"Cómo ver errores y advertencias en Visual Studio Code" matching MCP servers:

Enables AI-assisted visual regression testing by capturing baselines, comparing screenshots, and using AI to judge whether visual differences are bugs or acceptable changes.

Enables visual review of frontend UI variants by providing an MCP tool that opens, refreshes, verifies, captures, and closes review surfaces.

An MCP server that enables AI assistants to visually inspect and interact with rendered web pages via a persistent headless Chromium browser, supporting navigation, screenshots, clicks, viewport resizing, and console log retrieval.

Enables coding agents to run an evidence-backed visual QA loop for browser interfaces, including inspecting a running app, retrieving findings with screenshots, and rechecking only the captures affected by a fix.

Enables AI coding agents to see and interact with real Chrome pages through structured DOM inspection, targeted screenshots, recordings, and human-in-the-loop approvals across major coding-agent hosts.

Enables agents to verify educational, scientific, and engineering visuals by extracting structured evidence and running domain-specific checks against specs and theory.

An MCP server that enables AI agents to autonomously test, debug, and analyze web interfaces visually using Playwright, with 30 tools for screenshots, workflows, performance, and visual comparison.

A production MCP integration that lets AI agents control Roblox Studio to autonomously build, test, and debug Roblox games. Provides 39 tools for explorer control, script management, terrain generation, and autonomous testing.

Wraps Claude Code's code-review skill so any MCP client can run reviews over a PR, branch, working directory, or last commit, with five parallel reviewer subagents, low-to-max effort levels, and optional fix mode that edits files in place. Also supports follow-up questions on a previous review session and a free environment self-check.

AI-powered continuous code assistant for Cursor, VS Code, Antigravity, and Claude Code via MCP, providing tools for code review, testing, documentation, and quality analysis.

Enables AI assistants to perform comprehensive code reviews of local uncommitted changes by combining git diffs with static analysis from linters like ESLint and TypeScript. Returns structured JSON feedback with findings, suggestions, and quality assessments powered by Google's Gemini CLI.

Unified MCP server for Xcode + iOS Simulator — build, deploy, screenshot, and interact with your iOS app from Claude Code, Cursor, or any MCP client. Built in Swift. Single binary. No Node/Python runtime required.

Enables AI agents to create, edit, read, and run Bruno API collections (both .bru and .yml formats) with byte-parity to Bruno's own reader, without requiring the Bruno CLI.

Scaffolds new MCP servers for the OpenSIN-Code ecosystem with templates for Python, Node, Go; provides tools to add tools, test, validate, register, publish, and audit servers.

AI-powered code review tool that detects AI-generated code defects invisible to traditional linters — hallucinated packages, deprecated APIs, cross-file contradictions, hidden security anti-patterns, and over-engineering. Works as a standalone CLI, GitHub Action, or MCP server. Supports TypeScript, Python, Java, Go, and Kotlin. Free for individuals, no API key required.

Enables AI agents to research App Store screenshots and human-reviewed references, then produce evidence-backed messaging, narrative, art-direction briefs, and measurable copy/export QA.

Hybrid code quality analysis server combining fast logic-based checks with AI-powered deep analysis, offering tools for summaries, quick wins, and trend tracking.

Enables LLMs to access AST-based code structure maps, token-efficient skeletons, and analyze diff impact and blast radius for regression testing.

Enables auditing codebases for production readiness, including linting, testing, CI/CD, security, branch conventions, architecture, and an A–F quality scorecard via MCP tools.

Enables Codex CLI to perform code reviews on uncommitted changes via an MCP tool, keeping review context separate from the main session.

Lets AI coding agents build, test, and lint Android projects with Gradle, control devices and emulators via adb, automate UI through screenshots and input injection, and use a live IDE bridge for refactoring, code inspection, and run configurations. Works with any MCP client over stdio or authenticated HTTP.

Enables comprehensive GitHub PR reviews through Cursor's AI by fetching PR diffs, running static analysis tools (ESLint, Prettier, TypeScript, Semgrep), executing tests, and generating detailed code review reports with inline comments.

Records and visualizes evidence from a coding agent's own testing tools in a private local workspace, with live activity, domain charts, screenshot maps, findings, reproduction steps, human priorities, and persisted testing preferences. Runs 54 bundled CARBON command workflows over local stdio and serves a loopback-only visual workspace with strict evidence and permission gates.

Enables AI assistants to surgically extract and analyze code, reducing token usage and costs by up to 200x compared to reading entire files.

A code review MCP server built on FastMCP, supporting security audit, style check, complexity analysis, and bug pattern detection for Python code.

An MCP server for verifying AI-generated code quality, security, and performance, addressing trust gaps in AI coding assistants.

MCP server providing code review, auto-fix, test running, linting, formatting, and refactor suggestions for Claude Code.

Enables AI coding agents to verify code patches locally with sub-50ms neuro-symbolic analysis, detecting structural issues like dependency cycles and interface drift without consuming output tokens.

An MCP server for automated code review of GitHub pull requests. It checks security, quality, and license issues, and provides tools to list, inspect, and review PRs.

Enables coding agents to select bounded Python source context, run configured checks, and retrieve compact failure reports, diagnostic comparisons, and freshness evidence tied to the checked code.