"Resources for UI and Frontend Development" matching MCP servers:

LLM routing proxy that cuts API costs 60-90% by auto-selecting the cheapest capable model across OpenAI, Anthropic, and Google. Provides stats, config, and model comparison tools via MCP.

Provides tools for frontend testing including code analysis, test generation, test execution, and React component testing for Jest and Cypress frameworks.

Enables AI agents to analyze and scaffold React/Next.js applications enforcing Feature-Sliced Design (FSD) conventions, providing tools for project analysis, rule retrieval, and entity generation.

Transforms design inputs (URLs, screenshots, descriptions) into production-ready React components following G2's Elevate 2.0 design system, providing both an MCP server for Claude Code and a web interface.

Enables AI assistants to discover, retrieve, and compare official international development indicators from sources such as the World Bank, FAOSTAT, WHO, UNICEF, and IMF, while preserving source identifiers, units, and citations.

A Model Context Protocol server that enables seamless integration between Claude AI and development tools like VSCode, Augment, Vercel, Airtable, and Square.

Provides professional UI/UX design expertise and frontend development tools for analyzing interfaces, generating design systems, and creating modern components with accessibility and best practices built-in. Supports React, Vue, Angular and other frameworks with seamless Claude Code CLI integration.

A Model Context Protocol server that provides access to Magic UI components, allowing AI assistants and other MCP clients to discover and use UI components from the Magic UI design system.

Native Claude Code integration for @annondeveloper/ui-kit — a zero-dependency React component library with 147 components, 3 weight tiers, physics-based animations, and OKLCH color system. Gives Claude deep awareness of the library's components, design patterns, and conventions. Includes 5 skills for component discovery, code generation, design system reference, tier selection, and accessibility

Enables AI coding agents to inspect a project's design tokens and component catalog, audit React/Tailwind files against seven static UI quality rules, score overall UI health, and apply auto-fixes that are re-audited and reverted on regression. It works both locally over filesystem access and remotely on raw TSX/JSX or CSS snippets passed directly as strings.

Provides comprehensive UI design assistance tools for LLMs, generating color palettes, typography, spacing systems, shadows, design tokens, and more via the MCP protocol.

Enables AI agents to rapidly drive and inspect real web pages through persistent browser sessions, using accessibility-tree snapshots and DevTools-grade diagnostics to identify and diagnose issues.

Provides AI assistants with comprehensive access to Gluestack UI components, source code, and examples for React Native development.

Provides AI assistants with comprehensive access to shadcn/ui v4 components, blocks, demos, and metadata across React, Svelte, Vue, and React Native, enabling efficient component integration and code generation.

Helps AI assistants access shadcn/ui component documentation and examples through a TypeScript-based MCP server that provides reference information for component details, usage examples, and search capabilities.

Provides reference information for shadcn/ui components through a Model Context Protocol server that helps AI assistants access component documentation and examples.

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

Provides AI-powered tools to apply UX/UI best practices, Nielsen's heuristics, cognitive biases, and Material-UI patterns to React components. Enables automated application of responsive design, Apple design patterns, and complete UX guidelines through natural language commands.

A Model Control Protocol server that allows users to discover, install, and manage Shadcn UI components and blocks through natural language interactions in compatible AI tools.

Facilitates spec-driven development workflows by providing structured prompts for generating requirements in EARS format, design documents, and implementation code following a systematic approach.