Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MedVision MCP Analyze this chest X-ray and describe any clinical findings."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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: vClinic 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
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