How to Use the Feature Scaler Engine MCP in Cursor

Q: How do I add Feature Scaler Engine to my Cursor project config?

Create a .cursor/mcp.json file in your project root and paste the server configuration under the mcpServers key. Once saved, Cursor's agent mode will immediately gain access to the scalefeatures tool.

Q: Can Cursor use Feature Scaler Engine to write scaled arrays directly into my Python test files?

Yes. You can ask the Cursor agent to run the scalefeatures tool on a raw dataset and paste the resulting normalized Python array directly into your test suite.

Q: Does the server support both Z-score and MinMax mathematical standards?

Yes. The scalefeatures tool deterministically handles both Z-score standardization and MinMax scaling based on what your current model requires.

Q: Where does Feature Scaler Engine process my numeric database tables?

Every calculation is performed locally. The scalefeatures tool processes your numeric datasets inside Cursor's secure environment, ensuring your sensitive floats and integers never upload to external servers.

Run local Z-score and MinMax scaling on your code's numeric inputs directly inside Cursor.

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Connect Feature Scaler Engine MCP to Cursor

Create your Vinkius account to connect Feature Scaler Engine to Cursor 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 Cursor

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Generate Clean ML Code with Live Scaled Data in Cursor

Stop writing mock arrays to test your neural network inputs. This MCP Server allows Cursor to pull actual scaled values directly into your active code editor while you write your training loops. By executing the `scale_features` tool, the Cursor agent gets real math outputs instead of placeholder lists. You get to see exactly how your raw numbers transform into Z-scores right inside your editor window.

Instant Input Normalization for Active Debugging

When your model diverges, you need to check if your inputs are scaled correctly. Use Cursor's agent mode with this MCP Server to call the `scale_features` tool on your test datasets to pinpoint the exact columns causing gradient explosion. The agent runs the math locally, showing you the exact MinMax range of your features. You can patch your data pipeline bugs immediately without switching to an external terminal.

Deterministic Math inside the Cursor Sandbox

This MCP Server runs local, deterministic calculations so your training data remains consistent across runs. When Cursor invokes the `scale_features` tool, the math happens instantly inside your local environment. You get precise, floating-point accuracy for every column. The agent reads the output and writes the correct scaling parameters straight into your configuration files.

Setup guide

Set up Feature Scaler Engine MCP in Cursor

Prerequisites

Cursor installed (macOS, Windows, or Linux)
Active Vinkius subscription with a valid endpoint token

1

Open MCP Settings

Go to Cursor Settings → MCP or open the Command Palette (Cmd+Shift+P / Ctrl+Shift+P) and search for "MCP: Add Server".
2

Add the Feature Scaler Engine MCP

Cursor will create or open .cursor/mcp.json in your project root. Paste the JSON snippet on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Enable Agent mode

Open Composer (Cmd+I / Ctrl+I) and switch to Agent mode using the dropdown at the top. MCP tools are only available in Agent mode.
4

Verify the connection

Ask Cursor something like "List my recent Feature Scaler Engine transactions." If the MCP tools are loaded correctly, Cursor will call the Feature Scaler Engine tools automatically. You can also check Settings → MCP for a green status indicator.

.cursor/mcp.json

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

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Common questions about Feature Scaler Engine MCP in Cursor

Create a `.cursor/mcp.json` file in your project root and paste the server configuration under the `mcpServers` key. Once saved, Cursor's agent mode will immediately gain access to the `scale_features` tool.

Yes. You can ask the Cursor agent to run the `scale_features` tool on a raw dataset and paste the resulting normalized Python array directly into your test suite.

Writing manual scaling code takes time and introduces bugs like division by zero. This MCP Server lets Cursor automate the scaling process via the pre-tested `scale_features` tool, saving you from writing boilerplate math.

Yes. The `scale_features` tool deterministically handles both Z-score standardization and MinMax scaling based on what your current model requires.

Every calculation is performed locally. The `scale_features` tool processes your numeric datasets inside Cursor's secure environment, ensuring your sensitive floats and integers never upload to external servers.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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