with `list_products`.

Yeah, it touches **customer names and email addresses** via `get_customer`. When working in BigQuery, you need to manage access controls carefully.

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