Design
Apply gradient operations (Sobel, Prewitt, Roberts) for edge detection and image enhancement
Call this tool from your code in three languages.
# 1) Upload each file first → returns { filePath }
curl -X POST 'https://api.elysiatools.com/upload/image-gradient' \
-F 'file=@/path/to/imageFile.ext'
# 2) Call the tool with the returned filePath values
curl -X POST 'https://api.elysiatools.com/en/api/tools/image-gradient' \
-F 'imageFile=/path/to/file.ext' \
-F 'gradientType=sobel' \
-F 'direction=both' \
-F 'threshold=0' \
-F 'outputMode=edges'Send a POST request with your inputs as JSON. File parameters require a separate upload first.
POST https://api.elysiatools.com/en/api/tools/image-gradient| Name | Type | Required | Description |
|---|---|---|---|
| imageFile | fileupload required | Yes | Image to apply gradient operation for edge detection |
| gradientType | select | No | Type of gradient operation to apply |
| direction | select | No | Direction of gradient computation |
| threshold | number | No | Threshold for edge detection (0-255). Higher values detect only strong edges |
| outputMode | select |
Add this tool to your Model Context Protocol server so AI agents can list and call it.
Add this block to your MCP client configuration:
{
"mcpServers": {
"elysiatools-image-gradient": {
"name": "image-gradient",
"description": "Apply gradient operations (Sobel, Prewitt, Roberts) for edge detection and image enhancement",
"baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=image-gradient",
"command": "",
"args": [],
"env": {},
"isActive": true,
"type": "sse"
}
}
}After connecting to the SSE endpoint, list the exposed tools:
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/list"
}Invoke the tool by its id, passing arguments built from its parameters:
{
"jsonrpc": "2.0",
"id": 2,
"method": "tools/call",
"params": {
"name": "image-gradient",
"arguments": {
"imageFile": "https://example.com/file.ext",
"gradientType": "sobel",
"direction": "both",
"threshold": 0,
"outputMode": "edges"
}
}
}| No |
| How to render the gradient results |
File result
{
"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)"
}Questions or issues? Contact [email protected]