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Image Sharpen Kernel

Apply sharpen convolution kernel to enhance image clarity and edge definition

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-sharpen-kernel' \
  -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-sharpen-kernel' \
  -F 'imageFile=uploads/2026/01/01/your-tool-1700000000000-abc123.ext' \
  -F 'kernelType=basic' \
  -F 'intensity=1' \
  -F 'iterations=1' \
  -F 'format=original' \
  -F 'quality=95'

© 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-sharpen-kernel

Parameters

NameTypeRequiredDescription
imageFilefileupload requiredYesImage to apply sharpen kernel
kernelTypeselectNoType of sharpen kernel to apply
intensitynumberNoStrength of the sharpen effect (0.1-5.0)
iterationsnumberNoNumber of times to apply the kernel
formatselectNoOutput format for the processed image
qualitynumberNoOutput quality for lossy formats
File-type parameters must be uploaded first via POST /upload/image-sharpen-kernel, 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-sharpen-kernel": {
      "name": "image-sharpen-kernel",
      "description": "Apply sharpen convolution kernel to enhance image clarity and edge definition",
      "baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=image-sharpen-kernel",
      "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-sharpen-kernel",
    "arguments": {
  "imageFile": "https://example.com/file.ext",
  "kernelType": "basic",
  "intensity": 1,
  "iterations": 1,
  "format": "original",
  "quality": 95
}
  }
}
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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