shadcn-ui-mcp-server and ux-mcp-server

The tools are complements, as Jpisnice/shadcn-ui-mcp-server focuses on providing LLMs with context for Shadcn UI components, while elsahafy/ux-mcp-server offers a broader UX knowledge base and analysis tools, allowing an LLM to integrate UI implementation details with higher-level UX principles and analysis.

shadcn-ui-mcp-server
80
Verified
ux-mcp-server
44
Emerging
Maintenance 16/25
Adoption 19/25
Maturity 25/25
Community 20/25
Maintenance 10/25
Adoption 4/25
Maturity 20/25
Community 10/25
Stars: 2,731
Forks: 283
Downloads: 6,752
Commits (30d): 1
Language: TypeScript
License: MIT
Stars: 6
Forks: 1
Downloads:
Commits (30d): 0
Language: JavaScript
License: MIT
No risk flags
No risk flags

About shadcn-ui-mcp-server

Jpisnice/shadcn-ui-mcp-server

A mcp server to allow LLMS gain context about shadcn ui component structure,usage and installation,compaitable with react,svelte 5,vue & React Native

This project helps AI assistants quickly generate user interfaces using shadcn/ui components. It takes natural language requests from a developer, processes them, and outputs ready-to-use code for React, Svelte, Vue, or React Native. A software developer who uses AI tools to speed up their UI development workflow would find this valuable.

AI-assisted coding frontend development UI component generation React development Svelte development Vue development

About ux-mcp-server

elsahafy/ux-mcp-server

MCP server providing 28 UX knowledge resources, 23 analysis tools, and 4 workflow prompts. Complete UX ecosystem: WCAG, Nielsen heuristics, design systems, e-commerce, PWA, AI/ML, healthcare, finance & more.

Implements the Model Context Protocol (MCP) as a stdio-based server that integrates with Claude Desktop, Cursor, Continue.dev, and other MCP-compatible clients without requiring custom code. Organizes UX knowledge into 28 queryable resources (accessibility, design systems, framework patterns, emerging tech) with 23 analysis tools that perform tasks like WCAG audits, contrast validation, and dark pattern detection, plus 4 pre-configured audit workflows for comprehensive multi-dimensional UX evaluation.

Scores updated daily from GitHub, PyPI, and npm data. How scores work