mcp-code-sanitizer

Strict AI code reviewer powered by Groq. Finds bugs, SQL injections, hardcoded secrets and vulnerabilities. Scores code 0–100 with concrete fixes.

Strict AI-powered code reviewer for Claude Desktop, Cursor, VS Code, and Claude Code CLI. Finds bugs, vulnerabilities, and security issues — powered by Groq (free API).

Claude / Cursor / VS Code ──MCP──► code-sanitizer ──REST──► Groq API (server.py) (llama-3.3-70b)

{ "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"] }

claude mcp add code-sanitizer -e GROQ_API_KEY=gsk_your_key -- uvx mcp-code-sanitizer

{ "mcpServers": { "code-sanitizer": { "command": "uvx", "args": ["mcp-code-sanitizer"], "env": { "GROQ_API_KEY": "gsk_your_key_here" } } } }

{ "mcpServers": { "code-sanitizer": { "command": "uvx", "args": ["mcp-code-sanitizer"], "env": { "GROQ_API_KEY": "gsk_your_key_here" } } } }

{ "servers": { "code-sanitizer": { "command": "uvx", "args": ["mcp-code-sanitizer"], "env": { "GROQ_API_KEY": "gsk_your_key_here" } } } }

git clone https://github.com/notasandy/mcp-code-sanitizer cd mcp-code-sanitizer pip install -r requirements.txt cp .env.example .env # add your GROQ_API_KEY python server.py

{ "command": "python", "args": ["/full/path/to/server.py"], "env": { "GROQ_API_KEY": "gsk_your_key_here" } }

# .github/workflows/ai-review.yml name: AI Code Review on: pull_request: types: [opened, synchronize] permissions: contents: read pull-requests: write jobs: review: runs-on: ubuntu-latest steps: - uses: notasandy/mcp-code-sanitizer@v1 with: groq_api_key: ${{ secrets.GROQ_API_KEY }}

Review this code for vulnerabilities: def get_user(user_id): query = f"SELECT  FROM users WHERE id = {user_id}" return db.execute(query)

analyze_file /path/to/my_script.py generate_tests for this function: ... compare_code — before vs after refactor, did it get better? generate_report and save to /tmp/report.html

mcp-code-sanitizer/ ├── server.py # FastMCP entry point ├── config.py # Constants — keys, limits, extension map ├── groq_client.py # Async Groq client with auto-retry on 429 ├── cache.py # In-memory LRU 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 output

All settings via.envor environment variables:

PRs and Issues are welcome. Most wanted:

- Support for other LLM providers (OpenAI, Anthropic)
- New tools: dependency audit, complexity score, docstring generator
- Prompt improvements and new language support

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

- PyPI
- Groq Console — free API key
- FastMCP docs
- MCP specification
- Smithery
- MCP Registry

An MCP server for the DeepSeek API, providing code review, file management, and account management.