Design

Image Prewitt Edge Detection

Apply Prewitt operator for edge detection to identify boundaries and gradients in images

file· File resultOpen tool

Quick start

Call this tool from your code in three languages.

cURL

# 1) Request a presigned URL → returns { uploadUrl, storageKey }
curl -X POST 'https://api.elysiatools.com/api/upload/presign/image-prewitt' \
  -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/en/api/tools/image-prewitt' \
  -F 'imageFile=uploads/2026/01/01/your-tool-1700000000000-abc123.ext' \
  -F 'direction=both' \
  -F 'threshold=0' \
  -F 'outputMode=grayscale' \
  -F 'normalize=true'

API reference

Send a POST request with your inputs as JSON. File parameters require a separate upload first.

Endpoint

HTTP

POST https://api.elysiatools.com/en/api/tools/image-prewitt

Parameters

Name	Type	Required	Description
imageFile	fileupload required	Yes	Image to apply Prewitt edge detection
direction	select	No	Direction of edge detection to apply
threshold	number	No	Threshold for edge detection (0-255). Higher values detect only strong edges
outputMode	select	No	How to render the edge detection results
normalize	checkbox	No	Normalize the output to enhance edge visibility

File-type parameters must be uploaded first via POST /upload/image-prewitt, then the returned filePath is passed to the corresponding file field.

Response format

File result

JSON

{
  "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)"
}

MCP integration

Add this tool to your Model Context Protocol server so AI agents can list and call it.

Server configuration

Add this block to your MCP client configuration:

mcp.json

{
  "mcpServers": {
    "elysiatools-image-prewitt": {
      "name": "image-prewitt",
      "description": "Apply Prewitt operator for edge detection to identify boundaries and gradients in images",
      "baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=image-prewitt",
      "command": "",
      "args": [],
      "env": {},
      "isActive": true,
      "type": "sse"
    }
  }
}

List available tools

After connecting to the SSE endpoint, list the exposed tools:

tools/list

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/list"
}

Call this tool

Invoke the tool by its id, passing arguments built from its parameters:

tools/call

{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "image-prewitt",
    "arguments": {
  "imageFile": "https://example.com/file.ext",
  "direction": "both",
  "threshold": 0,
  "outputMode": "grayscale",
  "normalize": true
}
  }
}

Chain multiple tools in one session with a comma-separated toolId list, e.g. /mcp/sse?toolId=png-to-webp,jpg-to-webp,gif-to-webp (max 20).

File parameters accept a public file URL only (https://…). Local paths, data: URIs, and Base64 strings are not supported.

Questions or issues? Contact [email protected]