Awwwards-mcp: Free, open-source MCP server that gives AI coding agents design inspiration from award-winning websites

by INSANE0777 · MCP Server · ★ 278

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install Awwwards-mcp

claude mcp add awwwards -- npx -y awwwards-mcp
codex mcp add awwwards -- npx -y awwwards-mcp

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 70/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

ai-agentsawwwardsclaude-codecodexcoding-agentcursordesign-inspirationmcpmcp-serveropencode

Quick Facts

Stars278
Forks13
LanguageTypeScript
CategoryMCP Server
LicenseMIT
Quality Score70/100
Open Issues1
Last Updated2026-10-21
Created2026-09-18
Platformsbrowser, claude-code, codex, mcp, node
Est. Tokens~19k

About Awwwards-mcp

awwwards-mcp Free, open-source MCP server that gives AI agents design inspiration from Awwwards — the Mobbin-style visual reference loop, sourced from the web's best award-winning websites. Your agent searches in natural language ("dark 3D portfolio sites", "soft pastel e-commerce"), sees real screenshots inline, and can pull the design DNA of any site: color palette, tech stack, design elements,...

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

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