Feature Scaler Engine MCP Server with 1 Tools for Claude, Cursor, and AI Agents
GDPR Free for SubscribersStandardize (Z-Score) or MinMax scale numeric columns with mathematical perfection local. Essential normalization for neural networks and clustering algorithms. Vinkius routes your AI agents directly to Feature Scaler Engine through a governed connection. 1 tools ready to use with Claude, ChatGPT, Cursor, or any AI agent — no hosting, no setup, connect in 30 seconds.
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Compatible with every major AI agent and IDE
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What is the simple-statistics MCP Server?
The simple-statistics MCP Server routes AI agents like Claude, ChatGPT, and Cursor directly to simple-statistics via 1 tools. Standardize (Z-Score) or MinMax scale numeric columns with mathematical perfection local. Essential normalization for neural networks and clustering algorithms. Powered by Vinkius — your credentials stay on your side of the connection, every request is auditable. Connect in under 2 minutes.
Built-in capabilities (1)
Tools for your AI Agents to operate simple-statistics
Ask your AI agent "Standardize the 'Age' and 'Salary' columns to have a mean of 0 and variance of 1." and get the answer without opening a single dashboard. With 1 tools connected to real simple-statistics data, your agents reason over live information, cross-reference it with other MCP servers, and deliver insights you would spend hours assembling manually.
Works with Claude, ChatGPT, Cursor, and any MCP-compatible client. Powered by Vinkius — your credentials never touch the AI model, every request is auditable. Connect in under two minutes.
Why teams choose Vinkius
One subscription gives you the infrastructure to connect your AI agents to thousands of MCP servers — and deploy your own to the Vinkius Edge. Your credentials stay yours. Your data flows directly between your agent and the API. DLP blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade routing and governance, zero maintenance.
Build your own MCP Server with our secure development framework →The Feature Scaler Engine App Connector works with every AI agent you already use
…and any MCP-compatible client
Use all 1 Feature Scaler Engine tools with your AI agents right now
Vinkius routes your AI agents to Feature Scaler Engine through a governed proxy. Beyond a simple connection, you get full visibility into every action your agents perform, with enterprise-grade security and up to 60% savings on AI costs.
Scale features on Feature Scaler Engine
Deterministically Standardize (Z-Score) or MinMax Scale numeric columns offline
What the Feature Scaler Engine MCP Server unlocks
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.
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.
Frequently asked questions about the Feature Scaler Engine MCP Server
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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We built the connector to Feature Scaler Engine. Now put your agents to work. Fully governed.
Vinkius is the AI Gateway with managed hosting. Stop building connectors. Every connection runs inside eight layers of security.
Hosted, sandboxed, and live on AWS. You don't provision anything. You don't maintain anything. You connect.
Every tool call, every token, every response. Logged and auditable. Data flows direct from Feature Scaler Engine to your agent. Nothing is stored on our side. Ever.
Eight governance layers on every request. Sensitive data redacted before it reaches the model. Kill switch if anything goes sideways. Always on.