This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
Medical Vision AI Tools via Model Context Protocol (MCP)
MedVision MCP provides AI-powered medical image analysis tools accessible through the Model Context Protocol. It enables LLM agents (like Claude, GitHub Copilot) to analyze chest X-rays using Visual RAG (RAD-DINO + FAISS + DenseNet).
Related MCP server: Vision MCP Server
✅ DenseNet Classification: 18 pathology detection (Lung Opacity, Pneumonia, etc.)
✅ RAD-DINO Embeddings: 768-dim visual embeddings for similarity search
✅ FAISS Index: Fast similarity search for similar historical cases
✅ DICOM Support: Native DICOM file reading
✅ Gradio Canvas: Interactive ROI drawing/annotation interface
✅ ROI Analysis: Analyze specific regions drawn on X-rays
🔜 Medical SAM: SAM-based region segmentation
# Clone
git clone https://github.com/u9401066/medvision-mcp.git
cd medvision-mcp
# Install with uv
uv sync
# Test classification
uv run python -c "
import asyncio
from src.medvision_mcp.server import classify_xray
async def main():
result = await classify_xray('path/to/xray.dcm')
print(result)
asyncio.run(main())
"Tool | Description |
| Full Visual RAG analysis (classification + similarity) |
| Quick DenseNet-121 classification (18 pathologies) |
| RAG similarity search |
| Build FAISS index from image directory |
| Load pre-built index |
| Check model loading status |
Launch the interactive web UI:
# Start Gradio server
uv run python -m src.medvision_mcp.ui.app
# Open http://localhost:7860UI Tabs:
Tab | Description |
📊 Analysis | Full image analysis (classification + RAG) |
⚡ Quick Classify | Fast 18-pathology classification |
🎨 Canvas ROI | Draw ROIs and analyze specific regions |
🔧 Build Index | Create FAISS index from images |
📂 Load Index | Load pre-built index |
ℹ️ Status | Check model loading status |
Add to ~/.config/claude/claude_desktop_config.json:
{
"mcpServers": {
"medvision": {
"command": "uv",
"args": ["run", "--directory", "/path/to/medvision-mcp", "python", "-m", "src.medvision_mcp.server"]
}
}
}┌─────────────────────────────────────────────────────────┐
│ MCP Client (Claude, Copilot) │
└─────────────────────────┬───────────────────────────────┘
│ stdio
┌─────────────────────────▼───────────────────────────────┐
│ MedVision MCP Server │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────────┐ │
│ │ classify │ │ search │ │ analyze │ │
│ │ _xray │ │ _similar │ │ _xray │ │
│ └─────────────┘ └─────────────┘ └─────────────────┘ │
│ │ │
│ ┌───────────────────────▼────────────────────────────┐ │
│ │ Visual RAG Engine │ │
│ │ RAD-DINO │ FAISS │ DenseNet-121 │ │
│ └────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────┘# Install dev dependencies
uv sync --dev
# Run tests
uv run pytest
# Check types
uv run pyrightMIT
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
This server cannot be deployed
Physician-reviewed medical opinions and prescriptions for AI agents.
Image & PDF tools for AI agents: compress, convert, resize, PDF, AI vision, pipeline.
Telehealth support for Claude, Codex and Cursor: investigate cases and connect your tools.
OCR, transcription, file extraction, and image generation for AI agents via MCP.