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MCP-compliant image augmentation server using Albumentations

Reason this release was yanked:

breaks MCP stdio handshake

Project description

🚧 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
  • get_quick_transform_reference - Condensed transform keywords for prompting
  • get_getting_started_guide - Structured workflow guide for first-time assistants

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
  • getting_started_guide - Same content as the tool version, resource-style

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")

# Process from URL (two-step)
session = load_image_for_processing(image_source="https://example.com/image.jpg")
# Use the returned session_id from the previous call
augment_image(session_id="<session_id>", prompt="add blur and rotate 10 degrees")

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
  • Multi-Source Input - Works with local file paths, base64 payloads, and URLs (via loader)

Documentation

Configuration Files

License

MIT License - see LICENSE for details.

Contact: ramsi.kalia@gmail.com

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