napari-mcp 0.0.1a0

MCP server for remote control of napari viewers via Model Context Protocol

Napari MCP Server

MCP server for remote control of napari viewers via Model Context Protocol (MCP). Perfect for AI-assisted analysis with Claude Desktop.

🚀 Quick Start

Option 1: Install from PyPI (Recommended)

# Install the package
pip install napari-mcp

# Run the server
napari-mcp

Option 2: Install from GitHub (Latest)

# Install directly from GitHub
uv pip install git+https://github.com/royerlab/napari-mcp.git

# Run the server
napari-mcp

Option 3: Development Install

# Clone and install
git clone https://github.com/royerlab/napari-mcp.git
cd napari-mcp
uv pip install -e .

# Run the server
napari-mcp

Claude Desktop config (After Installation):

{
  "mcpServers": {
    "napari": {
      "command": "napari-mcp"
    }
  }
}

Alternative config (GitHub install):

{
  "mcpServers": {
    "napari": {
      "command": "uv",
      "args": [
        "run", "--with", "git+https://github.com/royerlab/napari-mcp.git",
        "napari-mcp"
      ]
    }
  }
}

Why this approach?

• ✅ Zero Installation - No pip install, no virtual environments
• ✅ Single File - Easy to share, version, and deploy
• ✅ Auto Dependencies - uv handles all dependencies automatically
• ✅ Direct GitHub Execution - Run latest version directly from repo without downloading
• ✅ Always Up-to-Date - GitHub URL ensures you get the latest version
• ✅ Reproducible - Same dependencies every time

🤖 Multi-LLM Support

Works with multiple AI assistants and IDEs:

Application	Status	Setup Method
Claude Desktop	✅ Full Support	Manual config (recommended)
Claude Code	✅ Full Support	fastmcp install claude-code
Cursor	✅ Full Support	fastmcp install cursor
ChatGPT	🟡 Limited	Remote deployment only

→ See LLM_INTEGRATIONS.md for complete setup guides

🔧 Alternative Installation Methods

Traditional Package Installation

# Clone and install
git clone https://github.com/royerlab/napari-mcp.git
cd napari-mcp
pip install -e .

# Run
napari-mcp

Claude Desktop config for installed version:

{
  "mcpServers": {
    "napari": {
      "command": "napari-mcp"
    }
  }
}

Development Installation

# With uv (recommended for development)
uv pip install -e ".[test,dev]"

# With pip
pip install -e ".[test,dev]"

Requirements:

• Python 3.10+
• napari 0.5.5+
• Qt Backend (PyQt6 installed automatically)

🛠 Available MCP Tools

Session Information

• session_information() - Get comprehensive session info including viewer state, layers, system details

Layer Management

• list_layers() - Get all layers and their properties
• add_image(path, name?, colormap?, blending?, channel_axis?) - Add image layer from file
• add_labels(path, name?) - Add segmentation labels from file
• add_points(points, name?, size?) - Add point annotations
• remove_layer(name) - Remove layer by name
• rename_layer(old_name, new_name) - Rename layer
• set_layer_properties(...) - Modify layer visibility, opacity, colormap, etc.
• reorder_layer(name, index?|before?|after?) - Change layer order
• set_active_layer(name) - Set selected layer

Viewer Controls

• init_viewer(title?, width?, height?) - Create or configure viewer
• close_viewer() - Close viewer window
• start_gui(focus?) - Start GUI event loop
• stop_gui() - Stop GUI event loop
• is_gui_running() - Check GUI status
• reset_view() - Reset camera to fit all data
• set_zoom(zoom) - Set zoom level
• set_camera(center?, zoom?, angle?) - Position camera
• set_ndisplay(2|3) - Switch between 2D/3D display
• set_dims_current_step(axis, value) - Navigate dimensions (time, Z-stack)
• set_grid(enabled?) - Enable/disable grid view

Utilities

• screenshot(canvas_only?) - Capture PNG image as base64
• execute_code(code) - Run Python with access to viewer, napari, numpy
• install_packages(packages, ...) - Install Python packages dynamically

⚠️ IMPORTANT SECURITY WARNING

This server includes powerful tools that allow arbitrary code execution:

• execute_code() - Runs any Python code in the server environment
• install_packages() - Installs any Python package via pip

Security Implications:

• ✅ Safe for local development with trusted AI assistants like Claude
• ❌ NEVER expose to untrusted networks or public internet
• ❌ Do not use in production environments without proper sandboxing
• ❌ Can access your filesystem, network, and install malware

Recommended Usage:

• Use only on localhost connections
• Run in isolated virtual environments
• Only use with trusted AI assistants

📖 Usage Examples

Basic Layer Operations

Add and manipulate images:

Ask Claude: "Add a sample image to napari and set its colormap to 'viridis'"

Work with annotations:

Ask Claude: "Create some point annotations at coordinates [[100,100], [200,200]] and make them size 10"

Advanced Analysis

Execute custom code:

Ask Claude: "Execute this code to create a synthetic image:
import numpy as np
data = np.random.random((512, 512))
viewer.add_image(data, name='random_noise')"

Install packages on-demand:

Ask Claude: "Install scipy and create a Gaussian filtered version of the current image"

View Management

Control the camera:

Ask Claude: "Reset the view, then zoom to 2x and center on coordinates [256, 256]"

Switch display modes:

Ask Claude: "Switch to 3D display mode and take a screenshot"

🧪 Testing

# Run basic tests (fast, no GUI)
./run_tests.sh

# Run with real GUI tests (requires display)
./run_realgui_tests.sh

# Run with coverage
pytest --cov=src --cov-report=html tests/ -m "not realgui"

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes with tests
4. Run pre-commit hooks: pre-commit run --all-files
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

Development setup:

git clone https://github.com/royerlab/napari-mcp.git
cd napari-mcp
uv pip install -e ".[test,dev]"
pre-commit install

📋 Architecture

The server architecture consists of:

• FastMCP Server: Handles MCP protocol communication
• Napari Integration: Manages viewer lifecycle and operations
• Qt Event Loop: Asynchronous GUI event processing
• Tool Layer: Exposes napari functionality as MCP tools

Key design decisions:

• Thread-safe: All napari operations are serialized through locks
• Non-blocking: Qt event loop runs asynchronously
• Stateful: Maintains viewer state across tool calls
• Extensible: Easy to add new tools and functionality

📚 Resources

• QUICKSTART.md - Get running in 2 minutes
• LLM_INTEGRATIONS.md - Complete guide for Claude Desktop, Claude Code, Cursor, ChatGPT
• Model Context Protocol - MCP specification
• FastMCP - Python MCP framework
• napari - Multi-dimensional image viewer
• Claude Desktop - AI assistant with MCP support

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

• napari team for the excellent imaging platform
• FastMCP for the MCP framework
• Anthropic for Claude and MCP development
• astral-sh for uv dependency management

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