Building a High-Speed Code Sanitizer MCP Server with Groq and Llama 3

I built an MCP server that reviews your code with Groq — here’s what it found

Developer Sandy built mcp-code-sanitizer, an MCP server that integrates with Claude Desktop and Cursor to provide strict AI-driven code reviews. The system utilizes Groq’s Llama-3.3-70b model to identify critical vulnerabilities like SQL injections in under two seconds.

Why This Matters

While AI tools like GitHub Copilot and ChatGPT accelerate development, they frequently introduce subtle security risks and insecure patterns that pass standard linters. By moving code review directly into the AI agent workflow via the Model Context Protocol (MCP), developers can catch vulnerabilities during the generation phase rather than after deployment, reducing the technical debt and security overhead associated with LLM-generated code.

Key Insights

• Groq’s Llama-3.3-70b model provides high-speed structured JSON output in 1-2 seconds (2026).
• The analyze_code tool identifies vulnerabilities and assigns scores, such as rating a specific SQL injection flaw at 23/100.
• The FastMCP server entry point manages the core tool logic in just 39 lines of Python code.
• In-memory caching with TTL prevents redundant API calls to Groq when reviewing identical code blocks during iterative debugging.
• Parallel chunking via the analyze_file tool allows for efficient analysis of entire source files.

Working Examples

Vulnerable code sample provided to the sanitizer for testing.

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

The structured JSON output returned by the sanitizer identifying the vulnerability.

{
  "summary": "Critical SQL injection vulnerability",
  "score": 23,
  "issues": [{
    "severity": "critical",
    "line": 2,
    "title": "SQL Injection",
    "description": "f-string directly interpolates user_id into SQL query",
    "fix": "cursor.execute('SELECT * FROM users WHERE id = %s', (user_id,))"
  }]
}

GitHub Action configuration for automated PR reviews.

- uses: actions/checkout@v4
# ... runs review_pr.py on changed files
# posts comment with issues, warnings, suggestions
# fails check if critical issues found

Practical Applications

• Use Case: Integrating mcp-code-sanitizer into GitHub Actions to automatically review every PR and post structured comments. Pitfall: Failing to handle rate limits in high-volume repositories without the built-in auto-retry client.
• Use Case: Using the explain_code tool within Claude Desktop for step-by-step logic walkthroughs for junior engineers. Pitfall: Accepting AI-generated code fixes without human verification, despite the strict senior-engineer persona of the reviewer.

References:

• https://dev.to/notasandy/i-built-an-mcp-server-that-reviews-your-code-with-groq-heres-what-it-found-3g7f
• https://github.com/notasandy/mcp-code-sanitizer
• https://console.groq.com
• https://glama.ai/mcp/servers