image-mcp-server 0.1.3

MCP server for image understanding using Alibaba Cloud's Qwen3-VL-Plus model

Image MCP Server

A Model Context Protocol (MCP) server for image understanding using Alibaba Cloud's Qwen3-VL-Plus model.

🚀 Quick Start

Installation

Option 1: Using uvx (Recommended)

uvx image-mcp-server

Option 2: Using pip

pip install image-mcp-server

Option 3: Using uv

uv pip install image-mcp-server

🔧 MCP Configuration

Claude Desktop Configuration

1. Locate your Claude Desktop config file:

   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %APPDATA%\Claude\claude_desktop_config.json
   • Linux: ~/.config/claude/claude_desktop_config.json

2. Add the MCP server configuration:

{
  "mcpServers": {
    "image-mcp-server": {
      "command": "uvx",
      "args": ["image-mcp-server@latest"],
      "env": {
        "IMAGE_MCP_DASHSCOPE_API_KEY": "your-dashscope-api-key-here"
      }
    }
  }
}

Configuration Notes:

• Replace your-dashscope-api-key-here with your actual DashScope API key
• Using @latest ensures you always get the newest version
• The server will automatically download and run when first used by Claude

3. Restart Claude Desktop to load the new configuration.

API Key Setup

1. Visit Alibaba Cloud DashScope Console
2. Sign up or log in to your account
3. Navigate to API Keys section
4. Create a new API key
5. Copy the key for use in your MCP configuration

🎯 Usage Examples

Once configured, you can use the image analysis capabilities directly in Claude:

Basic Image Analysis

Please analyze this image: [upload image]

Detailed Analysis Request

Describe this image in detail, including the main objects, colors, and overall scene. Also provide confidence scores for your observations.

Technical Analysis

What type of objects are visible in this image? Please provide a list with confidence scores.

URL-based Analysis

Please analyze the image at this URL: https://example.com/image.jpg

📋 Features

• Multi-format Support: JPEG, PNG, GIF, WebP
• Dual Input Methods: Direct upload or URL-based analysis
• Confidence Scoring: Provides confidence levels and scores for detected objects
• Structured Output: JSON-formatted analysis results
• Environment Configuration: Secure API key management
• Automatic Updates: Using @latest ensures latest features

🔧 Configuration Options

The server supports various configuration options via environment variables:

Environment Variable	Default	Description
IMAGE_MCP_DASHSCOPE_API_KEY	Required	Your DashScope API key
IMAGE_MCP_DASHSCOPE_BASE_URL	https://dashscope.aliyuncs.com/compatible-mode/v1	API endpoint URL
IMAGE_MCP_MODEL_NAME	qwen3-vl-plus	Model to use for analysis
IMAGE_MCP_MAX_IMAGE_SIZE_MB	10	Maximum image size in MB
IMAGE_MCP_REQUEST_TIMEOUT	30	Request timeout in seconds
IMAGE_MCP_MAX_RETRIES	3	Maximum retry attempts
IMAGE_MCP_LOG_LEVEL	INFO	Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
IMAGE_MCP_ENABLE_DEBUG	false	Enable debug logging

🛠️ Development

Setup Development Environment

# Clone the repository
git clone https://github.com/your-repo/image-mcp-server.git
cd image-mcp-server

# Install in development mode
uv pip install -e .

# Run tests
pytest

# Run tests with coverage
pytest --cov=image_mcp

# Lint code
ruff check .

# Format code
black .
isort .

Build Package

# Build for distribution
python -m build

# Upload to PyPI (requires credentials)
twine upload dist/*

📚 API Documentation

MCP Tools

The server provides the following MCP tools:

analyze_image

Analyzes an image and returns detailed information about its contents.

Parameters:

• image_data (optional): Base64-encoded image data
• image_url (optional): URL to an image file
• prompt (optional): Custom prompt for analysis

Example:

{
  "image_url": "https://example.com/image.jpg",
  "prompt": "Describe what you see in this image"
}

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

• Documentation: Full Documentation
• Issues: GitHub Issues
• Discussions: GitHub Discussions

🔗 Related Links

• Alibaba Cloud DashScope
• Qwen3-VL-Plus Documentation
• Model Context Protocol
• Claude Desktop

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Happy image analyzing! 🚀