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Image Laplacian Operator

Apply Laplacian operator for edge detection, sharpening, and image enhancement with different kernel variants

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-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/en/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'

© 2026 Elysia Tools. Developer documentation.

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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-laplacian

Parameters

NameTypeRequiredDescription
imageFilefileupload requiredYesImage to apply Laplacian operator
kernelTypeselectNoType of Laplacian kernel to apply
intensitynumberNoStrength of the Laplacian effect (0.1-5.0)
iterationsnumberNoNumber of Laplacian applications
thresholdnumberNoThreshold for edge detection (0-255)
outputModeselectNoHow to render the Laplacian results
File-type parameters must be uploaded first via POST /upload/image-laplacian, 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-laplacian": {
      "name": "image-laplacian",
      "description": "Apply Laplacian operator for edge detection, sharpening, and image enhancement with different kernel variants",
      "baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=image-laplacian",
      "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-laplacian",
    "arguments": {
  "imageFile": "https://example.com/file.ext",
  "kernelType": "standard",
  "intensity": 1,
  "iterations": 1,
  "threshold": 128,
  "outputMode": "edges"
}
  }
}
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]

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