How to Use the VTEX Orders MCP in Google ADK

Q: How do I search for VTEX Orders using Google ADK?

Use the searchorders tool to find what you need, filtering by status or managing pagination. The results are easily passed into BigQuery for immediate historical analysis.

Q: Can Google ADK help get tracking information for VTEX Orders?

Yes. After an order ships, use gettrackinginfo to grab the carrier data. This info can then be stored and analyzed in your BigQuery tables.

Q: If I register a tax invoice using Google ADK, where does that data go?

The agent calls registerinvoice, which writes the record back to VTEX. You can then use this confirmed data as a source of truth when running models on Vertex AI.

Q: Does the MCP Server support getting full details for VTEX Orders with Google ADK?

It does. The getorder tool provides all necessary fields, and Gemini's long-context window ensures your agent doesn't lose track of any critical details.

Process VTEX Orders with Google ADK. Build enterprise agents that connect directly to your Google Cloud stack.

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Connect VTEX Orders MCP to Google ADK

Create your Vinkius account to connect VTEX Orders 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 VTEX Orders with Google ADK

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Updating Order Status via MCP Server

The `update_order_status` tool lets you change an order's status on VTEX. Your agent handles this using native integration with BigQuery, making the audit trail simple. Since it works across Google Cloud infrastructure, you can reliably log every status transition for later analysis.

Fetching Order Details in Google ADK

`get_order` pulls full order details from VTEX. When paired with Gemini models, your agent can hold all that data—potentially over 1M tokens—to reason about the order's entire context. This means you don't just get a list of fields; you get enough information to make complex decisions based on multiple data points.

Managing Order Lifecycle using MCP Server

The agent can execute `cancel_order` and then use `search_orders` to find replacement orders. It manages the whole process, keeping track of both actions. For enterprise users on Google Cloud, this sequence makes sense because it’s traceable across your entire data platform.

Setup guide

Set up VTEX Orders 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 VTEX Orders 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="VTEX Orders_agent",
    model="gemini-2.0-flash",
    instruction="You have access to VTEX Orders tools via MCP.",
    tools=mcp_tools,
)

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Common questions about VTEX Orders MCP in Google ADK

Use the `search_orders` tool to find what you need, filtering by status or managing pagination. The results are easily passed into BigQuery for immediate historical analysis.

Yes. After an order ships, use `get_tracking_info` to grab the carrier data. This info can then be stored and analyzed in your BigQuery tables.

The agent calls `register_invoice`, which writes the record back to VTEX. You can then use this confirmed data as a source of truth when running models on Vertex AI.

It does. The `get_order` tool provides all necessary fields, and Gemini's long-context window ensures your agent doesn't lose track of any critical details.

It touches structured e-commerce operational data: Order IDs, payment statuses, shipment tracking numbers, and tax invoice records. This is ideal for BigQuery ingestion.

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