No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by birdseyevue · MCP Server · ★ 78
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is daisyui-mcp safe to install? View the security audit →
🌼 DaisyUI MCP Server A token-friendly local MCP server for DaisyUI component documentation Give your AI assistant the power to build beautiful UIs with DaisyUI 🚀 Features • Installation • Docker • Usage • Configuration ✨ Features 🎯 Token-Efficient — Only exposes relevant context via MCP tools, saving precious tokens 📚 60+ Components — Full coverage of DaisyUI's component library 🔄 Auto-Updatable — Fetch the latest docs anytime with one command ✏️ Customizable — Edit or add your own component docs to fit your project ⚡ Fast & Lightweight — Built with FastMCP for optimal performance 🛠️ MCP Tools This server exposes two tools that AI assistants can use: | Tool
| Stars | 78 |
| Forks | 17 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 67.7608746045833/100 |
| Last Updated | 2026-07-12 |
| Created | 2025-11-28 |
| Platforms | mcp, python |
| Est. Tokens | ~5k |
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daisyui-mcp is 🌼 A token-friendly local MCP server for DaisyUI component documentation using their public llms.txt.. It is categorized as a MCP Server with 78 GitHub stars.
daisyui-mcp is primarily written in Python. It covers topics such as ai, ai-agent-tools, ai-agents.
You can find installation instructions and usage details in the daisyui-mcp GitHub repository at github.com/birdseyevue/daisyui-mcp. The project has 78 stars and 17 forks, indicating an active community.
daisyui-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to daisyui-mcp on Agent Skills Hub include BaseLayer, mcp-gearbox, daymon. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: