# Active Context
## Current Development Focus
The project is currently focusing on **enhanced flexibility and usability** while maintaining strict MCP compliance. We're implementing practical features that make the service more useful in real-world scenarios.
## Recent Changes
1. **Enhanced Image Input Support**:
- Added support for image URLs
- Added support for local file paths
- Implemented automatic detection and conversion
- Created robust error handling for various input types
2. **Improved Testing**:
- Added comprehensive tests for all input formats
- Implemented test fixtures for consistent testing
- Added mocked API responses
- Ensured all code is formatted with black
3. **Documentation Updates**:
- Enhanced README with more detailed examples
- Added examples for different image input methods
- Updated memory bank documentation
## Current Implementation Status
- ✅ Core MCP server implemented with FastMCP
- ✅ `image_analysis` tool with flexible input handling
- ✅ Robust error handling and input validation
- ✅ Comprehensive tests for functionality
- ✅ Configuration via environment variables
## Active Decisions
1. **Input Flexibility**: Added support for multiple image input formats to make the service more practical and user-friendly
2. **Configuration**: Using primarily environment variables for simple configuration
3. **Error Handling**: Implemented a comprehensive exception hierarchy for different error cases
4. **Testing**: Focused on thorough testing with mocks to ensure reliability without requiring actual API calls
## Next Steps
The immediate next steps are:
1. **Performance Optimization**: Consider optimizing handling of large images
2. **Documentation Expansion**: Add more examples covering different use cases and models
3. **Specialized Tools**: Consider adding more specialized vision analysis tools
4. **Deployment Guidance**: Enhance deployment documentation for production use
## Implementation Principles
1. **Practical Usability**: Implement features that make the service more useful in real-world scenarios
2. **Robust Error Handling**: Provide clear error messages and graceful failure
3. **MCP Compatibility**: Ensure all tools work correctly with standard MCP clients
4. **Comprehensive Testing**: Maintain high test coverage to ensure reliability
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/Nazruden/mcp-openvision'
If you have feedback or need assistance with the MCP directory API, please join our Discord server