AI Tools
使用 MiniMax M3 识别 PNG 表格截图，并将重建后的网格导出为 Excel
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/png-table-screenshot-to-excel' \
-H 'Content-Type: application/json' \
-d '{"filename":"imageFile.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/png-table-screenshot-to-excel' \
-F 'imageFile=uploads/2026/01/01/your-tool-1700000000000-abc123.ext' \
-F 'outputFileName=table-from-screenshot' \
-F 'hasHeader=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/png-table-screenshot-to-excel| 参数名 | 类型 | 必填 | 说明 |
|---|---|---|---|
| imageFile | file需先上传 | 是 | — |
| outputFileName | text | 否 | — |
| hasHeader | checkbox | 否 | — |
文件结果
{
"filePath": "/public/processing/randomid.ext",
"fileName": "output.ext",
"contentType": "application/octet-stream",
"size": 1024,
"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-png-table-screenshot-to-excel": {
"name": "png-table-screenshot-to-excel",
"description": "使用 MiniMax M3 识别 PNG 表格截图，并将重建后的网格导出为 Excel",
"baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=png-table-screenshot-to-excel",
"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": "png-table-screenshot-to-excel",
"arguments": {
"imageFile": "https://example.com/file.ext",
"outputFileName": "table-from-screenshot",
"hasHeader": true
}
}
}有问题或反馈？请联系 [email protected]