Server data from the Official MCP Registry
Strict AI code reviewer powered by Groq. Finds bugs and vulnerabilities.
Strict AI code reviewer powered by Groq. Finds bugs and vulnerabilities.
Valid MCP server (0 strong, 3 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry. 1 finding(s) downgraded by scanner intelligence.
13 files analyzed Β· 1 issue found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
This plugin requests these system permissions. Most are normal for its category.
Set these up before or after installing:
Environment variable: GROQ_API_KEY
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-notasandy-mcp-code-sanitizer": {
"env": {
"GROQ_API_KEY": "your-groq-api-key-here"
},
"args": [
"mcp-code-sanitizer"
],
"command": "uvx"
}
}
}From the project's GitHub README.
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 ββMCPβββΊ code-sanitizer ββRESTβββΊ Groq API
(server.py) (llama-3.3-70b)
| Tool | Description |
|---|---|
analyze_code | Strict code review β bugs, vulnerabilities, score 0β100 |
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 |
{
"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"]
}
git clone https://github.com/YOUR_USERNAME/mcp-code-sanitizer
cd mcp-code-sanitizer
python -m venv venv
# macOS / Linux
source venv/bin/activate
# Windows
venv\Scripts\activate
pip install -r requirements.txt
Get a free key at console.groq.com/keys
cp .env.example .env
# Open .env and set GROQ_API_KEY=gsk_...
python server.py
Silence means it's working β the server is listening for MCP requests via stdio.
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 β you'll see the π§ icon in chat.
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"}
}
}
}
source venv/bin/activate # or venv\Scripts\activate on Windows
fastmcp dev inspector server.py
A browser UI opens with full tool testing interface.
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: ...
mcp-code-sanitizer/
βββ server.py # FastMCP entry point (39 lines)
βββ config.py # Constants β keys, limits, mappings
βββ groq_client.py # Groq API client with auto-retry on rate limits
βββ cache.py # In-memory cache with TTL
βββ prompts.py # System prompts for all tools
βββ tools/
βββ analyze.py # analyze_code
βββ compare.py # compare_code
βββ explain.py # explain_code
βββ tests.py # generate_tests
βββ file_tool.py # analyze_file (chunking + parallel analysis)
βββ cache_tool.py # cache_info
βββ report.py # generate_report (HTML)
All settings via environment variables or .env:
| Variable | Default | Description |
|---|---|---|
GROQ_API_KEY | β | 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 |
| Model | Speed | Quality |
|---|---|---|
llama-3.3-70b-versatile | β‘β‘ | βββββ (default) |
llama-3.1-8b-instant | β‘β‘β‘ | βββ |
mixtral-8x7b-32768 | β‘β‘ | ββββ |
fastmcp>=2.3.0
httpx>=0.27.0
python-dotenv>=1.0.0
PRs and Issues are welcome! Especially interested in:
MIT β do whatever you want. A GitHub star would be appreciated β
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