Cellpose MCP Server

Cellpose-mcp is a Model Context Protocol (MCP) server that enables AI assistants like Claude, Cursor IDE, etc. to perform cell segmentation through natural language commands. This tool exposes comprehensive Cellpose functionality through 13+ MCP tools, including 2D/3D segmentation, batch processing, image restoration (denoising, deblurring, upsampling), and custom model training. The system integrates seamlessly with Napari, enabling complete workflows from segmentation to interactive visualization.

📌 Note: This project started as a fun project inspired by napari-mcp and adapted for Cellpose segmentation workflows. If you would like to contribute then please get in touch with me at ssahu2@ucmerced.edu.

🚀 Quick Start

Requirements: Python 3.10 or later (use a virtual environment or conda).

Install from PyPI:

pip install cellpose-mcp

Install and configure for Cursor in one go:

pip install cellpose-mcp && cellpose-mcp-install cursor

The installer uses the Python that runs the command (or a conda env named Cellpose_mcp if present). Restart Cursor after configuring.

Development install (from source):

git clone https://github.com/surajinacademia/cellpose_mcp.git
cd cellpose_mcp
pip install -e .

Auto-Configure Your AI Application

After pip install cellpose-mcp, run the installer for your app. It writes to the correct MCP config file using your current Python.

Options: --python-path /path/to/python to use a specific Python; --env-name NAME to use a conda env (default: Cellpose_mcp).

If you prefer manual setup (or use Claude Code), create a .mcp.json file in your project root. Use the full path to your Python executable if python is not the one that has cellpose-mcp installed (e.g. a venv or conda):

{
  "mcpServers": {
    "cellpose": {
      "command": "python",
      "args": ["-m", "cellpose_mcp"],
      "env": {
        "KMP_DUPLICATE_LIB_OK": "TRUE",
        "OMP_NUM_THREADS": "1"
      }
    }
  }
}

For Cursor, use the same structure in ~/.cursor/mcp.json (global) or .cursor/mcp.json in your project.

After installation, restart your AI app and try asking:

"Can you list available Cellpose models?"
"Segment the cells in ./data/sample.tif using the cyto2 model"

🎯 What Can You Do?

Example: Cell Segmentation in Action

Basic Cell Segmentation

"Segment the cells in ./data/sample.tif using the cyto2 model"
"List available Cellpose models"
"Estimate cell diameter from ./data/image.tif"

Advanced Workflows

"Segment all TIFF files in ./data/images/ and save masks to ./output/"
"Train a custom segmentation model using images in ./train/images/ and masks in ./train/masks/"
"Restore and segment the noisy image in ./data/noisy.tif using oneclick_cyto3"

Batch Processing

"Process all images in ./data/ with the cyto2 model and save results to ./output/"

🛠 Available MCP Tools

The server exposes 13+ tools for complete Cellpose functionality:

Segmentation Tools

• segment_cells_2d - Segment cells in 2D images
• segment_cells_3d - Segment cells in 3D volumes
• segment_cells_batch - Batch process multiple images

Image Restoration Tools

• denoise_image - Denoise microscopy images
• deblur_image - Deblur microscopy images
• upsample_image - Upsample low-resolution images
• restore_and_segment - Combined restoration + segmentation

Training Tools

• train_segmentation_model - Train custom segmentation model
• train_restoration_model - Train custom restoration model

Utility Tools

• list_available_models - List all pretrained models
• estimate_cell_diameter - Estimate cell diameter from image
• save_masks - Save masks in various formats
• load_image_info - Get image metadata

📖 Documentation

• Quick Start Guide - Get running in 3 steps
• Available Tools - Complete tool list
• Release Notes - Detailed v0.1.0 release information
• Changelog - Version history and changes

📋 Architecture

• FastMCP Server: Handles MCP protocol communication
• Cellpose Integration: Manages model loading and segmentation operations
• Tool Layer: Exposes Cellpose functionality as MCP tools
• File I/O: Handles image reading, writing, and mask generation

Key features:

• Thread-safe: All operations are properly serialized
• Non-blocking: Async operations for better performance
• Napari Integration: Integration with Napari for visualization and analysis

Author: Suraj Sahu
Affiliation: Department of Physics, University of California Merced, CA, USA
Email: ssahu2@ucmerced.edu

📄 License

BSD-3-Clause License - see LICENSE file for details.

🙏 Acknowledgments

• Napari MCP by royerlab
• Cellpose team for the excellent segmentation library
• FastMCP for the MCP framework
• Anthropic for Claude and MCP development
• Model Context Protocol - Open standard for AI-tool integration