<!-- mcp-name: io.github.notasandy/mcp-code-sanitizer -->

🔍 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.

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.

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💬 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	⚡⚡	⭐⭐⭐⭐

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📦 Requirements

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

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🤝 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

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📄 License

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

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🔗 Links

• FastMCP docs
• Groq Console
• MCP specification
• Smithery — MCP server catalog