Data Processing
Advanced outlier detection and processing tool that identifies, removes, or replaces anomalous values in numerical data using multiple statistical methods. Perfect for data cleaning, statistical analysis, and machine learning data preparation. Features: - Multiple detection methods (IQR, Z-score, Modified Z-score, Isolation Forest) - Flexible handling strategies (Remove, Replace with mean/median/mode, Cap) - Automatic threshold optimization - Multi-dimensional outlier detection - Visual outlier statistics and reporting - Batch processing capabilities - Custom sensitivity levels - Comprehensive impact analysis Common Use Cases: - Data cleaning and preprocessing - Statistical analysis preparation - Machine learning dataset cleaning - Quality control in manufacturing - Financial anomaly detection - Sensor data validation
Call this tool from your code in three languages.
curl -X POST 'https://api.elysiatools.com/en/api/tools/data-outlier-processor' \
-H 'Content-Type: application/json' \
-d '{"inputData":"name,age,salary,score,temperature\nAlice,25,50000,85.2,36.5\nBob,32,75000,92.7,38.1\nCharlie,28,60000,78.9,37.2","targetColumns":"age, salary, score\n\nLeave empty to auto-detect numeric columns","detectionMethod":"iqr","threshold":1.5,"handlingStrategy":"remove","replacementMethod":"median","preserveOriginal":false,"markOutliers":true,"includeStatistics":true,"autoThreshold":false,"sensitivity":"medium"}'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-outlier-processor| Name | Type | Required | Description |
|---|---|---|---|
| inputData | textarea | Yes | — |
| targetColumns | textarea | No | — |
| detectionMethod | select | No | — |
| threshold | number | No | Sensitivity threshold for outlier detection. Lower values detect more outliers. |
| handlingStrategy | select | No | — |
| replacementMethod | select | No | — |
| preserveOriginal | checkbox | No | — |
| markOutliers | checkbox | No | Add columns to flag which values were detected as outliers |
| includeStatistics | checkbox | No | — |
| autoThreshold | checkbox | No | Automatically find optimal threshold based on data distribution |
| sensitivity | select | No | — |
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-outlier-processor": {
"name": "data-outlier-processor",
"description": "Advanced outlier detection and processing tool that identifies, removes, or replaces anomalous values in numerical data using multiple statistical methods. Perfect for data cleaning, statistical analysis, and machine learning data preparation.\n\nFeatures:\n- Multiple detection methods (IQR, Z-score, Modified Z-score, Isolation Forest)\n- Flexible handling strategies (Remove, Replace with mean/median/mode, Cap)\n- Automatic threshold optimization\n- Multi-dimensional outlier detection\n- Visual outlier statistics and reporting\n- Batch processing capabilities\n- Custom sensitivity levels\n- Comprehensive impact analysis\n\nCommon Use Cases:\n- Data cleaning and preprocessing\n- Statistical analysis preparation\n- Machine learning dataset cleaning\n- Quality control in manufacturing\n- Financial anomaly detection\n- Sensor data validation",
"baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=data-outlier-processor",
"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-outlier-processor",
"arguments": {
"inputData": "name,age,salary,score,temperature\nAlice,25,50000,85.2,36.5\nBob,32,75000,92.7,38.1\nCharlie,28,60000,78.9,37.2",
"targetColumns": "age, salary, score\n\nLeave empty to auto-detect numeric columns",
"detectionMethod": "iqr",
"threshold": 1.5,
"handlingStrategy": "remove",
"replacementMethod": "median",
"preserveOriginal": false,
"markOutliers": true,
"includeStatistics": true,
"autoThreshold": false,
"sensitivity": "medium"
}
}
}Questions or issues? Contact [email protected]