Design
应用拉普拉斯算子进行边缘检测、锐化和图像增强,支持不同核变体
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/image-laplacian' \
-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/image-laplacian' \
-F 'imageFile=uploads/2026/01/01/your-tool-1700000000000-abc123.ext' \
-F 'kernelType=standard' \
-F 'intensity=1' \
-F 'iterations=1' \
-F 'threshold=128' \
-F 'outputMode=edges'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/image-laplacian| 参数名 | 类型 | 必填 | 说明 |
|---|---|---|---|
| imageFile | file需先上传 | 是 | 应用拉普拉斯算子的图片 |
| kernelType | select | 否 | 要应用的拉普拉斯核类型 |
| intensity | number | 否 | 拉普拉斯效果的强度(0.1-5.0) |
| iterations | number | 否 | 拉普拉斯应用次数 |
| threshold | number | 否 | 边缘检测阈值(0-255) |
| outputMode | select | 否 | 如何渲染拉普拉斯结果 |
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-image-laplacian": {
"name": "image-laplacian",
"description": "应用拉普拉斯算子进行边缘检测、锐化和图像增强,支持不同核变体",
"baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=image-laplacian",
"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": "image-laplacian",
"arguments": {
"imageFile": "https://example.com/file.ext",
"kernelType": "standard",
"intensity": 1,
"iterations": 1,
"threshold": 128,
"outputMode": "edges"
}
}
}有问题或反馈?请联系 [email protected]