Napari MCP Server

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

https://github.com/user-attachments/assets/d261674c-9875-4671-8c60-a7f49d6f1b84

🚀 Quick Start (3 Steps)

1. Install the Package

pip install napari-mcp

2. Auto-Configure Your AI Application

# For Claude Desktop
napari-mcp-install install claude-desktop

# Include a napari GUI backend in the uv environment
napari-mcp-install install claude-desktop --backend pyqt6

# For other applications (Claude Code, Cursor, Cline, etc.)
napari-mcp-install install --help  # See all options

3. Restart Your Application & Start Using

Restart your AI app and you're ready! Try asking:

"Can you call session_information() to show my napari session details?"

→ See Full Documentation for detailed guides

Related MCP server: mcp-media-engine

🔌 Using as a napari Plugin

napari-mcp can also be used as a napari plugin for direct integration with a running napari session:

1. Start napari normally: napari

2. Open the widget: Plugins → napari-mcp: MCP Server Control

3. Click "Start Server" to expose your current session to AI assistants

4. Connect your AI app using the standard installer: napari-mcp-install install <app>

This mode enables AI assistants to control your current napari session rather than starting a new viewer. Perfect for integrating with existing workflows!

→ See Plugin Guide for detailed instructions

🎯 What Can You Do?

Basic Image Analysis

"Load the image from ./data/sample.tif and apply a viridis colormap"
"Create point annotations at coordinates [[100,100], [200,200]]"
"Take a screenshot and save it"

Advanced Workflows

"Execute this code to create a filtered version:
from scipy import ndimage
filtered = ndimage.gaussian_filter(viewer.layers[0].data, sigma=2)
viewer.add_image(filtered, name='filtered')"

"Install scikit-image and segment the cells in this microscopy image"

3D/4D Navigation

"Switch to 3D display mode"
"Navigate to time point 5, Z-slice 10"
"Create a rotating animation of this volume"

Automated Workflows

Want to automate image processing with Python scripts? Use any LLM (OpenAI, Anthropic, etc.) with napari MCP:

→ See Python Integration Examples for batch processing and workflow automation

🤖 Supported AI Applications

→ See Integration Guides for application-specific instructions

🛠 Available MCP Tools

The server exposes 16 tools for complete napari control:

Core Functions

• Session Management: init_viewer, close_viewer, session_information

• Layer Operations: add_layer, list_layers, get_layer, remove_layer, set_layer_properties, reorder_layer, apply_to_layers, save_layer_data

• Viewer Controls: configure_viewer

• Utilities: screenshot, execute_code, install_packages, read_output

⚠️ Security Notice

!!! warning "Code Execution Capabilities" This server includes powerful tools that allow arbitrary code execution:

- **`execute_code()`** - Runs Python code in the server environment
- **`install_packages()`** - Installs packages via pip

The bridge server binds to `127.0.0.1` (localhost only) with no authentication.
Any local process can invoke these tools.

**Use only with trusted AI assistants on local networks.**
Never expose to public internet without proper sandboxing.

📖 Documentation

• Quick Start Guide - Get running in 3 minutes

• Installation Options - Advanced installation methods

• Integration Guides - Setup for specific AI applications

• Python Examples - Automate workflows with custom scripts

• Troubleshooting - Common issues and solutions

• API Reference - Complete tool documentation

🧪 Development Setup

# Clone repository
git clone https://github.com/royerlab/napari-mcp.git
cd napari-mcp

# Install with development dependencies
pip install -e ".[dev]"

# Run tests
pytest -m "not realgui"  # Skip GUI tests
pytest --cov=src --cov-report=html  # With coverage

🤝 Contributing

Contributions are welcome! Please:

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 changes (git commit -m 'Add amazing feature')

6. Push to branch (git push origin feature/amazing-feature)

7. Open a Pull Request

📋 Architecture

• state.py — ServerState holding all mutable state (viewer, locks, execution namespace)

• server.py — create_server(state) factory; tools defined as closures over state

• qt_helpers.py — Qt application and viewer lifecycle management

• output.py — Output truncation utility

• bridge_server.py — Plugin bridge server (overrides 3 tools for Qt thread safety)

• viewer_protocol.py — ViewerProtocol for typed viewer backends

• cli/ — napari-mcp-install CLI for configuring AI applications

Key features:

• Thread-safe: All napari operations are serialized

• Non-blocking: Qt event loop runs asynchronously

• Stateful: Maintains viewer state across tool calls

• Extensible: Easy to add new tools

📚 Resources

• napari - Multi-dimensional image viewer

• Model Context Protocol - MCP specification

• FastMCP - Python MCP framework

• Claude Desktop - AI assistant with MCP support

📄 License

BSD-3-Clause 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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