Code Review Server

A custom MCP server that performs code reviews using Repomix and LLMs.

Features

• Flatten codebases using Repomix
• Analyze code with Large Language Models
• Get structured code reviews with specific issues and recommendations
• Support for multiple LLM providers (OpenAI, Anthropic, Gemini)
• Handles chunking for large codebases

Installation

Configuration

Create a .env file in the root directory based on the .env.example template:

Edit the .env file to set up your preferred LLM provider and API key:

Usage

As an MCP Server

The code review server implements the Model Context Protocol (MCP) and can be used with any MCP client:

The server exposes two main tools:

1. analyze_repo: Flattens a codebase using Repomix
2. code_review: Performs a code review using an LLM

When to Use MCP Tools

This server provides two distinct tools for different code analysis needs:

analyze_repo

Use this tool when you need to:

• Get a high-level overview of a codebase's structure and organization
• Flatten a repository into a textual representation for initial analysis
• Understand the directory structure and file contents without detailed review
• Prepare for a more in-depth code review
• Quickly scan a codebase to identify relevant files for further analysis

Example situations:

• "I want to understand the structure of this repository before reviewing it"
• "Show me what files and directories are in this codebase"
• "Give me a flattened view of the code to understand its organization"

code_review

Use this tool when you need to:

• Perform a comprehensive code quality assessment
• Identify specific security vulnerabilities, performance bottlenecks, or code quality issues
• Get actionable recommendations for improving code
• Conduct a detailed review with severity ratings for issues
• Evaluate a codebase against best practices

Example situations:

• "Review this codebase for security vulnerabilities"
• "Analyze the performance of these specific JavaScript files"
• "Give me a detailed code quality assessment of this repository"
• "Review my code and tell me how to improve its maintainability"

When to use parameters:

• specificFiles: When you only want to review certain files, not the entire repository
• fileTypes: When you want to focus on specific file extensions (e.g., .js, .ts)
• detailLevel: Use 'basic' for a quick overview or 'detailed' for in-depth analysis
• focusAreas: When you want to prioritize certain aspects (security, performance, etc.)

Using the CLI Tool

For testing purposes, you can use the included CLI tool:

Options:

• --files <file1,file2>: Specific files to review
• --types <.js,.ts>: File types to include in the review
• --detail <basic|detailed>: Level of detail (default: detailed)
• --focus <areas>: Areas to focus on (security,performance,quality,maintainability)

Example:

Development

LLM Integration

The code review server integrates directly with multiple LLM provider APIs:

• OpenAI (default: gpt-4o)
• Anthropic (default: claude-3-opus-20240307)
• Gemini (default: gemini-1.5-pro)

Provider Configuration

Configure your preferred LLM provider in the .env file:

Model Configuration

You can optionally specify which model to use for each provider:

How the LLM Integration Works

1. The code_review tool processes code using Repomix to flatten the repository structure
2. The code is formatted and chunked if necessary to fit within LLM context limits
3. A detailed prompt is generated based on the focus areas and detail level
4. The prompt and code are sent directly to the LLM API of your chosen provider
5. The LLM response is parsed into a structured format
6. The review is returned as a JSON object with issues, strengths, and recommendations

The implementation includes retry logic for resilience against API errors and proper formatting to ensure the most relevant code is included in the review.

Code Review Output Format

The code review is returned in a structured JSON format:

License

MIT