Side-by-side comparison of two Model Context Protocol servers — install paths, tools, quality signals, and directory engagement so you can pick the right one for Claude, Cursor, and other MCP clients.
Catch bad animations before they ship. 13 deterministic checks on the live page (duration, easing, stagger, exits, reduced-motion, touch-gated hover) with no API key or LLM, plus optional vision-LLM UX review across viewports. Install via NPM: npx -y motionlint mcp
Deterministic AI data-center design engine exposed as MCP tools (design, validate, layout): rack count, design PUE, total MVA, liquid/air cooling split, CDU planning, cost & timeline. NVIDIA Rubin-era, 22.9 kV intake, Korea. Remote Streamable HTTP at https://aidc-ai.io/api/mcp; no key for the anonymous tier.
Quality signal
41/100 (Fair)
45/100 (Fair)
Install path
npx · high
Remote · high
Engagement
0 0 0 0
2 0 0 3
Tools
Not listed yet
Design AI data centers with rack count, PUE, power, cooling, cost, and timeline outputsValidate designs for compliance with electrical, cooling, layout, and safety standardsGenerate rack-plan grids and site-block layouts with row/column positions in millimetersSupports NVIDIA Hopper, Blackwell, and Rubin-era GPUs with Korean site constraintsAccessible via Streamable HTTP MCP endpoint and REST API without authentication for anonymous tier
