#code-quality

A Model Context Protocol (MCP) server for analyzing GitLab repositories and performing security assessments.

AI-powered code quality analysis using MCP to help AI assistants review code more effectively. Analyze git changes for complexity, security issues, and more through structured prompts.

MCP server for analyzing code for bugs, errors, and functionality issues

Code intelligence MCP server that makes AI coding safer. Builds a dependency graph of your entire codebase so Claude, Cursor, or any MCP client knows what breaks before making changes. 60+ tools for impact analysis, health scoring, dead code detection, and architecture enforcement. Built for vibecoders who ship fast and teams who need reliability. Supports Python, TypeScript, JavaScript.

Stop AI coding disasters before they cost you weeks. Real-time anti-pattern detection for vibe coders who love AI tools but need a safety net to avoid expensive overengineering traps.

Deterministic quality gates for AI coding agents. Rigour runs 23 automated checks on every file AI writes — structural analysis, security scanning, AI-drift detection, and agent governance. Works as an MCP server for Claude Desktop, Cursor, and Cline. Supports TypeScript, JavaScript, Python, Go, Ruby, and C#. Forces AI agents to write production-grade code with PASS/FAIL enforcement.

Generates high-quality unit tests for Python code with a coverage before/after report on every run. Derives concrete edge cases (None inputs, numeric boundaries, empty collections, exception paths) and returns ready-to-run pytest files with external I/O automatically mocked. Free tier: 100 executions. Pro: $19/month.