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

# Clone the repository
git clone https://github.com/yourusername/code-review-server.git
cd code-review-server

# Install dependencies
npm install

# Build the server
npm run build

Configuration

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

cp .env.example .env

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

# LLM Provider Configuration
LLM_PROVIDER=OPEN_AI
OPENAI_API_KEY=your_openai_api_key_here

Usage

As an MCP Server

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

# Start the server
node build/index.js

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:

node build/cli.js <repo_path> [options]

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:

node build/cli.js ./my-project --types .js,.ts --detail detailed --focus security,quality

Development

# Run tests
npm test

# Watch mode for development
npm run watch

# Run the MCP inspector tool
npm run inspector

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:

# Set which provider to use
LLM_PROVIDER=OPEN_AI  # Options: OPEN_AI, AN

... [View full README on GitHub](https://github.com/crazyrabbitLTC/mcp-code-review-server#readme)