Data Analysis
上传 CSV 或 JSON 时间序列数据，基于 Z-Score 和 IQR 检测异常点，并输出带图表的报告
用三种语言从你的代码中调用此工具。
# 1) Request a presigned URL → returns { uploadUrl, storageKey }
curl -X POST 'https://api.elysiatools.com/api/upload/presign/time-series-anomaly-detector' \
-H 'Content-Type: application/json' \
-d '{"filename":"dataFile.ext","contentType":"application/octet-stream","size":12345}'
# 2) PUT the file bytes directly to the presigned uploadUrl
curl -X PUT '<presigned uploadUrl>' \
--data-binary @/path/to/file.ext
# 3) Call the tool, passing the returned storageKey for each file field
curl -X POST 'https://api.elysiatools.com/zh/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=uploads/2026/01/01/your-tool-1700000000000-abc123.ext' \
-F 'timestampColumn=timestamp' \
-F 'valueColumn=value' \
-F 'detectionMethod=both' \
-F 'zScoreThreshold=2.5' \
-F 'seasonalityWindow=0'以 JSON 形式 POST 提交输入参数。文件类型参数需先单独上传。
POST https://api.elysiatools.com/zh/api/tools/time-series-anomaly-detector| 参数名 | 类型 | 必填 | 说明 |
|---|---|---|---|
| rawInput | textarea | 否 | — |
| dataFile | file需先上传 | 否 | — |
| timestampColumn | text | 否 | — |
| valueColumn | text | 否 | — |
| detectionMethod | select | 否 | — |
| zScoreThreshold | number | 否 | — |
| seasonalityWindow | number | 否 | — |
HTML 结果
{
"result": "<div>Processed HTML content</div>",
"error": "Error message (optional)",
"message": "Notification message (optional)",
"metadata": {
"key": "value"
}
}将此工具加入你的 Model Context Protocol 服务，让 AI 智能体可以列出并调用它。
将以下内容加入你的 MCP 客户端配置：
{
"mcpServers": {
"elysiatools-time-series-anomaly-detector": {
"name": "time-series-anomaly-detector",
"description": "上传 CSV 或 JSON 时间序列数据，基于 Z-Score 和 IQR 检测异常点，并输出带图表的报告",
"baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=time-series-anomaly-detector",
"command": "",
"args": [],
"env": {},
"isActive": true,
"type": "sse"
}
}
}连接到 SSE 端点后，列出已开放的工具：
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/list"
}通过工具 id 调用，参数由其参数表构建：
{
"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
}
}
}有问题或反馈？请联系 [email protected]