How to Use the WooCommerce Inventory Updater MCP in Google ADK

Q: How does the Google ADK manage WooCommerce Inventory Updater?

You use McpToolset and pass it to an LlmAgent. The agent interprets your request, extracts the required product/variation IDs and new stock counts, and executes the update.

Q: What data does Google ADK touch when executing WooCommerce Inventory Updater?

It uses product identifiers (productId, variationId) and stock quantities. Since it operates within the GCP ecosystem, all actions are tied back to your secure cloud data sources.

Connect your store inventory to BigQuery using the Google ADK for enterprise-grade updates.

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Connect WooCommerce Inventory Updater MCP to Google ADK

Create your Vinkius account to connect WooCommerce Inventory Updater to Google ADK 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 WooCommerce Inventory Updater with Google ADK

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Centralized Inventory Sync

The `update_woocommerce_inventory` tool allows your agent to modify stock quantities across any WooCommerce product or variation. It's perfect when you need to sync inventory data that lives in BigQuery. Instead of manual uploads, the agent reads structured data from Google Cloud and executes the precise updates needed for your store.

BigQuery-Native Data Processing

Because it integrates natively with BigQuery, your agent can perform long-context reasoning over massive datasets. You don't just pass a file; you query and process the data first. This means complex logic—like calculating net stock based on multiple sources—happens before the update even begins.

Handling Massive Batches

Long-context Gemini models let you handle massive batches of inventory updates in a single reasoning pass. You can process thousands of records without running into token limits. This makes scaling up your inventory management incredibly straightforward for enterprise users.

Setup guide

Set up WooCommerce Inventory Updater 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 WooCommerce Inventory Updater 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="WooCommerce Inventory Updater_agent",
    model="gemini-2.0-flash",
    instruction="You have access to WooCommerce Inventory Updater tools via MCP.",
    tools=mcp_tools,
)

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Common questions about WooCommerce Inventory Updater MCP in Google ADK

You use `McpToolset` and pass it to an LlmAgent. The agent interprets your request, extracts the required product/variation IDs and new stock counts, and executes the update.

Yes. Because of its integration with BigQuery, you can write logic that calculates the correct `stockQuantity` based on multiple data tables before running the update.

It uses product identifiers (`productId`, `variationId`) and stock quantities. Since it operates within the GCP ecosystem, all actions are tied back to your secure cloud data sources.

It’s designed for enterprise use cases. The ability to reason over 1M+ tokens means you can process and validate massive, critical data sets before committing any changes.

The long-context window helps. You can load the entire dataset—the sheet, the rules, and the update instructions—into memory for the agent to reason over coherently.

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