Real Time Amazon Data MCP Server
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- AlicenseAqualityBmaintenanceRemote MCP server with 19 e-commerce and IP-compliance data tools — Amazon product/review/search/niche/bestseller data, AI SERP & keyword trends, local Maps POI, WIPO trademark search, and PACER patent litigation. No scraping code or proxies needed; one API key unlocks all tools.211MIT
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Easyparserofficial
AlicenseAqualityBmaintenanceReal-time, structured Amazon data for AI agents across 21 marketplaces: product details, seller offers, search results, 12-month sales history, Best Sellers Rank, package dimensions, and seller intelligence. 16 tools including free bulk-job monitoring and account usage tracking, available as a hosted endpoint or via npx.1759 npmMIT- AlicenseAqualityDmaintenanceFetch live Amazon product data using Amazon Scraper API MCP server447 npm2MIT
- AlicenseAqualityAmaintenanceEnables MCP clients to search Amazon products, retrieve product details by ASIN, look up seller profiles, and browse seller catalogues as structured JSON across 23 regional Amazon domains.5118 npm38 PyPI14MIT
TDQS
Scored across 18 tools
Most tools target distinct entities (products, sellers, influencers, deals, promo codes), but there is notable overlap risk: products_by_category and product_search both return products with offers and support the same filters; product_details and product_offers return largely the same product data (with product_offers just adding an offers array); and product_review_details vs product_reviews could be confused (one is a single review by id, the other paginates reviews, though they distinctively differ). These overlaps create selection ambiguity for agents.
The naming pattern is predominantly consistent noun_by_noun / noun_noun (e.g., product_details, product_search, product_reviews, seller_profile, seller_products, influencer_posts, deals). Most tools follow a predictable noun_[verb/noun] style. Minor deviations like asin_to_gtin (verb-based) and product_category_list break the pattern slightly, but overall the convention is coherent.
18 tools is reasonable for a broad Amazon data provider covering products, reviews, sellers, influencers, deals, and promotions. While on the higher end, each tool maps to a distinct Amazon scraping surface area, and the count is justified by the breadth of the domain.
The surface covers a wide variety of Amazon data: product search/details/offers, reviews, sellers, influencers, deals, promo codes, categories, and ASIN conversion. However, there are notable gaps: no update/create/delete operations are expected for a read-only API, but missing tools like cart/order operations and wishlist are acceptable. Gaps like product attributes/browse nodes or warehouse/deep-link retrieval are absent, and it lacks a tool to fetch a single product's reviews for a specific product without cookies (product_reviews requires a logged-in cookie). The truncated descriptions (two tools cut off) make full completeness assessment difficult.