Lucidity MCP

🚀 Lucidity - A Model Context Protocol (MCP) Server

Lucidity is an open-source tool designed to enhance the collaboration between developers and AI assistants. It aims to improve the quality of AI-generated code through intelligent prompt analysis.

✨ Features

• MCP Compatibility: Supports interaction via the MCP protocol.
• Multi-language Support: Capable of analyzing the code quality of multiple programming languages.
• Real-time Feedback: Quickly provides code optimization suggestions and detects potential issues.
• Scalable Architecture: Easy to integrate into various development workflows.
• User-friendly: Operable via simple command lines.

📦 Installation

Prerequisites

• Python 3.9+: Ensure you are using a compatible version.
• UV: Installed and configured (for dependency management).
• Git: For code repository operations (optional but recommended).

Steps

1. Clone the repository:

git clone https://github.com/hyperb1iss/lucidity.git
cd lucidity

2. Install dependencies using UV:

uv install

3. Start the server:

python -m lucidity.server --host 0.0.0.0 --port 8080

🚀 Quick Start

After installation, you can:

1. Start the Lucidity server:

python -m lucidity.server --transport sse --port 8080

2. Connect with an AI assistant and query code quality:

# Example query: Analyze recent git changes
/analyze_changes?workspace_root=/path/to/repo

💻 Analysis Dimensions

Lucidity can detect the following issues:

1. Code Quality Issues:
   • Duplicate logic
   • Poor programming habits
   • Low-readability code structures
2. Security Risks:
   • Storing sensitive information in plain text
   • Potential security vulnerabilities
   • Poor permission management
3. Test Coverage:
   • Uncovered functional points
   • Insufficient test cases
   • Mismatch between tests and code
4. Performance Optimization Suggestions:
   • Improper resource usage
   • Possible performance bottlenecks
   • Identification of optimization opportunities
5. Documentation Completeness:
   • Missing comments
   • Inconsistency between documentation and implementation
   • Insufficient API documentation

💻 Example Queries

Here are some example queries you can try with an AI assistant:

• "Analyze the code quality in my latest git commit."
• "Check for security vulnerabilities in my JavaScript changes."
• "Ensure my Python code follows best practices."
• "Identify performance issues in my recent code."

📚 Available MCP Tools

Tool List

• analyze_changes: Prepare git changes for analysis.
  • Parameters:
    • workspace_root: The root directory of the workspace or git repository.
    • path: The specific file path to be analyzed (optional).

🔧 Technical Details

Lucidity uses UV for dependency management and development processes. UV is a fast and reliable Python package manager and resolver.

# Update dependencies:
uv sync

# Run tests:
pytest

# Perform code checks:
ruff check .

# Run type checks:
mypy .

📄 Log Behavior

Lucidity handles logs according to the transport method:

• SSE Transport: Enables full console logging.
• Stdio Transport with --log-file specified: Writes all logs to a file and disables console output.
• Stdio Transport without --log-file specified: Displays only error logs.

📄 Command-line Options

usage: lucidity.server [-h] [--debug] [--host HOST] [--port PORT]

Optional arguments:
  -h, --help      Show this help message and exit
  -d, --debug     Enable debug mode
  --host HOST     Specify the host address to bind the server to, default is 127.0.0.1
  --port PORT     Specify the port the server uses, default is 8080

🤝 Contribution Guidelines

Contributions are welcome! Please refer to CONTRIBUTING.md to learn how to participate in the development.

📄 License

This project is licensed under the MIT License. Please see LICENSE for detailed information.

📞 Contact

For feedback or issues, please contact [email] or submit an issue on GitHub.

Project URL: https://github.com/hyperb1iss/lucidity Documentation URL: https://lucidity.readthedocs.io