"How to fix SonarQube findings" matching MCP servers:

Read-only MCP server that exposes SonarQube Web API tools for issue retrieval, quality gate status, and source context, enabling coding agents to fix code issues.

Enables Cursor AI to query SonarQube for pull request metrics, issues, file issues, and duplication reports directly, without manual data export.

Enables read-only exploration of SonarQube projects, explaining why a quality gate is red and searching unresolved issues, reading metrics and source snippets around a finding. All six tools are idempotent and leave the SonarQube instance untouched.

Enables AI assistants to scan deployed web apps by sending a flock of AI users that identify UX issues, mobile breakage, and conversion path problems, returning prioritized, fix-ready findings.

Enables MCP clients to launch AI persona tests against public web app URLs in a real browser, then retrieve status, findings, screenshots, and combined fix prompts for coding agents.

Enables AI agents to audit KiCad PCB designs through deterministic DRC/ERC checks, spatial heuristics, and multimodal vision review, returning structured, prioritized findings with coordinates and fix guidance.

Enables VS Code's AI assistant to query and manage SonarQube projects, quality gates, measures, issues, hotspots, branches, and system status through the SonarQube REST API over stdio.

Enables seamless integration with SonarQube Server or Cloud for code quality and security analysis. Supports querying project metrics, issues, and analyzing code snippets directly within the agent context.

Enables AI coding assistants to scan an iOS, Android, Flutter, React Native or Expo project for App Store and Google Play rejection risks, returning deterministic findings that cite the guideline section, link to the official Apple or Google page and explain how to fix each problem. Nothing leaves the machine and no AI is used in the checks.

An MCP server that measures how faithfully one UI reproduces another, returning a score and actionable findings for improvement.

Provides a testable gateway path for running a local MCP-style server in hosted-like conditions, with tenant-isolated reads, approval-denied writes, deterministic failures, and smoke-test verification.

Enables access to SonarQube code quality analysis through MCP, including issue searching, metrics retrieval, and quality gate status.

Enables AI agents to submit npm, PyPI, Go, or Maven packages and manifests to the free OSV.dev database and receive per-package and per-advisory results with CVE mappings, CVSS scores, severities, and the versions that fix each issue. It also supports filtering by minimum severity, de-duplicating alias advisories, and machine-readable error/warning rows for CI gates and upgrade recommendations.

Agent-readiness scorecard for any MCP server: protocol checks, 0-100 score and actionable findings.

Enables AI coding tools to run CheckMyLaunch scans on GitHub repos or live URLs and retrieve findings and repair prompts directly within the chat.

Enables conversational analysis of n8n workflows and Make scenarios for silent-failure risks, with tools to explain findings, compare workflow versions, and list rules.

A Model Context Protocol (MCP) server for interacting with SonarQube code quality platform.

MCP server that reviews and fixes code across four dimensions (security, standards, production readiness, sustainability), runs tests to verify fixes, and provides findings with diffs.

Screen-reader navigation cost analyzer. Measures how hard it is for assistive-technology users to discover, reach, and operate interactive targets on the web.

AI-powered QA testing platform — catches JS errors, accessibility failures, visual regressions, API loops, security issues, and more via Chrome DevTools MCP

Audits spacing, gaps, and element sizes in AI-generated UIs and returns fix hints the agent applies. Tailwind-first, with accessibility fallback.

Audits any site's UX the way a design-literate reviewer would — contrast, tap targets, type scale, colour discipline, scan patterns, copy — and returns the rule, the source line and the exact fix.

Enables a language model to run four deterministic data checks it cannot argue with — column-contract validation with an explicit quarantine list, arithmetic reconciliation of opening/movements/closing, distinct entity counting against row counts, and duplicate detection returned for human review — each answering with structured findings and row/column locators. Failures come back as results with reasons, so the model reports exactly which rows and columns are broken rather than asserting the data is clean.

Enables coding agents to compile supplied files, run optional focused tests, and get static findings across ten programming languages, providing a deterministic feedback loop on the code they are about to return.

Enables independent, read-only adversarial review of candidate test cases and bug findings, returning a review delta that authoring agents reconcile before writing final artifacts.

Enables MCP clients to run fresh audits of any public web page on mobile or desktop, returning category scores, lab metrics, and prioritized findings for performance, accessibility, best practices, and SEO. Each call performs a new uncached audit, so users can compare results within a conversation.

Enables AI coding agents to perform real-browser accessibility scanning of localhost pages using Playwright and axe-core, returning WCAG 2.1 violations with structured fix plans.

A local-first MCP server that scores your codebase's Build Readiness by reading code and running tests on your machine, outputting a diligence-grade score and risk register without uploading your source.

MCP server for semi-manual QA test execution on real Smart TVs (Tizen/webOS) and local Chrome via Chrome DevTools Protocol, enabling remote control, state inspection, and profiling.

Enables AI assistants to diagnose and fix iOS app issues that cause App Store rejections, such as missing privacy manifests, permission strings, and test credentials, before submission.