Data Analysis
Comprehensive data distribution analysis with normality tests, outlier detection, and goodness-of-fit assessments
Call this tool from your code in three languages.
curl -X POST 'https://api.elysiatools.com/en/api/tools/distribution-analyzer' \
-H 'Content-Type: application/json' \
-d '{"dataInput":"Enter your data values separated by commas or new lines...\n\nExamples:\n- Normal: 50, 51, 49, 52, 48, 50, 51, 49, 50, 52\n- Skewed: 45, 48, 50, 52, 55, 58, 60, 65, 70, 80\n- Bimodal: 20, 25, 30, 35, 40, 60, 65, 70, 75, 80","dataFormat":"single","significanceLevel":"0.05","includeHistogram":true,"testNormality":true,"detectOutliers":true}'Send a POST request with your inputs as JSON. File parameters require a separate upload first.
POST https://api.elysiatools.com/en/api/tools/distribution-analyzer| Name | Type | Required | Description |
|---|---|---|---|
| dataInput | textarea | Yes | — |
| dataFormat | select | Yes | — |
| significanceLevel | select | Yes | — |
| includeHistogram | checkbox | No | Generate frequency distribution and percentile information |
| testNormality | checkbox | No | Perform Anderson-Darling, Shapiro-Wilk, and Jarque-Bera tests |
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-distribution-analyzer": {
"name": "distribution-analyzer",
"description": "Comprehensive data distribution analysis with normality tests, outlier detection, and goodness-of-fit assessments",
"baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=distribution-analyzer",
"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": "distribution-analyzer",
"arguments": {
"dataInput": "Enter your data values separated by commas or new lines...\n\nExamples:\n- Normal: 50, 51, 49, 52, 48, 50, 51, 49, 50, 52\n- Skewed: 45, 48, 50, 52, 55, 58, 60, 65, 70, 80\n- Bimodal: 20, 25, 30, 35, 40, 60, 65, 70, 75, 80",
"dataFormat": "single",
"significanceLevel": "0.05",
"includeHistogram": true,
"testNormality": true,
"detectOutliers": true
}
}
}| detectOutliers | checkbox | No | Identify outliers using multiple methods (IQR, Z-score, and robust statistics) |
Text result
{
"result": "Processed text content",
"error": "Error message (optional)",
"message": "Notification message (optional)",
"metadata": {
"key": "value"
}
}Questions or issues? Contact [email protected]