Code Review

by crazyrabbitltc

5 stars

487 downloads

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GitHub

About

Enables automated code reviews through multiple LLM providers by analyzing repository structures and evaluating code for security, performance, quality, and maintainability with structured output containing issues, strengths, and recommendations.

Details

Author: crazyrabbitltc
Repository: crazyrabbitLTC/mcp-code-review-server
GitHub stars: 5
Downloads: 487
Categories: Design, Developer Tools, AI, Security, Automation, Infrastructure

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below
Plugin Name Code Review

Command (node, npx, python, etc.) node
Arguments
- Argument 1 build/index.js
Environment
- LLM_PROVIDER OPEN_AI
- OPENAI_API_KEY your_openai_api_key_here
Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "code review": {
            "env": {
                "LLM_PROVIDER": "OPEN_AI",
                "OPENAI_API_KEY": "your_openai_api_key_here"
            },
            "args": [
                "build/index.js"
            ],
            "command": "node"
        }
    }
}

JSON

{
    "mcpServers": {
        "code review": {
            "env": {
                "LLM_PROVIDER": "OPEN_AI",
                "OPENAI_API_KEY": "your_openai_api_key_here"
            },
            "args": [
                "/c",
                "node",
                "build/index.js"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "code review": {
            "env": {
                "LLM_PROVIDER": "OPEN_AI",
                "OPENAI_API_KEY": "your_openai_api_key_here"
            },
            "args": [
                "build/index.js"
            ],
            "command": "node"
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "code review": {
            "env": {
                "LLM_PROVIDER": "OPEN_AI",
                "OPENAI_API_KEY": "your_openai_api_key_here"
            },
            "args": [
                "build/index.js"
            ],
            "command": "node"
        }
    }
}

JSON

{
    "mcpServers": {
        "code review": {
            "env": {
                "LLM_PROVIDER": "OPEN_AI",
                "OPENAI_API_KEY": "your_openai_api_key_here"
            },
            "args": [
                "/c",
                "node",
                "build/index.js"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "code review": {
            "env": {
                "LLM_PROVIDER": "OPEN_AI",
                "OPENAI_API_KEY": "your_openai_api_key_here"
            },
            "args": [
                "build/index.js"
            ],
            "command": "node"
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "code review": {
            "env": {
                "LLM_PROVIDER": "OPEN_AI",
                "OPENAI_API_KEY": "your_openai_api_key_here"
            },
            "args": [
                "build/index.js"
            ],
            "command": "node"
        }
    }
}

JSON

{
    "mcpServers": {
        "code review": {
            "env": {
                "LLM_PROVIDER": "OPEN_AI",
                "OPENAI_API_KEY": "your_openai_api_key_here"
            },
            "args": [
                "/c",
                "node",
                "build/index.js"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "code review": {
            "env": {
                "LLM_PROVIDER": "OPEN_AI",
                "OPENAI_API_KEY": "your_openai_api_key_here"
            },
            "args": [
                "build/index.js"
            ],
            "command": "node"
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "code review": {
            "env": {
                "LLM_PROVIDER": "OPEN_AI",
                "OPENAI_API_KEY": "your_openai_api_key_here"
            },
            "args": [
                "build/index.js"
            ],
            "command": "node"
        }
    }
}

JSON

{
    "mcpServers": {
        "code review": {
            "env": {
                "LLM_PROVIDER": "OPEN_AI",
                "OPENAI_API_KEY": "your_openai_api_key_here"
            },
            "args": [
                "/c",
                "node",
                "build/index.js"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "code review": {
            "env": {
                "LLM_PROVIDER": "OPEN_AI",
                "OPENAI_API_KEY": "your_openai_api_key_here"
            },
            "args": [
                "build/index.js"
            ],
            "command": "node"
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "code review": {
            "env": {
                "LLM_PROVIDER": "OPEN_AI",
                "OPENAI_API_KEY": "your_openai_api_key_here"
            },
            "args": [
                "build/index.js"
            ],
            "command": "node"
        }
    }
}

JSON

{
    "mcpServers": {
        "code review": {
            "env": {
                "LLM_PROVIDER": "OPEN_AI",
                "OPENAI_API_KEY": "your_openai_api_key_here"
            },
            "args": [
                "/c",
                "node",
                "build/index.js"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "code review": {
            "env": {
                "LLM_PROVIDER": "OPEN_AI",
                "OPENAI_API_KEY": "your_openai_api_key_here"
            },
            "args": [
                "build/index.js"
            ],
            "command": "node"
        }
    }
}

From the repository

npm install

LLM_PROVIDER=OPEN_AI
OPENAI_API_KEY=your_openai_api_key_here
```

`analyze_repo`

Flattens a codebase using Repomix to provide a high-level overview of its structure and organization.

`code_review`

Performs a comprehensive code quality assessment, identifying issues and providing actionable recommendations for improvement.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "code review": {
            "env": {
                "LLM_PROVIDER": "OPEN_AI",
                "OPENAI_API_KEY": "your_openai_api_key_here"
            },
            "args": [
                "build/index.js"
            ],
            "command": "node"
        }
    }
}

Linux

{
    "env": {
        "LLM_PROVIDER": "OPEN_AI",
        "OPENAI_API_KEY": "your_openai_api_key_here"
    },
    "args": [
        "build/index.js"
    ],
    "command": "node"
}

Macos

{
    "env": {
        "LLM_PROVIDER": "OPEN_AI",
        "OPENAI_API_KEY": "your_openai_api_key_here"
    },
    "args": [
        "build/index.js"
    ],
    "command": "node"
}

Windows

{
    "env": {
        "LLM_PROVIDER": "OPEN_AI",
        "OPENAI_API_KEY": "your_openai_api_key_here"
    },
    "args": [
        "/c",
        "node",
        "build/index.js"
    ],
    "command": "cmd"
}

# 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 ```bash # 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: ```bash cp .env.example .env ``` Edit the `.env` file to set up your preferred LLM provider and API key: ```bash # 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: ```bash # 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: ```bash node build/cli.js <repo_path> [options] ```

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Details

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Setting up with Highlight

From the repository

analyze_repo

code_review

Claude Desktop / Cursor

Linux

Macos

Windows

Install

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Context 7

Sequential Thinking

`analyze_repo`

`code_review`