MCP Server - Code Review

@arahisman

代码仓库

基本信息

分类

版本控制

传输方式

stdio

作者

arahisman

关于 MCP Server - Code Review

暂无概览

#mcp-server #code-review #ai-chat

MCP Server - Code Review 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-server-code-review": {
      "command": "node",
      "args": [
        "test-mcp-server.js"
      ]
    }
  }
}

MCP Server - Code Review 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is MCP Server - Code Review?

A server implementation for the Model Context Protocol (MCP) that connects Cursor to OpenRouter API via stdio, enabling code review and AI chat functionalities. It is designed for developers using Cursor who want automated code review with project context and access to various large language models.

How to use MCP Server - Code Review?

Install globally or locally via npm (npm install -g mcp-server-code-review). Run from the command line: mcp-server-code-review --api-key YOUR_OPENROUTER_API_KEY [--default-model MODEL_ID]. The server implements two MCP tools: ask for text prompts and code_review for project‑aware code review. Configure the server in Cursor by adding a "mcp-openrouter-stdio" entry using npx with your API key and optional model.

Key features of MCP Server - Code Review

Connects to OpenRouter API for various large language models
Provides ask tool for simple text prompt responses
Provides code_review tool with project context analysis
Integrates with repomix for codebase packaging and analysis
Supports multi‑language project detection automatically
Runs as a stdio server compatible with MCP protocol

Use cases of MCP Server - Code Review

Get AI‑powered answers to coding questions directly in Cursor.
Automate code reviews with context from completed tasks and work reports.
Analyze entire project directories for code quality and suggestions.
Use from any MCP‑compatible editor (e.g., Cursor) for flexible AI chat.

常见问题

What is the OpenRouter API key and how do I get one?

The API key is required to connect to OpenRouter. You can obtain one at [OpenRouter](https://openrouter.ai).

What are the runtime requirements?

Node.js version 16 or higher and an OpenRouter API key. No other external dependencies are needed.

What LLM models can be used?

Any model available on OpenRouter. The default is `google/gemini-2.0-flash-thinking-exp:free`. You can override it with the `--default-model` option or per‑call in the `code_review` tool.

How does the `code_review` tool differ from the `ask` tool?

The `ask` tool sends a simple text prompt and returns the response. The `code_review` tool includes project‑specific context such as completed tasks, work report, project path, and planned tasks, enabling a more thorough analysis.

What programming languages are supported for project detection?

JavaScript/TypeScript, Python, C#, PHP, Java, Ruby, Go, Rust, C++, and Swift. The server automatically detects the primary language.

版本控制分类下的更多 MCP 服务器

Atlassian Bitbucket MCP Server

aashari

Node.js/TypeScript MCP server for Atlassian Bitbucket. Enables AI systems (LLMs) to interact with workspaces, repositories, and pull requests via tools (list, get, comment, search). Connects AI directly to version control workflows through the standard MCP interface.

157

Bitbucket Server MCP

garc33

GitHub MCP Server Extension for Zed

LoamStudios

A GitHub MCP Server extension for Zed

Git

modelcontextprotocol

Model Context Protocol Servers

88.1K

Atlassian Jira MCP Server