"Material Design Icons" matching MCP servers:

Compare design and implementation screenshots using pixel-by-pixel analysis, generating visual diff images and metrics.

Enables AI coding agents to audit rendered web pages for design and UI defects by measuring geometry, styles, contrast and motion in a local browser and judging them against evidence-tiered rules. Findings come back as short, fixable items expressed in the project's own design tokens, while only sanitized numeric measurements leave the machine.

Enables AI agents to validate design system constraints, including WCAG accessibility, token relationships, and consistency, through MCP tools like validate, explain, and suggest-fix.

Enables AI agents to generate Korea Government Design System components and page templates with KWCAG 2.2 accessibility requirements built in, then validate rendered pages and block task completion until remaining accessibility violations are corrected.

Enables agents to measure a live page or Figma frame against a reference design, reporting exact numerical deviations in layout, spacing, typography, colors, states, and animations before any baseline exists.

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 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 AI agents to score live URLs against a deterministic design contract, validate DTCG tokens and Lottie animations, audit accessibility, and retrieve design-system contracts, catalogs, and review rubrics.

Enables AI assistants to summarize Playwright test runs, identify flaky tests, heuristically triage failures, generate Playwright test stubs, and retrieve recent GitHub CI status through MCP tool calls.

Grades MCP servers on agent usability (A–F) — description quality, schema design, tool naming and token cost, the properties that decide whether a model picks the right tool. Benchmarked across 36 popular servers; a third scored D or F.

The pixel-diff verifier for Figma-to-code: converts a frame to HTML, React, Next.js or Tailwind with design tokens, then renders it and pixel-diffs it against Figma until it converges. Also verifies your own running app.

Provides AI assistants with visual feedback for responsive web design by capturing screenshots and extracting DOM layout data through the Viewpo macOS app. It enables automated UI verification and responsive behavior comparison across multiple viewport breakpoints.

Local MCP server that compares a mobile app screen (Android or iOS Simulator) with its Figma design

Enables AI agents to conduct UX reviews of web applications, detecting and fixing AI-generated design and usability issues while preserving the original style.

Enables MCP clients to open websites, capture screenshots at multiple breakpoints, and run automated design audits for issues like overflow, small tap targets, low contrast, missing alt text, tiny text, and clipped content.

An MCP server that audits websites for accessibility (WCAG 2.1 AA/EAA), performance, SEO, design quality, and mobile responsiveness, providing actionable scores, grades, and prioritized fixes.

Compares Figma frames to live pages, checking colors, fonts, and border radii, and generates a shareable HTML report.

An MCP server for in-loop design review of web previews. It enables agents to submit a preview URL, receive structured findings with suggested fixes, and recheck after applying changes, while never editing code itself.

An MCP server that gives coding agents a design brief and a pass or fail UI check. Published on PyPI and the official MCP Registry.

This server provides coding agents with domain-specific design guidance, curated palettes, and visual blueprints. It also allows agents to audit web interfaces against a comprehensive set of design rules.

Voice-powered bug reporting MCP server with 13 tools. Captures console logs, network errors, DOM traces, and developer voice descriptions. AI reads structured reports to find and fix bugs automatically. Chrome Extension + Python CLI.

Enables AI agents to read the user's real logged-in Chrome session, returning stability-ranked and uniqueness-verified selectors, annotated screenshots, page objects, design tokens, accessibility checks, and QA diagnostics. Actions that change the page are proposed to the user for in-page approval before anything happens.

Gives AI coding agents a ten-phase frontend design pipeline (audit, style direction, build, review, verify, perf, brand) with an exclusive style menu. Its verify_page tool loads a URL in a real Playwright browser at mobile and desktop viewports and returns screenshots, ARIA snapshots, console errors, failed requests, horizontal-overflow offenders, axe-core violations, and pointer type.

This is an unofficial, community-built MCP server for qTest Manager. Not affiliated with or endorsed by Tricentis. Browse projects, modules, and test cases Create/delete test design modules Create and manage test execution cycles Add test cases into execution suites

Orchestrates full-stack frontend engineering—from intent discovery and design genome generation to automated multi-viewport QA—through JSON-RPC tools for AI agents.

Lets Claude design, run and compare local robotics experiments — swarm behaviours, formations, coverage, cooperative localisation and synthetic visual odometry — through tools for cataloguing worlds, launching runs and campaigns, tuning parameters, saving floor plans and diffing metrics. Everything executes on the user's machine via a stdio JSON-RPC process, so runs are reproducible and no data leaves the host.

Post-generation quality gate MCP server that audits and fixes AI-generated code for UI/UX design compliance and backend architecture robustness via tools like critique_ui, critique_backend, and generate_fix.

Comprehensive MCP server for end-to-end UI development, offering tools to generate components, manage design tokens, audit accessibility, autofix issues, inspect live pages, compare screenshots, and more across multiple frameworks.

An MCP server implementing a 7-stage agentic frontend workflow—from design audit to PR review—including AI-driven component generation, browser validation, E2E testing, and CI self-healing.

This server performs a comprehensive analysis of a website by fetching its HTML and scoring it across five categories: performance, SEO, design, functionality, and technology. It enables users to identify technical strengths and weaknesses in a webpage's structure and health.

Enables AI assistants to perform automated web testing by controlling a Chrome browser to navigate, interact with pages, capture screenshots, extract console logs, and simulate mobile devices for responsive design testing.

Enables agents to probe URLs and classify bot-wall responses with vendor and signal evidence, perform offline classification, scan for exposed key-like material, and run owner-side access audits through stdio tools.

Enables AI assistants to read and write Figma designs, manage design tokens and variables, create components and FigJam boards, and run accessibility and design-code parity checks through a bidirectional Figma integration.

Enables desktop FDM 3D-printer users to deterministically estimate filament, time, energy, and cost; assess cantilever and container viability; compare failure costs; plan unattended print start times; and decide whether off-design or repaired parts should be kept, tested, or reprinted.

Enables headless Blender asset QA and FBX reimport verification for game and 3D asset pipelines, checking mesh count, material count, and naming prefixes with deterministic JSON results.

An MCP server that provides comprehensive UX best practices covering accessibility, usability, UI patterns, design systems, performance, and more, enabling clients to analyze and generate UX-optimized code and recommendations.

Audits local datasets and ML training code for target, temporal, and entity leakage, PII exposure, and invalid evaluation design, returning structured evidence and pass/review/block verdicts before model training.

Enables AI coding agents to absorb UI references into perceptual design tokens, semantic responsive layout skeletons, motion physics parameters, and zero-dependency multi-framework components, while auditing WCAG contrast, touch targets, reduced-motion guardrails, and layout stability.

Enables AI agents to build and audit web UIs by serving a fixed workflow and rule-priority guide, running deterministic static linting of source, rendering pages headlessly at multiple widths for screenshots and accessibility snapshots, and gathering element-level design feedback. All output is written only to a local log folder, leaving project files untouched and external network access blocked by default.

Enables comparison of mobile app screenshots against design mockups, with Calorix as the first integration target.