Elysia Tools Docs
EN
Language
ZHESPTRUFRDEEN
Back to site
Browse tools

Data Processing

Data Outlier Processor

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

text· Text resultOpen tool

Quick start

Call this tool from your code in three languages.

cURL
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":false,"autoThreshold":false,"sensitivity":"medium"}'

© 2026 Elysia Tools. Developer documentation.

Back to siteBrowse tools

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-outlier-processor

Parameters

NameTypeRequiredDescription
inputDatatextareaYes—
targetColumnstextareaNo—
detectionMethodselectNo—
thresholdnumberNoSensitivity threshold for outlier detection. Lower values detect more outliers.
handlingStrategyselectNo—
replacementMethodselectNo—
preserveOriginalcheckboxNo—
markOutlierscheckboxNoAdd columns to flag which values were detected as outliers
includeStatisticscheckboxNo—
autoThresholdcheckboxNoAutomatically find optimal threshold based on data distribution
sensitivityselectNo—

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-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"
    }
  }
}

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-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": false,
  "autoThreshold": false,
  "sensitivity": "medium"
}
  }
}
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]

Browse tools
Open tool