precision-medicine-mcp
Verified Safeby lynnlangit
Overview
Deep learning-based cell segmentation and classification in microscopy images for quantitative phenotyping and visualization.
Installation
python -m mcp_deepcellEnvironment 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.
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