apparelhub-mcp
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- AlicenseBqualityAmaintenanceEnables AI agents to operate a Store Builder site end-to-end — designing pages, filling them with data, reviewing screenshots, and publishing — without human intervention.374,737 npmMIT
- AlicenseNot gradedqualityBmaintenanceCreate, launch, and manage Meta + Google ads from Claude and ChatGPT. Analytics, strategy, and autopilot automation for any store.1Apache 2.0
TDQS
Scored across 124 tools
With 124 tools, several clusters overlap—order lifecycle (approve_order/confirm_order/submit_order_to_fulfillment), analytics (analyze_what_works vs analytics_summary), and design generation (generate_image vs design_apparel)—but the descriptions explicitly cross-reference and guide selection. An agent can usually find the right tool, though the sheer volume increases misselection risk.
All tools use snake_case and predominantly follow a verb_noun pattern (create_store, list_my_orders, sync_to_channel), with minor deviations for analytics/channel tools (analytics_summary, channel_performance) and a few noun-first names (api_request). This is mostly consistent and readable.
124 tools far exceeds the 3-15 range typical for well-scoped servers; even for a broad commerce platform, this is an extreme mismatch that burdens tool selection and context. Many tools could be consolidated or grouped.
The surface is remarkably comprehensive, covering CRUD for stores, products, designs, collections, orders, workspaces, members, plus analytics, listing optimization, and fulfillment issue handling. Minor gaps exist—no dedicated get_product, get_store, or get_design tools—but these are partly mitigated by list tools and the api_request escape hatch.