Does this MCP prevent LLM math errors?

Yes. It moves the calculation from the LLM's reasoning process to a deterministic math library, ensuring the results are mathematically perfect.

What scaling methods are supported?

It supports both Z-Score standardization (Mean=0, Variance=1) and MinMax scaling (Range 0-1).

Is my data sent to an external server for processing?

No. The scaling happens locally on your machine, which keeps your sensitive training data private.

Can I scale multiple columns at the same time?

Yes. You can specify multiple features to be scaled in a single command.

How do I use the scaling metrics later?

The MCP returns the exact means, standard deviations, minimums, and maximums used, allowing you to perform inverse transformations if needed.

What is the difference between Standard and MinMax scaling?

Standard scaling (Z-Score) centers data at 0 with a variance of 1, ideal for algorithms that assume normally distributed features. MinMax compresses all values precisely between 0 and 1, ideal for neural networks and distance-based algorithms.

Are the computed scaling parameters returned for inverse transforms?

Yes. The JSON response includes the exact Mean and Std Dev (for Standard) or Min and Max (for MinMax) used to scale each column, enabling precise inverse transformations when needed.

Can it scale 10+ columns at once?

Absolutely. Pass a JSON array of all column names and they will all be scaled simultaneously in memory. The engine processes each column independently with its own computed metrics.

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Connect Feature Scaler Engine to ChatGPT, Claude and Gemini

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Get mathematically perfect normalization for your machine learning models.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 1 capability

We ship only the actions that matter.

Capability set01 / 01

01

1 capability in this set.

Part of 1 available through Feature Scaler Engine.

01
Scale features
This capability performs deterministic Z-Score or MinMax scaling on numeric columns. It executes the math locally to ensure your AI client doesn't hallucinate the results.

Real work

What teams actually run

Neural Network Preprocessing

Scale input features to prevent gradient issues during training.

K-Means Clustering

Normalize features so that distance-based algorithms aren't biased by different units.

Image Data Preparation

Compress pixel intensity values into a 0 to 1 range for computer vision tasks.

Data Auditing

Get the exact statistical parameters used to transform a dataset for verification.

Automate a task

Put Feature Scaler Engine to work on one thing.

Step-by-step automations your AI runs through this Connector: the workflow, the prompts to start with, and the exact capabilities behind each move.

One connector, every AI

Feature Scaler Engine works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

Building your own app? The connector is yours to use.

You don't need a client to put Feature Scaler Engine to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

847ms average. Fast in production.

Feature Scaler Engine is checked daily against the live service.

Daily averagePeak 1109ms

Oct 3Today

Fastest day: 676ms
Slowest day: 1109ms
14-day trend: Slowing+8%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 1 capability arrives ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Feature Scaler Engine, so you can see the experience inside your AI.

It does not authenticate your account with Feature Scaler Engine. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Feature Scaler Engine Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_F7GwWGq3LmnuDkiU1owmlZfI1wLX3GKZ5metNm37/mcp

Claude Desktop

Follow the steps below to connect in seconds.

1In Claude Desktop, open Settings → Connectors.
2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3Click Add and start a new chat — Feature Scaler Engine capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "feature-scaler-engine-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_F7GwWGq3LmnuDkiU1owmlZfI1wLX3GKZ5metNm37/mcp"
    }
  }
}

Claude
ChatGPT
Cursor
VS Code
Windsurf
Claude Code
JetBrains
Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up Feature Scaler Engine in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Feature Scaler Engine owners hand off.

Data scientists and machine learning engineers who need to prep datasets for model training without manual math verification.

01
Machine Learning Engineer
Hands off the feature engineering math to the MCP to ensure neural network inputs are perfectly normalized.
02
Data Scientist
Uses the capability to prepare data for clustering algorithms like K-Means.
03
AI Developer
Integrates deterministic scaling into agentic workflows to avoid LLM math hallucinations.

FAQ

Questions Feature Scaler Engine owners ask.

01
Does this MCP prevent LLM math errors?
Yes. It moves the calculation from the LLM's reasoning process to a deterministic math library, ensuring the results are mathematically perfect.
02
What scaling methods are supported?
It supports both Z-Score standardization (Mean=0, Variance=1) and MinMax scaling (Range 0-1).
03
Is my data sent to an external server for processing?
No. The scaling happens locally on your machine, which keeps your sensitive training data private.
04
Can I scale multiple columns at the same time?
Yes. You can specify multiple features to be scaled in a single command.
05
How do I use the scaling metrics later?
The MCP returns the exact means, standard deviations, minimums, and maximums used, allowing you to perform inverse transformations if needed.
06
What is the difference between Standard and MinMax scaling?
Standard scaling (Z-Score) centers data at 0 with a variance of 1, ideal for algorithms that assume normally distributed features. MinMax compresses all values precisely between 0 and 1, ideal for neural networks and distance-based algorithms.
07
Are the computed scaling parameters returned for inverse transforms?
Yes. The JSON response includes the exact Mean and Std Dev (for Standard) or Min and Max (for MinMax) used to scale each column, enabling precise inverse transformations when needed.
08
Can it scale 10+ columns at once?
Absolutely. Pass a JSON array of all column names and they will all be scaled simultaneously in memory. The engine processes each column independently with its own computed metrics.

Explore

More in Developer Tools

Connect Feature Scaler Engine to Claude, Cursor, ChatGPT & more

Your AI does the work. We keep it safe and sound.

Each Connector runs inside its own sealed environment.

One switch stops every agent. Instantly.

Spending limits that pause instead of surprise.

We ship only the actions that matter.

01

Scale features

What teams actually run

Neural Network Preprocessing

K-Means Clustering

Image Data Preparation

Data Auditing

Building your own app? The connector is yours to use.

Feature Scaler Engine Connector

Claude Desktop

Machine Learning Engineer

Data Scientist

AI Developer

Does this MCP prevent LLM math errors?

What scaling methods are supported?

Is my data sent to an external server for processing?

Can I scale multiple columns at the same time?

How do I use the scaling metrics later?

What is the difference between Standard and MinMax scaling?

Are the computed scaling parameters returned for inverse transforms?

Can it scale 10+ columns at once?

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