Command Line Interface

KINTSUGI provides a command-line interface for common operations.

Commands

kintsugi check

Check all dependencies and report their status.

kintsugi check
kintsugi check --verbose  # More detailed output
kintsugi check --json     # Output as JSON

kintsugi info

Display system information and KINTSUGI version.

kintsugi info

kintsugi template

Generate a configuration template file.

kintsugi template -o config.json
kintsugi template --output my_config.json

kintsugi register

Run the registration workflow.

# Dry run (show what would be done)
kintsugi register config.json --dry-run

# Run registration
kintsugi register config.json

# Override config options
kintsugi register config.json --src /path/to/images --dst /path/to/output

kintsugi init

Initialize a new KINTSUGI project.

kintsugi init /path/to/project
kintsugi init /path/to/project --name "My Project" --description "Project description"

MCP Server Commands

KINTSUGI includes an MCP (Model Context Protocol) server for Claude Code integration.

kintsugi mcp start

Start the MCP server for Claude Code integration.

kintsugi mcp start

The server exposes image processing tools that Claude Code can use for signal isolation and quality assessment.

kintsugi mcp tools

List all available MCP tools.

kintsugi mcp tools

Available Tools:

Category	Tools
Signal Isolation	`load_channel`, `subtract_blank`, `denoise`, `denoise_advanced`, `apply_clahe`, `clean_background`, `gaussian_subtract`
Quality Assessment	`assess_quality`, `compute_snr`
Visualization	`get_image_stats`, `get_thumbnail`
Workflow	`list_channels`, `save_processed`, `suggest_parameters`, `generate_jupyter_cell`
Parameter Learning	`get_learned_parameters`, `record_successful_parameters`, `suggest_with_learning`, `approve_and_learn`, `get_learning_statistics`

kintsugi mcp config

Generate and create Claude Code MCP configuration for a project.

kintsugi mcp config /path/to/project

This automatically creates .claude/settings.local.json with the MCP server configuration. If the file already exists, it adds the KINTSUGI server to your existing configuration.

Use --print-only to display the configuration without creating files:

kintsugi mcp config /path/to/project --print-only

Configuration File Format

The configuration file is a JSON file with the following structure:

{
    "src_dir": "/path/to/source/images",
    "dst_dir": "/path/to/output",
    "reference_image": "cycle1.tif",
    "image_type": "tif",
    "series": 0,
    "max_image_dim_px": 2048,
    "max_processed_image_dim_px": 2048,
    "micro_rigid_registrar_cls": "RigidRegistrar",
    "align_to_reference": true,
    "create_masks": true,
    "resolution_xyu": [0.325, 0.325, "um"],
    "channel_names": [],
    "compose_non_rigid": true,
    "crop_to_overlap": true
}

Environment Variables

Variable	Description
`KINTSUGI_DATA_DIR`	Default data directory
`VIPS_PATH`	libvips binary directory (Windows)

Installing Optional Features

Install optional dependency groups:

# GPU acceleration (PyTorch + CuPy)
pip install kintsugi[gpu]

# Napari visualization
pip install kintsugi[viz]

# Spatial analysis (scanpy, scimap)
pip install kintsugi[analysis]

# Deep learning segmentation
pip install kintsugi[dl]

# Claude Code MCP integration
pip install kintsugi[claude]

# Advanced denoising (N2V, CARE)
pip install kintsugi[denoise]

# All optional features
pip install kintsugi[full]