albumentations-mcp 0.1.0b4

MCP-compliant image augmentation server using Albumentations

🚧 Work in Progress (Beta Testing ongoing)

Albumentations-MCP

Natural language image augmentation via MCP protocol. Transform images using plain English with this MCP-compliant server built on Albumentations.

Example: "add blur and rotate 15 degrees" → Applies GaussianBlur + Rotate transforms automatically

Quick Start

# Install from PyPI
pip install albumentations-mcp

# Run as MCP server
uvx albumentations-mcp

MCP Client Setup

Claude Desktop

Copy claude-desktop-config.json to ~/.claude_desktop_config.json

Or add manually:

{
  "mcpServers": {
    "albumentations": {
      "command": "uvx",
      "args": ["albumentations-mcp"],
      "env": {
        "MCP_LOG_LEVEL": "INFO",
        "OUTPUT_DIR": "./outputs",
        "ENABLE_VISION_VERIFICATION": "true",
        "DEFAULT_SEED": "42"
      }
    }
  }
}

Kiro IDE

Copy kiro-mcp-config.json to .kiro/settings/mcp.json

Or add manually:

{
  "mcpServers": {
    "albumentations": {
      "command": "uvx",
      "args": ["albumentations-mcp"],
      "env": {
        "MCP_LOG_LEVEL": "INFO",
        "OUTPUT_DIR": "./outputs",
        "ENABLE_VISION_VERIFICATION": "true",
        "DEFAULT_SEED": "42"
      },
      "disabled": false,
      "autoApprove": ["augment_image", "list_available_transforms"]
    }
  }
}

Available Tools

• augment_image - Apply augmentations using natural language or presets
• list_available_transforms - Get supported transforms and parameters
• validate_prompt - Test prompts without processing images
• list_available_presets - Get available preset configurations
• set_default_seed - Set global seed for reproducible results
• get_pipeline_status - Check pipeline health and configuration

Available Prompts

• compose_preset - Generate augmentation policies from presets with optional tweaks
• explain_effects - Analyze pipeline effects in plain English
• augmentation_parser - Parse natural language to structured transforms
• vision_verification - Compare original and augmented images
• error_handler - Generate user-friendly error messages and recovery suggestions

Available Resources

• transforms_guide - Complete transform documentation with parameters and ranges
• policy_presets - Built-in preset configurations (segmentation, portrait, lowlight)
• available_transforms_examples - Usage examples and patterns organized by categories
• preset_pipelines_best_practices - Best practices guide for augmentation workflows
• troubleshooting_common_issues - Common issues, solutions, and diagnostic steps

Usage Examples

# Simple augmentation
augment_image(
    image_path="photo.jpg",
    prompt="add blur and rotate 15 degrees"
)

# Using presets
augment_image(
    image_path="dataset/image.jpg",
    preset="segmentation"
)

# Test prompts
validate_prompt(prompt="increase brightness and add noise")

Features

• Natural Language Processing - Convert English descriptions to transforms
• Preset Pipelines - Pre-configured transforms for common use cases
• Reproducible Results - Seeding support for consistent outputs
• MCP Protocol Compliant - Full MCP implementation with tools, prompts, and resources
• Comprehensive Documentation - Built-in guides, examples, and troubleshooting resources
• Production Ready - Comprehensive testing, error handling, and structured logging

Documentation

• Installation & Setup
• API Reference
• Usage Examples
• Troubleshooting
• Contributing

Configuration Files

• Claude Desktop Config
• Kiro IDE Config
• All Configuration Examples

License

MIT License - see LICENSE for details.

Contact: [e-mail]