Code Review Server

Name: Code Review Server
Author: crazyrabbitLTC

@crazyrabbitLTC

リポジトリ

Code Review Server について

A MCP server for code reviews

基本情報

カテゴリ

バージョン管理

ランタイム

node

トランスポート

stdio

公開者

crazyrabbitLTC

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

ツール

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ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Code Review Server?

A custom MCP server that performs code reviews by flattening repositories with Repomix and analyzing them using Large Language Models (LLMs). It is intended for developers who want structured, AI‑powered code reviews with multiple LLM provider options.

How to use Code Review Server?

Clone the repository, run npm install and npm run build, then create a .env file with your preferred LLM provider and API key. Start the MCP server with node build/index.js or use the CLI tool (node build/cli.js <repo_path> [options]). The server exposes two tools: analyze_repo (flattens a codebase) and code_review (performs an LLM‑based review).

Key features of Code Review Server

Flatten codebases using Repomix
Analyze code with multiple LLMs (OpenAI, Anthropic, Gemini)
Get structured reviews with issues, strengths, and recommendations
Handle chunking for large codebases
Supports CLI and MCP protocol usage

Use cases of Code Review Server

Quickly scan a repository’s structure before a detailed review
Perform comprehensive code quality assessments with severity ratings
Identify security vulnerabilities, performance bottlenecks, or maintenance issues
Focus reviews on specific files, file types, or focus areas (e.g., security, quality)

FAQ from Code Review Server

What LLM providers are supported?

OpenAI (default gpt‑4o), Anthropic (default claude‑3‑opus‑20240307), and Gemini (default gemini‑1.5‑pro). You can also override the default models for each provider.

How do I configure the server?

Set your preferred provider via LLM_PROVIDER and the corresponding API key in a .env file (e.g., OPENAI_API_KEY). Optional model overrides are available.

What does the code review output look like?

A structured JSON object containing a summary, a list of issues (each with type, severity, description, line numbers, and recommendation), a list of strengths, and overall recommendations.

How are large codebases handled?

The server chunks the code to fit within LLM context limits, and includes retry logic for API resilience.

Does the server require a database or external storage?

No external database is mentioned; the server works with local repositories and communicates via the MCP protocol using API keys for authentication.

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