Which integrations are available for this server?

Utilizes Google's AI infrastructure, including Gemini and Vertex AI, to power automated code quality assessments and requirement validation. Uses Google Cloud's Vertex AI platform to access high-performance models for code review workflows and security analysis. Serves as the default AI provider for analyzing code for bugs, scoring quality, and validating alignment with requirements documents. Integrated via Azure OpenAI to provide ChatGPT-powered code reviews, best practice suggestions, and unified diff generation for code improvements.

How do I use MCP Code Reviewer?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Code Reviewer review auth.py for security vulnerabilities and propose fixes" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

MCP Code Refiner

A powerful second-layer LLM MCP server that refines and reviews code using AI. Perfect for improving AI-generated code or enhancing human-written code through natural language feedback.

What is This?

This is an MCP (Model Context Protocol) server that adds code refinement and review capabilities to any MCP client like Claude Desktop. It acts as a "second layer" AI that specializes in code improvement, working alongside your primary AI assistant.

Use it to:

Refine code generated by ChatGPT, Claude, or any AI with natural language feedback
Get comprehensive code reviews with security and performance analysis
Iteratively improve code until it meets your standards
Learn from AI-suggested improvements

Features

Code Refinement - Improve code with natural language feedback ("make it more logical", "add error handling")
Code Review - AI-powered analysis for bugs, security, performance, and best practices
Multi-Model Support - Choose between Gemini, Claude, or OpenAI models
Plug & Play - Works with Claude Desktop and any MCP client
Smart Prompts - Optimized prompts for high-quality, actionable results
Diff View - See exactly what changes before applying them

Quick Start

Prerequisites

Python 3.10 or higher
At least one AI provider API key (Gemini recommended for free tier)

1. Clone and Install

git clone https://github.com/yourusername/mcp_code_review.git cd mcp_code_review python -m venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate pip install -r requirements.txt

2. Configure API Keys

Create a .env file from the example:

cp .env.example .env

Edit .env and add at least ONE API key:

# Recommended: Google Gemini (free tier available) GOOGLE_API_KEY=your-gemini-api-key-here # Alternative: Anthropic Claude ANTHROPIC_API_KEY=your-anthropic-api-key-here # Alternative: OpenAI OPENAI_API_KEY=your-openai-api-key-here

Get API keys from:

Gemini: https://ai.google.dev/
Claude: https://console.anthropic.com/
OpenAI: https://platform.openai.com/api-keys

3. Connect to Claude Desktop

Edit your Claude Desktop config file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

Add the server configuration:

{ "mcpServers": { "code-refiner": { "command": "python", "args": ["/absolute/path/to/mcp_code_review/mcp_server.py"], "env": { "GOOGLE_API_KEY": "your-gemini-api-key" } } } }

Important: Replace /absolute/path/to/mcp_code_review/ with the actual path on your system.

Restart Claude Desktop to load the server.

Usage

Once configured, just talk to Claude naturally in Claude Desktop. The tools are automatically available!

Code Refinement

Improve existing code with natural language instructions:

You: "Refine ./my_script.py to make it more logical and add error handling"

Claude will:

Call refine_code_tool with your request
Show you a diff of proposed changes
Explain what was changed and why
Ask for your approval
Apply changes with apply_refinement_tool if you confirm

Code Review

Get comprehensive code analysis:

You: "Review ./server.py for security issues and performance problems"

Claude will:

Call review_code_tool on the file
Show issues found with severity levels (high/medium/low)
Highlight code strengths
Provide an overall quality score
Suggest specific improvements

Real-World Examples

Refinement:

"Make ./app.py more performant by optimizing loops"
"Simplify the logic in ./utils/helper.py"
"Add comprehensive error handling to ./api/routes.py"
"Refactor ./legacy_code.py to follow modern Python best practices"
"Add type hints and docstrings to ./calculator.py"

Review:

"Review ./authentication.py for security vulnerabilities"
"Check ./database.py for SQL injection risks"
"Analyze ./api_client.py for error handling issues"
"Review ./main.py and suggest improvements"

Available Models

Configure via ai_provider parameter or Claude will use the default (gemini).

Gemini (Google)

gemini - Gemini 2.0 Flash (fast, free tier)
gemini-pro - Gemini 1.5 Pro (more capable)

Claude (Anthropic)

claude or claude-sonnet - Claude 3.5 Sonnet (high quality)
claude-opus - Claude 3 Opus (most capable)
claude-haiku - Claude 3.5 Haiku (fastest)

OpenAI

openai or gpt-4o - GPT-4o (balanced)
gpt-4 - GPT-4 Turbo
gpt-3.5 - GPT-3.5 Turbo (fastest)

MCP Tools Reference

This server provides three MCP tools that Claude Desktop can call automatically:

1. `refine_code_tool`

Purpose: Improves existing code based on natural language feedback using a second-layer LLM.

Parameters:

user_request (string, required) - What you want to improve (e.g., "make it more logical", "add error handling")
file_path (string, required) - Path to the code file to refine
ai_provider (string, optional) - AI model to use (default: "gemini")

Returns:

{ "status": "success", "explanation": "Added error handling and simplified logic...", "diff": "--- original\n+++ refined\n...", "refined_code": "def improved_function():\n ...", "file_path": "./app.py" }

2. `review_code_tool`

Purpose: Analyzes code for bugs, security vulnerabilities, performance issues, and quality.

Parameters:

file_path (string, required) - Path to the code file to review
ai_provider (string, optional) - AI model to use (default: "gemini")

Returns:

{ "status": "success", "issues": [ { "severity": "high", "category": "security", "issue": "SQL injection vulnerability", "line": 42, "suggestion": "Use parameterized queries..." } ], "strengths": ["Good error handling", "Clear naming"], "overall_assessment": "Code is functional but has security concerns...", "score": 7 }

3. `apply_refinement_tool`

Purpose: Applies refined code to the file after user approval.

Parameters:

file_path (string, required) - Path to the file to update
refined_code (string, required) - The improved code from refine_code_tool

Returns:

{ "status": "success", "message": "Code successfully applied to ./app.py" }

Important: Only use this after the user has reviewed and approved the changes!

Testing

Test the server without Claude Desktop:

python client.py

This runs a simple test client to verify the server works.

Project Structure

mcp_code_review/ ├── mcp_server.py # Main MCP server entry point ├── client.py # Test client for local testing ├── requirements.txt # Python dependencies ├── .env.example # Environment variables template ├── .env # Your API keys (git-ignored) │ ├── tools/ # MCP tool implementations │ ├── __init__.py │ ├── file_ops.py # File read/write utilities │ ├── code_refinement.py # Code refinement logic │ └── code_review.py # Code review logic │ ├── prompts/ # AI prompt templates │ ├── code_refinement.txt # Refinement prompt template │ └── code_review.txt # Review prompt template │ └── utils/ # Helper utilities ├── __init__.py ├── llm_client.py # LiteLLM wrapper for multi-provider support └── diff_generator.py # Unified diff generation

How It Works

This server implements a "second-layer LLM" architecture:

You interact with Claude Desktop (first-layer AI) using natural language
Claude understands your intent and calls the appropriate MCP tool
MCP Server receives the request and invokes a second-layer LLM specialized for code tasks
Second-layer LLM analyzes or refines the code using optimized prompts
Results are returned to Claude with diffs, explanations, and suggestions
Claude presents the results to you for review
You approve or reject the changes
Changes are applied only after your confirmation

This two-layer approach combines Claude's conversational abilities with specialized code analysis/refinement models.

Use Cases

1. Refining AI-Generated Code

First LLM generates code → Use this to improve it

2. Code Review Assistant

Get AI-powered feedback on your code

3. Iterative Improvement

Keep refining until perfect

4. Learning Tool

See how AI would improve your code and learn from it

Requirements

Python 3.10 or higher
At least one AI provider API key (Gemini recommended for free tier)
Dependencies listed in requirements.txt:
- fastmcp - FastMCP framework
- mcp - Model Context Protocol SDK
- litellm - Multi-provider LLM wrapper
- rich - Terminal formatting
- python-dotenv - Environment variable management

Troubleshooting

Server Not Appearing in Claude Desktop

Check that the path in claude_desktop_config.json is absolute, not relative
Verify the Python path is correct (use which python in your activated venv)
Check Claude Desktop logs for errors:
- macOS: ~/Library/Logs/Claude/
- Windows: %APPDATA%\Claude\logs\
Restart Claude Desktop after config changes

API Key Errors

Verify your API key is correct in the .env file
Make sure the key is also in the claude_desktop_config.json env section
Check that you have API credits/quota remaining
Try using a different AI provider as a fallback

File Path Issues

Always use absolute paths or paths relative to where you run the command
On Windows, use forward slashes / or escaped backslashes \\
Verify the file exists: ls /path/to/file.py

Module Import Errors

Ensure virtual environment is activated
Reinstall dependencies: pip install -r requirements.txt --upgrade
Check Python version: python --version (must be 3.10+)

Testing the Server

Run the test client to verify the server works:

python client.py

This bypasses Claude Desktop and tests the MCP server directly.

Contributing

Contributions are welcome! Here's how you can help:

Report bugs - Open an issue with details about the problem
Suggest features - Share ideas for new capabilities
Improve prompts - The prompt templates in prompts/ can always be refined
Add AI providers - Extend support for additional LLM providers
Submit PRs - Fix bugs, add features, improve documentation

License

MIT License - see LICENSE file for details

Acknowledgments

Built with:

FastMCP - FastMCP framework for building MCP servers
LiteLLM - Unified interface for multiple LLM providers
MCP Protocol - Model Context Protocol specification

Resources

Questions or issues? Open an issue on GitHub or check the troubleshooting section above.

MCP Code Refiner

What is This?

Features

Quick Start

Prerequisites

1. Clone and Install

2. Configure API Keys

3. Connect to Claude Desktop

Usage

Code Refinement

Code Review

Real-World Examples

Available Models

Gemini (Google)

Claude (Anthropic)

OpenAI

MCP Tools Reference

1. refine_code_tool

2. review_code_tool

3. apply_refinement_tool

Testing

Project Structure

How It Works

Use Cases

1. Refining AI-Generated Code

2. Code Review Assistant

3. Iterative Improvement

4. Learning Tool

Requirements

Troubleshooting

Server Not Appearing in Claude Desktop

API Key Errors

File Path Issues

Module Import Errors

Testing the Server

Contributing

License

Acknowledgments

Resources

Resources

Appeared in Searches

Latest Blog Posts

MCP directory API

1. `refine_code_tool`

2. `review_code_tool`

3. `apply_refinement_tool`