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Data Processing

Z-Score Standardizer

Standardize numerical data using Z-score (standard score) normalization to transform values with mean=0 and standard deviation=1. Perfect for statistical analysis, machine learning feature preprocessing, outlier detection, and data comparison across different scales. Features: - Z-score standardization (mean=0, std=1) - Robust Z-score option (using median and MAD) - Custom scaling to target range - Multiple column selection - Automatic data type detection - Handles missing values intelligently - Preserves non-numeric columns - Comprehensive statistical summary - Outlier detection and reporting Common Use Cases: - Machine learning feature preparation - Statistical hypothesis testing - Outlier detection and removal - Data comparison across different units - Principal Component Analysis (PCA) preprocessing

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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/data-zscore-normalizer' \
  -H 'Content-Type: application/json' \
  -d '{"inputData":"name,age,salary,score\nAlice,25,50000,85.5\nBob,30,75000,92.3\nCharlie,35,120000,78.9","targetColumns":"age, salary or name, email\n\nLeave empty to auto-detect numeric columns","standardizationType":"zscore","outputRange":"0, 1","handleMissing":"skip","preserveOriginal":false,"decimalPlaces":4,"includeStatistics":false,"detectOutliers":true,"outlierThreshold":2}'

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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/data-zscore-normalizer

Parameters

NameTypeRequiredDescription
inputDatatextareaYes—
targetColumnstextareaNo—
standardizationTypeselectNo—
outputRangetextNoOptional: Scale standardized values to target range. Leave empty for standard z-score output.
handleMissingselectNo—
preserveOriginalcheckboxNo—
decimalPlacesnumberNo—
includeStatisticscheckboxNo—
detectOutlierscheckboxNo—
outlierThresholdnumberNoValues beyond this many standard deviations will be flagged as outliers

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-data-zscore-normalizer": {
      "name": "data-zscore-normalizer",
      "description": "Standardize numerical data using Z-score (standard score) normalization to transform values with mean=0 and standard deviation=1. Perfect for statistical analysis, machine learning feature preprocessing, outlier detection, and data comparison across different scales.\n\nFeatures:\n- Z-score standardization (mean=0, std=1)\n- Robust Z-score option (using median and MAD)\n- Custom scaling to target range\n- Multiple column selection\n- Automatic data type detection\n- Handles missing values intelligently\n- Preserves non-numeric columns\n- Comprehensive statistical summary\n- Outlier detection and reporting\n\nCommon Use Cases:\n- Machine learning feature preparation\n- Statistical hypothesis testing\n- Outlier detection and removal\n- Data comparison across different units\n- Principal Component Analysis (PCA) preprocessing",
      "baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=data-zscore-normalizer",
      "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": "data-zscore-normalizer",
    "arguments": {
  "inputData": "name,age,salary,score\nAlice,25,50000,85.5\nBob,30,75000,92.3\nCharlie,35,120000,78.9",
  "targetColumns": "age, salary or name, email\n\nLeave empty to auto-detect numeric columns",
  "standardizationType": "zscore",
  "outputRange": "0, 1",
  "handleMissing": "skip",
  "preserveOriginal": false,
  "decimalPlaces": 4,
  "includeStatistics": false,
  "detectOutliers": true,
  "outlierThreshold": 2
}
  }
}
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).

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