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Code Review Server

@crazyrabbitLTC

Repository

About Code Review Server

A MCP server for code reviews

Config

Add this server to your MCP-compatible client using the configuration below.

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

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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.

Frequently asked questions

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.

Comments

Basic information

Category

Version Control

Transports

stdio

Author

crazyrabbitLTC

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