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 presetslist_available_transforms- Get supported transforms and parametersvalidate_prompt- Test prompts without processing imageslist_available_presets- Get available preset configurationsset_default_seed- Set global seed for reproducible resultsget_pipeline_status- Check pipeline health and configurationget_quick_transform_reference- Condensed transform keywords for promptingget_getting_started_guide- Structured workflow guide for first-time assistants
Available Prompts
compose_preset- Generate augmentation policies from presets with optional tweaksexplain_effects- Analyze pipeline effects in plain Englishaugmentation_parser- Parse natural language to structured transformsvision_verification- Compare original and augmented imageserror_handler- Generate user-friendly error messages and recovery suggestions
Available Resources
transforms_guide- Complete transform documentation with parameters and rangespolicy_presets- Built-in preset configurations (segmentation, portrait, lowlight)available_transforms_examples- Usage examples and patterns organized by categoriespreset_pipelines_best_practices- Best practices guide for augmentation workflowstroubleshooting_common_issues- Common issues, solutions, and diagnostic stepsgetting_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
- Installation & Setup
- Architecture Overview
- Purpose & Rationale
- Preset Configurations
- Session Folders (outputs/) Guide
- Regex Security Analysis
- Known Issues
- Design Philosophy
- Usage Examples
- Troubleshooting
- Contributing
Configuration Files
License
MIT License - see LICENSE for details.
Contact: ramsi.kalia@gmail.com
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