How to Use the ApparelMagic MCP in Google ADK

Q: Can I filter which ApparelMagic tools the agent accesses?

You can pass a specific list to the toolnames argument. This restricts the agent to specific endpoints, keeping it focused on targeted tasks.

Feed your Google ADK agents live ApparelMagic data. Cross-reference warehouse counts with BigQuery using Gemini's massive context window.

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Connect ApparelMagic MCP to Google ADK

Create your Vinkius account to connect ApparelMagic 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 ApparelMagic with Google ADK

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Dump Catalogs into Google ADK

The `list_styles` and `list_products` tools pull your entire seasonal catalog directly into the agent. Gemini's 1M-token window swallows the whole dataset at once. Instead of pinging the API for every single variant, your agent reads the bulk list and cross-references it against historical sales data sitting in BigQuery.

Map Logistics via MCP Server

Your agent pulls tracking data using `list_shipments` and `get_shipment`. It maps those outbound boxes to the original invoices pulled via `get_order` and `list_orders`. You pass these tools into the LlmAgent using the McpToolset class. The agent analyzes the transit delays across thousands of orders in a single prompt.

Cross-Reference Buyer Profiles

The `list_customers` and `get_customer` tools extract wholesale buyer details from the ERP. The agent matches these profiles against your Vertex AI predictive models. You can restrict which endpoints the agent sees using the tool_names filter. If the agent only needs to analyze buyers, you block the inventory tools entirely.

Setup guide

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

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Common questions about ApparelMagic MCP in Google ADK

Create an McpToolset using the StreamableHttpServerParameters class. Pass that toolset into your LlmAgent configuration.

You can pass a specific list to the tool_names argument. This restricts the agent to specific endpoints, keeping it focused on targeted tasks.

You can pull massive arrays of active SKUs. Gemini holds the entire response in context, letting you run complex analysis without building local databases.

The framework manages the HTTP connection natively. If a request times out, the agent receives the failure state and can retry the specific query.

The endpoints transmit raw wholesale buyer profiles and order histories. Vinkius operates a zero-trust architecture where authentication tokens are ephemeral and never logged to disk.

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