How to Use the Feature Scaler Engine MCP in Google ADK

Q: Can I restrict which tools Feature Scaler Engine exposes to Google ADK?

Yes, you can use the toolnames filter in your toolset configuration. This limits the agent to only what you explicitly allow.

Connect Feature Scaler Engine to Google ADK to prepare your BigQuery data for Gemini models.

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Connect Feature Scaler Engine MCP to Google ADK

Create your Vinkius account to connect Feature Scaler Engine 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 Feature Scaler Engine with Google ADK

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Scale BigQuery data for Gemini

The `scale_features` tool acts as a bridge between your raw cloud data and your agent's reasoning process. It normalizes your columns to ensure Gemini models receive stable input. Simply pass your dataset to the tool. It calculates the necessary shifts and returns the formatted data ready for high-token context windows.

Google ADK toolset integration

You add the server to your agent using the McpToolset class. This makes `scale_features` available as a native function within your agent's toolkit. You can filter tool names if you need to keep your agent's scope tight. It works with both Stdio and HTTP transports depending on your deployment.

High-precision normalization logic

The `scale_features` tool handles division-by-zero errors internally. It ensures your scaling logic never crashes your enterprise agent pipelines. You get reliable math every time. The engine treats your numeric columns as primary data, applying the exact scaling method you request.

Setup guide

Set up Feature Scaler Engine 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 Feature Scaler Engine 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="Feature Scaler Engine_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Feature Scaler Engine tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Feature Scaler Engine MCP in Google ADK

You use the McpToolset class to point your agent at the server URL. Adding it to the tools list allows your agent to invoke scaling operations automatically.

Yes, you can use the tool_names filter in your toolset configuration. This limits the agent to only what you explicitly allow.

Not at all. The server runs independently in our sandbox. Your agent just receives the processed numeric output over the transport layer.

The server processes numeric features efficiently. It allows your agent to feed normalized data into large context windows without bloating the token count.

We isolate your execution in a zero-trust environment. Your input numeric records are processed on-the-fly and deleted immediately after the response.

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