# Code Review

An automated code documentation generation tool based on the OpenRouter API that intelligently generates documentation, test plans, and code review reports by analyzing the codebase structure and content.

A lightweight and configurable WordPress code review MCP server that supports dynamic retrieval of development specifications and security rules from external URLs.

A TypeScript - based MCP server for fetching random LGTM (Looks Good To Me) images, suitable for developer communication scenarios such as code reviews.

The MAGI MCP Server is a server implementation of a code review system based on the Model Context Protocol (MCP), providing standardized interfaces for code submission and review processes, and supporting multi - agent reviews and majority decision - making mechanisms.

A code review server based on the MCP protocol, providing structured code analysis services using Repomix and LLMs technology

The GitHub PR Helper is a tool based on the GitHub CLI that provides PR creation and code review functions

This project builds a bridge between Claude Code and Google Gemini AI, enabling direct calls to Gemini in the Claude Code environment for Q&A, code review, and creative brainstorming, providing a convenient AI collaboration tool.

An AI-based code review tool for GitLab merge requests that uses Anthropic's Claude model to automatically analyze code quality, style, and security issues and provides review suggestions.

A Ruby-based AI pair programming MCP server that provides AI-assisted programming functions such as code review, brainstorming, performance analysis, and security check through OpenRouter.

An AI mentor server based on the Model Context Protocol, providing second opinion services such as code review, design evaluation, writing feedback, and creative brainstorming through Deepseek-Reasoning

A production-level MCP server integrated with the DeepSeek API, providing functions such as code review, file management, and API account management, supporting multi-model selection and JSON-formatted responses.

The AI Development Assistant MCP Server is an AI - based code development toolkit that provides functions such as code architecture generation, UI screenshot analysis, and code review, specifically designed for Cursor.

A custom code review server based on the MCP protocol that uses Repomix and large language models for code analysis and provides structured review reports.

An intelligent agent for handling programming assistance tasks, supporting functions such as knowledge management, documentation-driven development, code review, and test management.

A Cursor plugin tool that automatically generates Git differences and performs code reviews based on the MCP protocol, supporting GitHub PR review and Notion integration.

An MCP server project that provides a service for automatically selecting the OpenAI O3 or Google Gemini 2.5 Pro model based on the number of tokens, supports recursive embedding of file paths in prompts, and is suitable for code review and solving complex problems.

A server implementing the MCP protocol that connects Cursor via the OpenRouter API, providing code review and AI chat functions, supporting multi-language project analysis and context processing.

An MCP server for interacting with GitHub pull requests

This is an AI Development Assistant MCP Server project that provides intelligent coding assistance tools, including code architecture generation, UI screenshot analysis, and code review functions. It is mainly used for tutorial demonstrations rather than production environments.

Zen MCP is a multi-model AI collaborative development server that provides enhanced workflow tools and cross-model context management for AI coding assistants such as Claude and Gemini CLI. It supports seamless collaboration of multiple AI models to complete development tasks such as code review, debugging, and refactoring, and can maintain the continuation of conversation context between different workflows.

An enterprise - level AI assistant system based on the Model Context Protocol, with intelligent server selection, text analysis, code review, sentiment analysis, and knowledge management functions, providing an aesthetically pleasing Web interface.

A multi-agent debate framework based on the MCP protocol for code review and debate planning. It conducts code reviews by executing multiple AI agents (such as Claude and Codex) in parallel, uses a deterministic scoring system (P0/P1/P2 severity classification) and platform-specific rules, and finally merges the results to generate a review report.

An MCP server for obtaining change information of GitHub pull requests and creating Notion analysis summary pages.

MCP as a Judge is a behavioral MCP server that acts as a validation layer between AI coding assistants and LLMs. By enforcing evidence - based research, code quality reviews, and human decision - making intervention, it ensures the generation of safer and higher - quality code.

A multi-model AI code review and analysis server based on the Model Context Protocol. It supports the parallel execution of code quality checks, security analysis, and multi-agent consensus reviews by multiple AI models such as OpenAI, Anthropic, and Google Gemini.

Corbat MCP is an AI coding standards server that injects team coding specifications before the AI generates code through the MCP protocol, ensuring that the generated code meets production standards, security requirements, and passes the code review. It supports multiple programming languages and development tools.

An MCP server based on Gemini, providing code review, analysis, documentation generation, and logic verification functions, supporting 13 tools such as PR review, code smell detection, and complexity analysis.

The MCP protocol service for Bitbucket Server, providing Pull Request management functions

A GitHub code review server based on the Model Context Protocol that provides automated code review services for Pull Requests by integrating OpenRouter's AI models.

The Chain of Draft Server is an AI-driven development tool that helps developers optimize design, code, and decision-making through systematic iterative improvement.