"Resources for Designing User Interfaces (UI)" matching MCP servers:

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

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.

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.

Enables AI coding agents to build, evaluate, and validate ServiceNow Next Experience UI Framework components and projects, helping keep component code and now-ui.json manifests in sync while using live instance API metadata.

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.

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

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.

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.

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.

Integrates Google's Gemini AI models into Claude Code and other MCP clients to provide second opinions, code comparisons, and token counting. It supports streaming responses and multi-turn conversations directly within your existing AI development workflow.

Provides AI agents with ground-truth knowledge of Strapi projects by reading real schema files, enabling accurate queries and safe refactoring operations.

Tells you how you and your Claude agent actually work together. Reads CLAUDE.md, hooks, skills, memory + scheduled tasks and writes you a letter. Local-only, no API keys, no data leaves your machine.

Enables MCP-compatible AI clients to inspect Godot project and editor state, observe opted-in running scenes, manage processes, and apply permission-scoped guarded edits. It starts read-only and exposes additional capabilities only through explicit command-line flags.

Uses Claude as an independent second-model reviewer inside Codex, performing reviews on disposable Git snapshots with structured findings.

Provides AI tools with direct access to project file bundles and structure through MCP resources and tools, enabling context-aware development workflows with real-time bundle management.

Enables Claude Code to leverage OpenAI models for expert code review, deep architecture analysis, and security audits, with automatic markdown report generation.

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.

Free deterministic security scan of a public git repository (GitHub, GitLab, Codeberg, Bitbucket): vulnerable dependencies via OSV.dev, secret patterns, and config lint, returned as structured JSON. Tools: scan_repository(url), audit_pricing(). Runs locally over stdio (python3 web/mcp_stdio.py or the Dockerfile) and is also hosted at https://project-feldspar.com/mcp. MIT licence, stdlib-only. Buil