An intelligent MCP server with a fully automated batch pipeline for web-ready images. Features include noise reduction, auto levels/curves, JPEG artifact removal, 4K resizing, smart sharpening with shadow/highlight enhancement, and advanced WebP conversion. Optimized compression delivers smaller files without sacrificing quality.
- Strong noise reduction using median filtering
- Intelligent auto levels and curves based on image entropy
- Advanced texture enhancement with modulation and sharpening
- Smart resolution optimization (up to 4K)
- Optimized WebP conversion
Process and optimize a batch of images with advanced enhancements.
{
inputDir: string; // Directory containing input images
outputDir: string; // Directory for optimized output
}
-
logs/{date}
: Access processing logs by date (YYYY-MM-DD){ "date": "2024-01-20", "entries": [{ "timestamp": "2024-01-20T10:00:00Z", "imagesProcessed": 15, "totalInputSize": "5.2MB", "totalOutputSize": "1.1MB", "compressionRatio": "78.8%", "averageProcessingTime": "1.2s" }] }
-
stats/monthly/{month}
: Monthly statistics (YYYY-MM){ "month": "2024-01", "totalImagesProcessed": 450, "averageCompressionRatio": "82%", "popularFormats": { "input": ["JPEG", "PNG"], "output": ["WebP"] }, "totalStorageSaved": "150MB" }
-
stats/summary
: Overall processing statistics{ "totalImagesProcessed": 5280, "averageCompressionRatio": "81%", "totalStorageSaved": "1.8GB", "popularEnhancements": [ "noise_reduction", "auto_levels_curves", "texture_enhancement" ], "performanceMetrics": { "averageProcessingTime": "1.5s", "peakThroughput": "45 images/minute" } }
-
config/optimization-presets
: Available optimization presets{ "presets": { "web_standard": { "maxWidth": 1920, "format": "webp", "quality": 85, "enhancements": ["noise_reduction", "auto_levels_curves"] }, "web_high_quality": { "maxWidth": 3840, "format": "webp", "quality": 90, "enhancements": [ "noise_reduction", "auto_levels_curves", "texture_enhancement" ] }, "thumbnail": { "maxWidth": 400, "format": "webp", "quality": 80, "enhancements": ["noise_reduction"] } } }
- Clone the repository:
git clone https://github.com/splendasucks/webperfect-mcp-server.git
cd webperfect-mcp-server
- Install dependencies:
bun install
- Build the server:
bun run build
- Add the server to your Claude MCP settings (typically in
claude_desktop_config.json
):
{
"mcpServers": {
"webperfect": {
"command": "bun",
"args": ["/path/to/webperfect-mcp-server/build/index.js"],
"env": {}
}
}
}
-
Restart Claude to load the MCP server.
-
The server will be available through Claude's MCP tools and resources:
// Process a batch of images
<use_mcp_tool>
<server_name>webperfect</server_name>
<tool_name>process_images</tool_name>
<arguments>
{
"inputDir": "/path/to/input",
"outputDir": "/path/to/output"
}
</arguments>
</use_mcp_tool>
// Access processing statistics
<access_mcp_resource>
<server_name>webperfect</server_name>
<uri>stats/summary</uri>
</access_mcp_resource>
- Bun >= 1.0
- Sharp image processing library
- Model Context Protocol SDK
MIT