Data Processing
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
Call this tool from your code in three languages.
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":true,"detectOutliers":true,"outlierThreshold":2}'Send a POST request with your inputs as JSON. File parameters require a separate upload first.
POST https://api.elysiatools.com/en/api/tools/data-zscore-normalizer| Name | Type | Required | Description |
|---|---|---|---|
| inputData | textarea | Yes | — |
| targetColumns | textarea | No | — |
| standardizationType | select | No | — |
| outputRange | text | No | Optional: Scale standardized values to target range. Leave empty for standard z-score output. |
| handleMissing | select | No | — |
| preserveOriginal | checkbox | No | — |
| decimalPlaces | number | No | — |
| includeStatistics | checkbox | No | — |
| detectOutliers | checkbox | No | — |
| outlierThreshold | number | No | Values beyond this many standard deviations will be flagged as outliers |
Text result
{
"result": "Processed text content",
"error": "Error message (optional)",
"message": "Notification message (optional)",
"metadata": {
"key": "value"
}
}Add this tool to your Model Context Protocol server so AI agents can list and call it.
Add this block to your MCP client configuration:
{
"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"
}
}
}After connecting to the SSE endpoint, list the exposed tools:
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/list"
}Invoke the tool by its id, passing arguments built from its parameters:
{
"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": true,
"detectOutliers": true,
"outlierThreshold": 2
}
}
}Questions or issues? Contact [email protected]