Data Analysis
综合标准差分析,包含变异性评估、置信区间和实用洞察
用三种语言从你的代码中调用此工具。
curl -X POST 'https://api.elysiatools.com/zh/api/tools/standard-deviation-analyzer' \
-H 'Content-Type: application/json' \
-d '{"dataInput":"输入您的数据值,用逗号或换行分隔...\n\n示例:\n- 低变异性:50, 51, 49, 52, 48, 50, 51, 49, 50, 52\n- 高变异性:20, 80, 35, 65, 45, 75, 25, 85, 15, 95\n- 中等:45, 52, 48, 58, 42, 55, 43, 57, 46, 54","dataFormat":"single","confidenceLevel":"0.95","includeOutliers":true,"detailedAnalysis":true}'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/standard-deviation-analyzer| 参数名 | 类型 | 必填 | 说明 |
|---|---|---|---|
| dataInput | textarea | 是 | — |
| dataFormat | select | 是 | — |
| confidenceLevel | select | 是 | — |
| includeOutliers | checkbox | 否 | 使用IQR方法识别和分析异常值 |
| detailedAnalysis | checkbox | 否 | 包含全面的解释和建议 |
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-standard-deviation-analyzer": {
"name": "standard-deviation-analyzer",
"description": "综合标准差分析,包含变异性评估、置信区间和实用洞察",
"baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=standard-deviation-analyzer",
"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": "standard-deviation-analyzer",
"arguments": {
"dataInput": "输入您的数据值,用逗号或换行分隔...\n\n示例:\n- 低变异性:50, 51, 49, 52, 48, 50, 51, 49, 50, 52\n- 高变异性:20, 80, 35, 65, 45, 75, 25, 85, 15, 95\n- 中等:45, 52, 48, 58, 42, 55, 43, 57, 46, 54",
"dataFormat": "single",
"confidenceLevel": "0.95",
"includeOutliers": true,
"detailedAnalysis": true
}
}
}有问题或反馈?请联系 [email protected]
文本结果
{
"result": "Processed text content",
"error": "Error message (optional)",
"message": "Notification message (optional)",
"metadata": {
"key": "value"
}
}