Data Analysis
Advanced correlation analysis tool that calculates correlation coefficients between variables to measure the strength and direction of their linear relationships. Perfect for statistical analysis, financial modeling, scientific research, and data exploration. Features: - Multiple correlation methods (Pearson, Spearman, Kendall) - Correlation matrix generation - Statistical significance testing (p-values) - Confidence intervals calculation - Heatmap visualization - Scatter plot matrix generation - Missing value handling strategies - Outlier detection and handling - Group analysis capabilities - Detailed statistical reports Common Use Cases: - Financial market analysis and risk assessment - Scientific research and hypothesis testing - Customer behavior and marketing analysis - Healthcare and medical data analysis - Quality control and process optimization - Educational performance evaluation
Call this tool from your code in three languages.
curl -X POST 'https://api.elysiatools.com/en/api/tools/correlation-analyzer' \
-H 'Content-Type: application/json' \
-d '{"inputData":"stock_price,market_cap,volume,pe_ratio,eps\n150.25,2500000000,5000000,25.5,12.8\n148.80,2450000000,4800000,24.8,11.2\n152.30,2580000000,5200000,26.2,13.5\n145.90,2420000000,4900000,23.9,11.8","targetColumns":"stock_price, market_cap, volume, pe_ratio\n\nLeave empty to auto-detect numeric columns","correlationMethod":"pearson","significanceLevel":"0.05","handleMissing":"remove","outlierMethod":"none","confidenceInterval":true,"groupColumn":"category, region, or department","generateHeatmap":true,"generateScatterPlots":true,"includeStatistics":true,"showPValues":true,"showConfidenceIntervals":true,"decimalPlaces":3}'Send a POST request with your inputs as JSON. File parameters require a separate upload first.
POST https://api.elysiatools.com/en/api/tools/correlation-analyzer| Name | Type | Required | Description |
|---|---|---|---|
| inputData | textarea | Yes | — |
| targetColumns | textarea | No | — |
| correlationMethod | select | No | — |
| significanceLevel | select | No | — |
| handleMissing | select | No | — |
| outlierMethod | select | No | — |
| confidenceInterval | checkbox | No | — |
| groupColumn | text | No | Column to group analysis by (e.g., category, region, department) |
| generateHeatmap | checkbox | No | — |
| generateScatterPlots | checkbox | No | — |
| includeStatistics | checkbox | No | — |
| showPValues | checkbox | No | — |
| showConfidenceIntervals | checkbox | No | — |
| decimalPlaces | number | No | Number of decimal places for correlation coefficients |
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-correlation-analyzer": {
"name": "correlation-analyzer",
"description": "Advanced correlation analysis tool that calculates correlation coefficients between variables to measure the strength and direction of their linear relationships. Perfect for statistical analysis, financial modeling, scientific research, and data exploration.\n\nFeatures:\n- Multiple correlation methods (Pearson, Spearman, Kendall)\n- Correlation matrix generation\n- Statistical significance testing (p-values)\n- Confidence intervals calculation\n- Heatmap visualization\n- Scatter plot matrix generation\n- Missing value handling strategies\n- Outlier detection and handling\n- Group analysis capabilities\n- Detailed statistical reports\n\nCommon Use Cases:\n- Financial market analysis and risk assessment\n- Scientific research and hypothesis testing\n- Customer behavior and marketing analysis\n- Healthcare and medical data analysis\n- Quality control and process optimization\n- Educational performance evaluation",
"baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=correlation-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": "correlation-analyzer",
"arguments": {
"inputData": "stock_price,market_cap,volume,pe_ratio,eps\n150.25,2500000000,5000000,25.5,12.8\n148.80,2450000000,4800000,24.8,11.2\n152.30,2580000000,5200000,26.2,13.5\n145.90,2420000000,4900000,23.9,11.8",
"targetColumns": "stock_price, market_cap, volume, pe_ratio\n\nLeave empty to auto-detect numeric columns",
"correlationMethod": "pearson",
"significanceLevel": "0.05",
"handleMissing": "remove",
"outlierMethod": "none",
"confidenceInterval": true,
"groupColumn": "category, region, or department",
"generateHeatmap": true,
"generateScatterPlots": true,
"includeStatistics": true,
"showPValues": true,
"showConfidenceIntervals": true,
"decimalPlaces": 3
}
}
}Questions or issues? Contact [email protected]