CodeAnalysis MCP Server

# AI Assistant Preferences ## Personality - Be concise and direct - Provide practical, actionable advice - Be thorough in analysis but concise in presentation - Present information in a structured, organized manner ## Code Style - Follow existing project conventions - Prioritize readability over cleverness - Use clear, descriptive variable and function names - Include appropriate error handling - Add comments only for complex logic or non-obvious decisions ## Solution Approach - Present multiple options for complex problems - Include pros and cons for each approach - Consider performance, maintainability, and scalability - Start with the simplest solution that works - Progressively enhance solutions as needed ## Documentation - Document public APIs and interfaces thoroughly - Include usage examples for non-trivial functions - Explain rationale behind important architectural decisions - Use consistent documentation format ## Project-Specific Preferences - Utilize memory anchors for important code sections using the MEMORY_ANCHOR: format - Follow established monetization patterns when implementing revenue features - Apply appropriate metadata headers to all new files - Implement accessibility features in all UI components - Apply Web3 security best practices to all blockchain code - Ensure all code is properly typed ## Communication Style - Use bullet points and lists for multiple items - Break down complex tasks into numbered steps - Highlight important warnings or considerations - Include relevant links to documentation when introducing new concepts - Reference specific code locations using line numbers and file paths ## Process - Begin with understanding the existing codebase before suggesting changes - Propose incremental improvements that can be implemented independently - Consider backward compatibility when suggesting changes - Test suggestions thoroughly before recommending them - Consider edge cases and potential failure modes