Which integrations are available for this server?

Generates Jest test cases with happy path, edge cases, and security tests. Generates pytest test cases with happy path, edge cases, and security tests.

🔍 mcp-code-sanitizer

A strict AI-powered code reviewer that runs your code through Groq LLM directly from Claude Desktop, Cursor, or any MCP-compatible agent.

Python FastMCP Groq License

Claude Desktop  ──MCP──►  code-sanitizer  ──REST──►  Groq API
                            (server.py)               (llama-3.3-70b)

✨ Features

Tool	Description
`analyze_code`	Strict code review — bugs, vulnerabilities, score 0–100
`compare_code`	Compares two versions, finds regressions, recommends merge/request_changes
`explain_code`	Step-by-step explanation for junior/middle/senior audience
`generate_tests`	Generates pytest/jest/go test with happy path, edge cases, security tests
`analyze_file`	Analyzes a whole file from disk with parallel chunking
`generate_report`	Builds a beautiful HTML report from any analysis result
`cache_info`	Cache statistics and clearing

Example response

{
  "summary": "Critical SQL injection and secret exposed in logs",
  "score": 23,
  "issues": [
    {
      "severity": "critical",
      "line": 2,
      "title": "SQL Injection",
      "description": "f-string directly interpolates user_id into query",
      "fix": "cursor.execute('SELECT * FROM users WHERE id = %s', (user_id,))"
    }
  ],
  "warnings": [{"title": "No exception handling", "description": "..."}],
  "suggestions": ["Consider using an ORM instead of raw SQL"]
}

🚀 Quick Start

1. Clone the repository

git clone https://github.com/YOUR_USERNAME/mcp-code-sanitizer
cd mcp-code-sanitizer

2. Create virtual environment and install dependencies

python -m venv venv

# macOS / Linux
source venv/bin/activate

# Windows
venv\Scripts\activate

pip install -r requirements.txt

3. Add your Groq API key

Get a free key at console.groq.com/keys

cp .env.example .env
# Open .env and set GROQ_API_KEY=gsk_...

4. Test the server

python server.py

Silence means it's working — the server is listening for MCP requests via stdio.

🔌 Connect to Claude Desktop

Find your config file and add the mcpServers section:

OS	Config path
macOS	`~/Library/Application Support/Claude/claude_desktop_config.json`
Windows	`%APPDATA%\Claude\claude_desktop_config.json`
Linux	`~/.config/Claude/claude_desktop_config.json`

{
  "mcpServers": {
    "code-sanitizer": {
      "command": "/full/path/to/venv/bin/python",
      "args": ["/full/path/to/server.py"],
      "env": {
        "GROQ_API_KEY": "gsk_your_key_here"
      }
    }
  }
}

Restart Claude Desktop — you'll see the 🔧 icon in chat.

🔌 Connect to Cursor

Create .cursor/mcp.json in your project root:

{
  "mcpServers": {
    "code-sanitizer": {
      "command": "/full/path/to/venv/bin/python",
      "args": ["/full/path/to/server.py"],
      "env": {"GROQ_API_KEY": "gsk_your_key_here"}
    }
  }
}

🧪 Testing via MCP Inspector

source venv/bin/activate  # or venv\Scripts\activate on Windows
fastmcp dev inspector server.py

A browser UI opens with full tool testing interface.

💬 Usage in chat

After connecting to Claude Desktop, just write:

Review this code for vulnerabilities:

def get_user(user_id):
    query = f"SELECT * FROM users WHERE id = {user_id}"
    return db.execute(query)

Or explicitly call a tool:

Use analyze_file on /path/to/my_script.py
Generate tests for this function: ...
Compare these two versions and tell me if it got better: ...

🏗️ Architecture

mcp-code-sanitizer/
├── server.py          # FastMCP entry point (39 lines)
├── config.py          # Constants — keys, limits, mappings
├── groq_client.py     # Groq API client with auto-retry on rate limits
├── cache.py           # In-memory cache with TTL
├── prompts.py         # System prompts for all tools
└── tools/
    ├── analyze.py     # analyze_code
    ├── compare.py     # compare_code
    ├── explain.py     # explain_code
    ├── tests.py       # generate_tests
    ├── file_tool.py   # analyze_file (chunking + parallel analysis)
    ├── cache_tool.py  # cache_info
    └── report.py      # generate_report (HTML)

⚙️ Configuration

All settings via environment variables or .env:

Variable	Default	Description
`GROQ_API_KEY`	—	Required. Get at console.groq.com
`GROQ_MODEL`	`llama-3.3-70b-versatile`	Groq model
`CACHE_TTL`	`3600`	Cache TTL in seconds
`CACHE_MAX`	`200`	Max cache entries

Available Groq models

Model	Speed	Quality
`llama-3.3-70b-versatile`	⚡⚡	⭐⭐⭐⭐⭐ (default)
`llama-3.1-8b-instant`	⚡⚡⚡	⭐⭐⭐
`mixtral-8x7b-32768`	⚡⚡	⭐⭐⭐⭐

📦 Requirements

fastmcp>=2.3.0
httpx>=0.27.0
python-dotenv>=1.0.0

🤝 Contributing

PRs and Issues are welcome! Especially interested in:

Support for other LLM providers (OpenAI, Anthropic)
New tools (security audit, dependency check, complexity analysis)
Prompt improvements

📄 License

MIT — do whatever you want. A GitHub star would be appreciated ⭐

MCP Code Sanitizer