Optimized Memory MCP Server V2

MIT License
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# WebPerfect MCP Server

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.

## Features

### Advanced Image Processing Pipeline
1. Strong noise reduction using median filtering
2. Intelligent auto levels and curves based on image entropy
3. Advanced texture enhancement with modulation and sharpening
4. Smart resolution optimization (up to 4K)
5. Optimized WebP conversion

### Tools

#### `process_images`
Process and optimize a batch of images with advanced enhancements.
```typescript
{
  inputDir: string;      // Directory containing input images
  outputDir: string;     // Directory for optimized output
}
```

### Resources

#### Resource Templates
- `logs/{date}`: Access processing logs by date (YYYY-MM-DD)
  ```json
  {
    "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)
  ```json
  {
    "month": "2024-01",
    "totalImagesProcessed": 450,
    "averageCompressionRatio": "82%",
    "popularFormats": {
      "input": ["JPEG", "PNG"],
      "output": ["WebP"]
    },
    "totalStorageSaved": "150MB"
  }
  ```

#### Static Resources
- `stats/summary`: Overall processing statistics
  ```json
  {
    "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
  ```json
  {
    "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"]
      }
    }
  }
  ```

## Installation

1. Clone the repository:
```bash
git clone https://github.com/splendasucks/webperfect-mcp-server.git
cd webperfect-mcp-server
```

2. Install dependencies:
```bash
npm install
```

3. Build the server:
```bash
npm run build
```

## Usage with Claude

1. Add the server to your Claude MCP settings (typically in `claude_desktop_config.json`):
```json
{
  "mcpServers": {
    "webperfect": {
      "command": "node",
      "args": ["/path/to/webperfect-mcp-server/build/index.js"],
      "env": {}
    }
  }
}
```

2. Restart Claude to load the MCP server.

3. The server will be available through Claude's MCP tools and resources:
```typescript
// 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>
```

## Requirements

- Node.js >= 16
- Sharp image processing library
- Model Context Protocol SDK

## License

MIT