"Material Design Icons" matching MCP servers:

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.

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.

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

Enables auto-configuration and validation of React components against a glassmorphic design token system using an AST correction engine. It supports managing UI presets, enforcing design uniformity in Monaco IDE, and exporting tokens as CSS, Tailwind, or JavaScript variables.

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

Enables the generation and iterative refinement of React UI components from natural language descriptions or design images using Vercel's v0 API. It provides tools for design-to-code workflows and chat-based component development within Claude, Cursor, and other MCP environments.

Enables Claude to consult GLM-4.6's architectural intelligence for system design, code analysis, scalability patterns, and technical decision-making. Provides specialized tools for enterprise architecture consultation, distributed systems design, and code review through the Model Context Protocol.

An MCP server that enables AI to interact with the private 'my-design' React component library and design tokens for UI generation and technical support. It provides tools for component searching, API documentation retrieval, and migration guidance based on specific internal design specifications.

A local MCP server that audits software projects against the UK ICO Age Appropriate Design Code (Children's Code) entirely on the user's machine.

Comprehensive MCP server for Fluent UI React v9 development, providing component knowledge, design tokens, code generation, and validation tools.

Enables AI to analyze, query, and manage a graph-based representation of software architecture for impact analysis, dependency tracking, and design.

Enables AI to directly read and analyze Lanhu design drafts and requirement documents to generate HTML, CSS, and structural analyses. It allows users to extract design slices and process prototype pages directly within AI clients.

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 intelligent design pattern recommendations using semantic search through a comprehensive catalog of 200+ patterns across 20 categories. Users can describe programming problems in natural language to discover appropriate design patterns with contextual recommendations.

Enforces design systems and vocabulary via ontology checks, candidate scanning, linting, and structural search across repositories.

A read-only MCP evidence server for chip verification and RTL debugging that exposes structured design and waveform data from Synopsys Verdi databases via MCP tools and CLI. It provides auditable evidence like contexts, objects, connectivity, traces, waveforms, and source mappings without modifying RTL or design DBs.

MCP server for Next2D application development that generates code following MVVM + Clean Architecture + Atomic Design patterns, validates architecture, and provides API references.

Enables AI coding tools like Cursor to access Figma design data through the Model Context Protocol, allowing accurate implementation of designs across frameworks. Based on NCDS (NHN Commerce Design System) with enhanced source generation capabilities.

Enables asking a consuming app in plain language to audit its design system usage, returning tables that sort every value and component by origin—design system, app-named, or none—along with token and contrast checks.

Exposes the 23 Gang of Four design patterns to AI coding agents for generation, canonical examples, AST-based detection, validation, and anti-pattern refactoring in Python codebases.