Lucidity MCP is an MCP server designed to elevate AI-generated code quality through intelligent, prompt-driven analysis. Functioning as a valuable tool for developers, it provides AI coding assistants with structured guidance to identify and address common code quality issues, leading to cleaner, more maintainable, and robust code.

Lucidity analyzes code across ten critical dimensions, including complexity, security vulnerabilities, and style inconsistencies. It seamlessly integrates with AI models like Claude via the MCP protocol, offering actionable feedback and clear recommendations. By analyzing changes directly from git diffs, Lucidity is ideal for pre-commit reviews.

Developers benefit from Lucidity's language-agnostic design, comprehensive issue detection, and structured outputs, enabling them to produce higher-quality code more efficiently. Lucidity supports both standard input/output and HTTP/SSE for flexible integration.

Comprehensive Code Quality Analysis

Lucidity-mcp offers in-depth code analysis across ten critical dimensions, including complexity, security vulnerabilities, and style inconsistencies. This comprehensive approach allows AI coding assistants to identify a wide range of potential issues that might be missed by simpler analysis tools. The analysis goes beyond basic syntax checks, delving into the logic and structure of the code to provide more meaningful feedback. By identifying these issues early in the development process, developers can save time and effort by addressing them before they become more significant problems. This feature enhances the AI model's ability to generate high-quality, maintainable code, reducing the need for extensive manual review.

For example, Lucidity-mcp can detect overly complex algorithms that could impact performance or identify potential security vulnerabilities in user input validation. This is achieved through static analysis techniques and pattern matching, allowing the AI to provide specific recommendations for improvement.

Contextual Change Analysis

Lucidity-mcp performs contextual analysis by comparing code changes against the original code. This allows the AI to identify unintended modifications, such as accidental deletion of critical functionality or the introduction of new errors. By understanding the context of the changes, the AI can provide more relevant and accurate feedback, helping developers avoid introducing regressions or breaking existing functionality. This feature is particularly useful in collaborative development environments where multiple developers are working on the same codebase.

For instance, if a developer refactors a function and inadvertently removes a necessary validation check, Lucidity-mcp will detect this discrepancy and alert the developer to the potential issue. This is accomplished by analyzing the git diff and comparing the before and after states of the code.

AI-Guided Actionable Feedback

Lucidity-mcp structures its outputs to guide AI assistants in providing actionable feedback with clear recommendations. Instead of simply flagging potential issues, it provides the AI with the context and information needed to suggest specific solutions. This structured approach ensures that the feedback is not only accurate but also helpful and easy to implement. By providing actionable guidance, Lucidity-mcp empowers AI assistants to become more effective code reviewers and collaborators.

For example, if Lucidity-mcp detects code duplication, it can provide the AI with the information needed to suggest refactoring the duplicated code into a reusable function. The AI can then present this suggestion to the developer with specific instructions on how to implement the change. This is achieved through a combination of static analysis and natural language generation techniques.

Git-Aware Integration

Lucidity-mcp is designed to analyze code changes directly from git diffs, making it ideal for pre-commit reviews and continuous integration workflows. This integration allows developers to catch potential issues before they are committed to the repository, reducing the risk of introducing bugs or security vulnerabilities. By working directly with git, Lucidity-mcp can provide accurate and up-to-date analysis, even in rapidly changing codebases. This feature streamlines the code review process and helps ensure that only high-quality code is merged into the main branch.

The analyze_changes tool takes the workspace root and optional file path as parameters, allowing the AI to specify the scope of the analysis. This tool leverages git commands to extract the necessary code changes for analysis.