Code Review Integration with Large Language Models

Overview

A custom MCP server designed to perform code reviews using Repomix and Large Language Models (LLMs). This server provides structured code reviews with specific issues and recommendations, supporting multiple LLM providers such as OpenAI, Anthropic, and Gemini.

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

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

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

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, ANTHROPIC, or GEMINI

# Provider API Keys (add your key for the chosen provider)
OPENAI_API_KEY=your-openai-api-key
ANTHROPIC_API_KEY=your-anthropic-api-key
GEMINI_API_KEY=your-gemini-api-key

Model Configuration

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

# Optional: Override the default models
OPENAI_MODEL=gpt-4-turbo
ANTHROPIC_MODEL=claude-3-sonnet-20240229
GEMINI_MODEL=gemini-1.5-flash-preview

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

Code Review Output Format

The code review is returned in a structured JSON format:

{
  "summary": "Brief summary of the code and its purpose",
  "issues": [
    {
      "type": "SECURITY|PERFORMANCE|QUALITY|MAINTAINABILITY",
      "severity": "HIGH|MEDIUM|LOW",
      "description": "Description of the issue",
      "line_numbers": [12, 15],
      "recommendation": "Recommended fix"
    }
  ],
  "strengths": ["List of code strengths"],
  "recommendations": ["List of overall recommendations"]
}

License

MIT