"A search for user interface (UI) design resources" matching MCP servers:

Enables AI assistants to discover, document, and generate React code using the pastel-themed Baby Design UI component library. It provides tools for retrieving design tokens and component specifications to streamline the development of user interfaces.

Lets developers and MCP agents run deterministic local HTML audits, turning inline HTML into structured findings for missing labels, unnamed buttons, skipped headings, ambiguous links, duplicate IDs, and missing landmarks. It also exposes a rule list and returns stable, actionable reports without external API keys.

This server automatically validates consistency between AI-generated frontend and backend code by enforcing OpenAPI 3.0 specifications. It injects smart constraints into AI prompts to ensure that generated data structures and API calls remain synchronized across the entire stack.

MCP server that lets AI agents search and inspect shadcn components from the encode-ui registry, returning metadata, source code, and install commands.

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.

This MCP server renders UI design artifacts headlessly, runs deterministic linters, and manages stateful design review loops with an independent vision critic.

Enables high-craft web design and engineering through OKLCH color systems, fluid typography, Three.js 3D scenes, frontend security audits, multi-viewport capture, and Python-backed design verification.

Provides software design guidance via MCP, including SOLID principles, design patterns, pattern scaffolding, and heuristic code-smell detection to help review and refactor code.

MCP server that gives Claude deep awareness of your frontend codebase by scanning components, hooks, services, routes, and data flow for React or Vue projects.

An MCP server for Arabic UI/UX design, offering tools to generate design tokens, build RTL components, stitch layouts, and audit RTL compliance using Material 3 Expressive and Google Stitch.

Provides access to Ant Design component library documentation and information for automated code generation. Enables searching components by category or keyword, retrieving component props, examples, API documentation, and available icons.

A mcp server to allow LLMS gain context about shadcn ui component structure,usage and installation

Enables AI-powered generation, customization, and documentation of its-just-ui React components. Provides tools for component generation, theme management, form creation, responsive layouts, and accessibility guidance.

Provides AI assistants with comprehensive access to Grafana's React component library, including TypeScript source code, MDX documentation, Storybook examples, test files, and design system tokens for building Grafana-compatible interfaces.

A Model Context Protocol server that helps programmers understand code by providing explanations, tech stack analysis, and best practice suggestions through prompt templates.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

Provides a real API for coding agents to look up design system components, props, and tokens, preventing guessed answers.

Enables semantic code search across indexed code folders using vector embeddings, with support for multiple embedding providers and automatic file watching. Provides an admin UI and integrates with MCP clients for natural language code queries.

Semantic + lexical code search as an MCP server. Agents query in natural language and get back ranked file:line ranges to read precisely.