Document Tools
根据 PDF 字数、分页节奏和章节拆分估算阅读时长
用三种语言从你的代码中调用此工具。
# 1) Upload each file first → returns { filePath }
curl -X POST 'https://api.elysiatools.com/upload/pdf-reading-time-estimator' \
-F 'file=@/path/to/sourceFile.ext'
# 2) Call the tool with the returned filePath values
curl -X POST 'https://api.elysiatools.com/zh/api/tools/pdf-reading-time-estimator' \
-F 'sourceFile=/public/samples/pdf/sample-multipage.pdf' \
-F 'wordsPerMinute=200' \
-F 'includePageBreakdown=true' \
-F 'includeSections=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/pdf-reading-time-estimator| 参数名 | 类型 | 必填 | 说明 |
|---|---|---|---|
| sourceFile | file需先上传 | 是 | — |
| wordsPerMinute | number | 否 | — |
| includePageBreakdown | checkbox | 否 | — |
| includeSections | checkbox | 否 | — |
JSON 结果
{
"key": {...},
"metadata": {
"key": "value"
},
"error": "Error message (optional)",
"message": "Notification message (optional)"
}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-pdf-reading-time-estimator": {
"name": "pdf-reading-time-estimator",
"description": "根据 PDF 字数、分页节奏和章节拆分估算阅读时长",
"baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=pdf-reading-time-estimator",
"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": "pdf-reading-time-estimator",
"arguments": {
"sourceFile": "/public/samples/pdf/sample-multipage.pdf",
"wordsPerMinute": 200,
"includePageBreakdown": true,
"includeSections": true
}
}
}有问题或反馈？请联系 [email protected]