Feature Scaler Engine MCP Server for Google ADKGive Google ADK instant access to 1 tools to Scale Features
GDPR Free for SubscribersGoogle Agent Development Kit (ADK) is Google's framework for building production AI agents. Add Feature Scaler Engine as an MCP tool provider through Vinkius and your ADK agents can call every tool with full schema introspection.
Ask AI about this MCP Server for Google ADK
The Feature Scaler Engine MCP Server for Google ADK is a standout in the Developer Tools category — giving your AI agent 1 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
Geminifrom google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import (
StreamableHTTPConnectionParams,
)
# Your Vinkius token. get it at cloud.vinkius.com
mcp_tools = McpToolset(
connection_params=StreamableHTTPConnectionParams(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
)
)
agent = Agent(
model="gemini-2.5-pro",
name="feature_scaler_engine_agent",
instruction=(
"You help users interact with Feature Scaler Engine "
"using 1 available tools."
),
tools=[mcp_tools],
)
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Feature Scaler Engine MCP Server
Neural Networks and K-Means clustering algorithms fail spectacularly if features aren't normalized. If an LLM attempts to subtract the mean and divide by the standard deviation across 5,000 rows, it will hallucinate 90% of the math.
Google ADK natively supports Feature Scaler Engine as an MCP tool provider. declare Vinkius Edge URL and the framework handles discovery, validation, and execution automatically. Combine 1 tools with Gemini's long-context reasoning for complex multi-tool workflows, with production-ready session management and evaluation built in.
This MCP brings deterministic Feature Scaling to your AI using simple-statistics. The AI specifies whether it wants Standard scaling (Mean=0, Variance=1) or MinMax scaling (Range 0-1), and the engine flawlessly transforms the target columns in milliseconds — returning the exact computed metrics for auditability.
The Superpowers
- Flawless Normalization: No LLM math hallucinations — exact scaling computed by your CPU.
- Multi-Column Support: Scale multiple features simultaneously in a single call.
- Automated Metric Extraction: Returns the exact Means, Std Devs, Mins, and Maxs used for scaling.
- Data Privacy: Your sensitive training data stays entirely on your machine.
The Feature Scaler Engine MCP Server exposes 1 tools through the Vinkius. Connect it to Google ADK in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 1 Feature Scaler Engine tools available for Google ADK
When Google ADK connects to Feature Scaler Engine through Vinkius, your AI agent gets direct access to every tool listed below — spanning data-normalization, machine-learning, z-score, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.
Scale features on Feature Scaler Engine
Deterministically Standardize (Z-Score) or MinMax Scale numeric columns offline
Connect Feature Scaler Engine to Google ADK via MCP
Follow these steps to wire Feature Scaler Engine into Google ADK. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.
Install Google ADK
pip install google-adkReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenCreate the agent
Explore tools
Why Use Google ADK with the Feature Scaler Engine MCP Server
Google ADK provides unique advantages when paired with Feature Scaler Engine through the Model Context Protocol.
Google ADK natively supports MCP tool servers. declare a tool provider and the framework handles discovery, validation, and execution
Built on Gemini models, ADK provides long-context reasoning ideal for complex multi-tool workflows with Feature Scaler Engine
Production-ready features like session management, evaluation, and deployment come built-in. not bolted on
Seamless integration with Google Cloud services means you can combine Feature Scaler Engine tools with BigQuery, Vertex AI, and Cloud Functions
Feature Scaler Engine + Google ADK Use Cases
Practical scenarios where Google ADK combined with the Feature Scaler Engine MCP Server delivers measurable value.
Enterprise data agents: ADK agents query Feature Scaler Engine and cross-reference results with internal databases for comprehensive analysis
Multi-modal workflows: combine Feature Scaler Engine tool responses with Gemini's vision and language capabilities in a single agent
Automated compliance checks: schedule ADK agents to query Feature Scaler Engine regularly and flag policy violations or configuration drift
Internal tool platforms: build self-service agent platforms where teams connect their own MCP servers including Feature Scaler Engine
Example Prompts for Feature Scaler Engine in Google ADK
Ready-to-use prompts you can give your Google ADK agent to start working with Feature Scaler Engine immediately.
"Standardize the 'Age' and 'Salary' columns to have a mean of 0 and variance of 1."
"Apply MinMax scaling to the 'PixelIntensity' feature so all values are between 0 and 1."
"Normalize all numeric features in this dataset before training my K-Means clustering model."
Troubleshooting Feature Scaler Engine MCP Server with Google ADK
Common issues when connecting Feature Scaler Engine to Google ADK through Vinkius, and how to resolve them.
McpToolset not found
pip install --upgrade google-adkFeature Scaler Engine + Google ADK FAQ
Common questions about integrating Feature Scaler Engine MCP Server with Google ADK.
How does Google ADK connect to MCP servers?
Can ADK agents use multiple MCP servers?
Which Gemini models work best with MCP tools?
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