Enables AI shopping agents to search products, check stock, apply promotions, manage cart sessions, and create cryptographically signed checkout sessions on e-commerce storefronts, while giving merchants analytics into agent intent and catalog demand gaps.
Serves a product store's catalog over MCP tool calls so agents can search products, add items to a cart, inspect the cart, and complete checkout without parsing full HTML pages. This keeps action payloads and token usage far smaller than DOM scraping, especially as catalogs grow, and also works through Chrome's native document.modelContext API.
Turns an online store's catalog into MCP tools so agents can search products, fetch details, look up barcodes, analogs, categories, promotions and pickup points, and hand off a cart as a payment link rather than checking out. It is driven by a single TOML config with capability-based tool sets, bilingual model-facing texts, public catalog access plus OAuth 2.1-scoped personal tools like order history, and ready adapters for YML/Google Merchant feeds and WooCommerce across ten store engine presets.
Enables LLM agents to handle e-commerce customer support by searching a product catalog and FAQ knowledge base with hybrid retrieval, plus managing orders, carts, returns, shipment tracking, and human escalation through support tickets.