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Image Custom Convolution Kernel

Apply custom convolution kernel by defining your own matrix values for personalized image effects and processing

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-custom-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-custom-kernel' \
  -F 'imageFile=uploads/2026/01/01/your-tool-1700000000000-abc123.ext' \
  -F 'kernelSize=3' \
  -F 'kernelValues=Enter comma-separated values (e.g., 1, 2, 1, 2, 4, 2, 1, 2, 1 for 3x3)' \
  -F 'intensity=1' \
  -F 'normalize=true' \
  -F 'outputMode=normal'

© 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-custom-kernel

Parameters

NameTypeRequiredDescription
imageFilefileupload requiredYesImage to apply custom convolution kernel
kernelSizeselectNoSize of the convolution matrix
kernelValuestextareaYesMatrix values (left to right, top to bottom). For 3x3: 9 values, for 5x5: 25 values, etc.
intensitynumberNoStrength of the convolution effect (0.1-10.0)
normalizecheckboxNoNormalize kernel so that sum of all elements equals 1 (except for edge detection)
outputModeselectNoAdditional processing on the output
File-type parameters must be uploaded first via POST /upload/image-custom-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-custom-kernel": {
      "name": "image-custom-kernel",
      "description": "Apply custom convolution kernel by defining your own matrix values for personalized image effects and processing",
      "baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=image-custom-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-custom-kernel",
    "arguments": {
  "imageFile": "https://example.com/file.ext",
  "kernelSize": "3",
  "kernelValues": "Enter comma-separated values (e.g., 1, 2, 1, 2, 4, 2, 1, 2, 1 for 3x3)",
  "intensity": 1,
  "normalize": true,
  "outputMode": "normal"
}
  }
}
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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