Data Analysis
Upload CSV or JSON time series data, detect anomalies with Z-Score and IQR methods, and return a chart-backed report
Call this tool from your code in three languages.
# 1) Upload each file first → returns { filePath }
curl -X POST 'https://api.elysiatools.com/upload/time-series-anomaly-detector' \
-F 'file=@/path/to/dataFile.ext'
# 2) Call the tool with the returned filePath values
curl -X POST 'https://api.elysiatools.com/en/api/tools/time-series-anomaly-detector' \
-F 'rawInput=timestamp,value
2026-03-01,110
2026-03-02,112
2026-03-03,109
2026-03-04,315
2026-03-05,111' \
-F 'dataFile=/path/to/file.ext' \
-F 'timestampColumn=timestamp' \
-F 'valueColumn=value' \
-F 'detectionMethod=both' \
-F 'zScoreThreshold=2.5' \
-F 'seasonalityWindow=0'Send a POST request with your inputs as JSON. File parameters require a separate upload first.
POST https://api.elysiatools.com/en/api/tools/time-series-anomaly-detector| Name | Type | Required | Description |
|---|---|---|---|
| rawInput | textarea | No | — |
| dataFile | fileupload required | No | — |
| timestampColumn | text | No | — |
| valueColumn | text | No | — |
| detectionMethod | select | No | — |
| zScoreThreshold | number | No | — |
| seasonalityWindow | number | No | — |
HTML result
{
"result": "<div>Processed HTML content</div>",
"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-time-series-anomaly-detector": {
"name": "time-series-anomaly-detector",
"description": "Upload CSV or JSON time series data, detect anomalies with Z-Score and IQR methods, and return a chart-backed report",
"baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=time-series-anomaly-detector",
"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": "time-series-anomaly-detector",
"arguments": {
"rawInput": "timestamp,value\n2026-03-01,110\n2026-03-02,112\n2026-03-03,109\n2026-03-04,315\n2026-03-05,111",
"dataFile": "https://example.com/file.ext",
"timestampColumn": "timestamp",
"valueColumn": "value",
"detectionMethod": "both",
"zScoreThreshold": 2.5,
"seasonalityWindow": 0
}
}
}Questions or issues? Contact [email protected]