Back to Home
lynnlangit icon

precision-medicine-mcp

Verified Safe

by lynnlangit

Overview

Deep learning-based cell segmentation and classification in microscopy images for quantitative phenotyping and visualization.

Installation

Run Command
python -m mcp_deepcell

Environment Variables

  • DEEPCELL_OUTPUT_DIR
  • DEEPCELL_DRY_RUN

Security Notes

The server processes file paths provided as arguments (`image_path`, `segmentation_mask_path`) to its tool functions. While the Streamlit UI includes file sanitization, the server's internal tool implementations do not explicitly re-sanitize these paths before file operations (e.g., `PIL.Image.open()`, `fig.savefig()`). This could potentially lead to path traversal vulnerabilities if arbitrary, unsanitized input is passed directly by a compromised LLM or client. The server operates within a designated output directory (`DEEPCELL_OUTPUT_DIR`), which is a good practice. No 'eval', code obfuscation, or hardcoded sensitive secrets were detected.

Similar Servers

Stats

Interest Score37
Security Score7
Cost ClassMedium
Avg Tokens12500
Stars7
Forks4
Last Update2026-01-19

Tags

Deep LearningCell SegmentationMicroscopyImage AnalysisBioinformatics