#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

An MCP server to get Air Quality Data using AQICN.org

ISO 9001 Model Context Protocol Server Implementation

An MCP (Model Context Protocol) server that exposes Interzoid's AI-powered data quality, matching, enrichment, and standardization APIs to AI agents and LLM applications. This MCP server makes 29 Interzoid APIs discoverable and callable by any MCP-compatible client including Claude Desktop, Claude Code, Cursor, Windsurf, and other AI tools. AI agents can discover the available data quality tools and invoke them as needed during conversations and workflows. More info at interzoid.com

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.

WIP - MCP server for querying Google Map's environmental API to retrieve air quality data for a geolocation

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.

BrowserStack's Official MCP Server

The SonarQube MCP Server is a Model Context Protocol (MCP) server that enables seamless integration with SonarQube Server or Cloud for code quality and security. It also supports the analysis of code snippet directly within the agent context.

Description KnowAir Weather MCP is a comprehensive weather and air quality Model Context Protocol (MCP) server. It provides real-time meteorological data and detailed air quality monitoring (PM2.5, PM10, O₃, SO₂, NO₂, CO, AQI – CN & US standards), along with short- and long-term forecasts and astronomical information. Built with Python 3.12+ and FastMCP, it integrates with the Caiyun Weather API to deliver high-precision environmental intelligence. Features • 🌤️ Meteorology: real-time temperature, humidity, wind, precipitation • 🏙️ Air Quality: pollutant concentrations + AQI (CN & US) • 📅 Forecasts: 72-hour hourly and 7-day daily predictions • 🌧️ Minute-level precipitation: hyper-local rain/snow timing (China cities) • 🌅 Astronomy: sunrise, sunset, moon phases • ⚠️ Alerts: real-time weather and air quality warnings Example Usage Ask Claude (with this MCP enabled): “What’s the current weather and air quality in Beijing?” “Get comprehensive environmental data for 116.4575, 39.9113”

Open-Meteo MCP Server - A comprehensive Model Context Protocol server that provides AI models with complete access to Open-Meteo's free weather API. Get real-time weather forecasts, historical climate data, air quality information, marine conditions, and seasonal projections through a simple MCP interface. Supports all major weather models (ECMWF, GFS, ICON, JMA, Météo-France) with data covering temperature, precipitation, wind, humidity, UV index, and specialized datasets for research and professional applications.

Zenable cleans up sloppy AI code, prevents vulnerabilities, and automates governance with deterministic guardrails so developers can ship faster, safer, and with confidence. Use Cases: 1. AI coding assistants generate vulnerable code with security flaws and compliance violations. Zenable acts as a real-time safety net that catches SQL injections, hardcoded secrets, and policy violations as code is written - ensuring you ship AI-accelerated code with confidence and zero security compromises. 2. Rapid AI-driven development introduces inconsistent patterns and technical debt. Zenable provides automated governance checks that ensure every AI-generated feature meets your standards, enabling 10x development speed without sacrificing code quality. 3. Bugs slip through reviews and automated testing misses edge cases. Zenable provides AI-powered analysis that identifies subtle bugs and suggests fixes before production, resulting in fewer production incidents.

TestDino MCP boosts your AI assistant with powerful tools and analysis capabilities. It lets your AI analyze test runs, perform root-cause analysis, and detect failure patterns

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.