Data Processing

Feature Scaler

Scale and normalize features using various methods for machine learning preprocessing and data standardization

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Quick start

Call this tool from your code in three languages.

cURL

curl -X POST 'https://api.elysiatools.com/en/api/tools/feature-scaler' \
  -H 'Content-Type: application/json' \
  -d '{"csvData":"Enter your CSV data here...\n\nExample:\nFeature1,Feature2,Feature3,Label\n1.5,2.3,0.8,A\n3.2,1.1,2.5,B\n0.8,4.5,1.2,C\n2.1,3.4,1.8,A\n4.5,1.9,0.7,B","scalingMethod":"standard","selectedColumns":"Comma-separated column names. Leave empty to auto-detect numeric columns.","scalingParams":"JSON format parameters for inverse transformation. Leave empty for forward transformation.","hasHeader":true,"inverseTransform":false,"outputFormat":"csv","saveParams":true,"scaledOnly":false}'

API reference

Send a POST request with your inputs as JSON. File parameters require a separate upload first.

Endpoint

HTTP

POST https://api.elysiatools.com/en/api/tools/feature-scaler

Parameters

Name	Type	Required	Description
csvData	textarea	Yes	—
scalingMethod	select	Yes	—
selectedColumns	text	No	Specify which columns to scale. If empty, all numeric columns will be automatically detected and scaled.
scalingParams	textarea	No	Only required for inverse transform. Paste the scaling parameters from a previous run.
hasHeader	checkbox	No	Treat first row as column headers
inverseTransform	checkbox	No	Apply inverse transformation using provided parameters
outputFormat	select	Yes	—
saveParams	checkbox	No	Include scaling parameters in the output for future inverse transformations
scaledOnly	checkbox	No	Skip detailed analysis and only output the scaled data

Response format

Text result

JSON

{
  "result": "Processed text content",
  "error": "Error message (optional)",
  "message": "Notification message (optional)",
  "metadata": {
    "key": "value"
  }
}

MCP integration

Add this tool to your Model Context Protocol server so AI agents can list and call it.

Server configuration

Add this block to your MCP client configuration:

mcp.json

{
  "mcpServers": {
    "elysiatools-feature-scaler": {
      "name": "feature-scaler",
      "description": "Scale and normalize features using various methods for machine learning preprocessing and data standardization",
      "baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=feature-scaler",
      "command": "",
      "args": [],
      "env": {},
      "isActive": true,
      "type": "sse"
    }
  }
}

List available tools

After connecting to the SSE endpoint, list the exposed tools:

tools/list

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/list"
}

Call this tool

Invoke the tool by its id, passing arguments built from its parameters:

tools/call

{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "feature-scaler",
    "arguments": {
  "csvData": "Enter your CSV data here...\n\nExample:\nFeature1,Feature2,Feature3,Label\n1.5,2.3,0.8,A\n3.2,1.1,2.5,B\n0.8,4.5,1.2,C\n2.1,3.4,1.8,A\n4.5,1.9,0.7,B",
  "scalingMethod": "standard",
  "selectedColumns": "Comma-separated column names. Leave empty to auto-detect numeric columns.",
  "scalingParams": "JSON format parameters for inverse transformation. Leave empty for forward transformation.",
  "hasHeader": true,
  "inverseTransform": false,
  "outputFormat": "csv",
  "saveParams": true,
  "scaledOnly": false
}
  }
}

Chain multiple tools in one session with a comma-separated toolId list, e.g. /mcp/sse?toolId=png-to-webp,jpg-to-webp,gif-to-webp (max 20).

Questions or issues? Contact [email protected]