keyapi-amazon-ecommerce
Explore and analyze Amazon e-commerce data at scale — from product discovery and competitive pricing intelligence to seller profiling, review analysis, and influencer storefront research.
This skill provides comprehensive Amazon marketplace intelligence using the KeyAPI MCP service. It enables product search and discovery, category-level browsing, multi-ASIN detail retrieval, best seller and deal monitoring, seller profile and review analysis, influencer post and product research, and identifier conversion — all through a unified, cache-first workflow supporting 24 Amazon marketplaces.
Use this skill when you need to:
- - Search and discover Amazon products by keyword, category, brand, price range, or seller
- Retrieve detailed product information, availability, and variant data for up to 10 ASINs per call
- Monitor best sellers, new releases, trending products, and active deals with rich filter options
- Analyze seller profiles, seller reviews, and seller product catalogs
- Research Amazon influencer storefronts — posts, creative lists, and recommended products
- Retrieve and analyze customer reviews with star rating and verified purchase filters
- Compare product offers across conditions (new, used, refurbished) and delivery options
- Convert Amazon ASINs to Global Trade Item Numbers (GTINs)
author: KeyAPI
license: MIT
repository: https://github.com/EchoSell/keyapi-skills
Prerequisites
| Requirement | Details |
|---|
| KEYAPITOKEN | A valid API token from keyapi.ai. Register at the site to obtain your free token. Set it as an environment variable: INLINECODE0 |
| Node.js |
v18 or higher |
|
Dependencies | Run
npm install in the skill directory to install
@modelcontextprotocol/sdk |
author: KeyAPI
license: MIT
repository: https://github.com/EchoSell/keyapi-skills
MCP Server Configuration
All tool calls in this skill target the KeyAPI Amazon MCP server:
CODEBLOCK0
Setup (one-time):
CODEBLOCK1
author: KeyAPI
license: MIT
repository: https://github.com/EchoSell/keyapi-skills
Analysis Scenarios
| User Need | Node(s) | Best For |
|---|
| Keyword or ASIN-based product search | INLINECODE3 | Product discovery, competitive research |
| Browse products in a specific category |
products_by_category | Category-level analysis, assortment research |
| Detailed product info for 1–10 ASINs |
product_details | Product audit, price/spec comparison |
| Top-level category list for a marketplace |
product_category_list | Category hierarchy discovery |
| Best sellers, new releases, trending items |
best_seller | Market trend monitoring, top-performer benchmarking |
| Convert ASIN to GTIN/EAN/UPC |
asin_to_gtin | Cross-marketplace identifier mapping |
| Customer reviews for a product |
product_reviews | Sentiment analysis, quality signals |
| Full details for a specific review |
product_review_details | Deep review audit, reviewer profiling |
| Top-ranked helpful reviews |
top_product_reviews | Quick quality pulse, best review sampling |
| Available purchase offers (new/used/refurb) |
product_offers | Price comparison, buy box intelligence |
| Active deals with filters |
deals | Deal monitoring, promotional intelligence |
| Products in a specific deal |
deal_products | Deal content analysis |
| Promo code discount details |
promo_code_detail | Coupon validation, discount research |
| Seller profile and business info |
seller_profile | Seller credibility assessment |
| Seller customer reviews |
seller_reviews | Seller reputation analysis |
| Seller product catalog |
seller_products | Seller assortment research |
| Amazon influencer storefront profile |
influencer_profile | Influencer discovery, follower/bio data |
| Influencer posts (lists, photos, videos) |
influencer_posts | Content audit, product promotion patterns |
| Products in an influencer list post |
influencer_post_products | Product attribution, affiliate research |
author: KeyAPI
license: MIT
repository: https://github.com/EchoSell/keyapi-skills
Workflow
Step 1 — Identify Analysis Targets and Select Nodes
Clarify the research objective and map it to one or more nodes. Typical entry points:
- - Product discovery: Use
product_search with keyword + filters → deepen with product_details for specific ASINs. - Category analysis: Use
product_category_list to find category_id → products_by_category to browse listings. - Competitive pricing: Combine
product_details + product_offers to compare price and condition options. - Best seller research: Use
best_seller with category path and type parameter. - Deal monitoring: Use
deals with filters → deal_products for specific deal contents. - Seller intelligence: Use
seller_profile → seller_reviews + seller_products for full seller audit. - Review analysis: Use
product_reviews with star_rating and verified_purchases_only filters; top_product_reviews for quick sampling. - Influencer research: Use
influencer_profile → influencer_posts → influencer_post_products for product attribution.
Multi-ASIN Batch Calls
product_details and product_offers accept comma-separated ASIN lists in the asin parameter (up to 10 ASINs per call). This enables efficient bulk retrieval in a single request.
Example: INLINECODE47
country Parameter — Multi-Marketplace Support
Most endpoints accept a country parameter (default: us). Set it to target a specific Amazon marketplace:
us, uk, de, fr, it, es, ca, jp, au, in, mx, br, sg, ae, sa, tr, nl, pl, se, be, eg, za, ng, INLINECODE74
fields Projection
Most endpoints accept an optional fields parameter — a comma-separated list of fields to return. Use it to reduce response payload size and focus on only the data you need.
best_seller Category Path
The category parameter uses the URL path from Amazon's Best Sellers page. For example, software maps to https://www.amazon.com/Best-Sellers-Software/zgbs/software. The type parameter controls the list: BEST_SELLERS, NEW_RELEASES, MOVERS_AND_SHAKERS, MOST_WISHED_FOR, GIFT_IDEAS.
deals Pagination
deals uses offset (not page) for pagination. Increment by 30 for each page: 0, 30, 60, 90, etc.
Influencer Post Workflow
To retrieve products from an influencer post:
- 1. Call
influencer_posts with influencer_name to list posts — note the post_id values. - Call
influencer_post_products with influencer_name + post_id (only works for posts of type list).
Step 2 — Retrieve API Schema
Before calling any node, inspect its input schema to confirm required parameters and available options:
CODEBLOCK2
Step 3 — Call APIs and Cache Results Locally
Execute tool calls and persist responses to the local cache to avoid redundant API calls.
Calling a tool:
CODEBLOCK3
Example — search products with filters:
CODEBLOCK4
Example — get details for multiple ASINs in one call:
CODEBLOCK5
Example — get best sellers:
CODEBLOCK6
Example — get active deals:
CODEBLOCK7
Example — get product reviews filtered by star rating:
CODEBLOCK8
Example — get seller profile and reviews:
CODEBLOCK9
Example — get influencer posts then products:
CODEBLOCK10
Pagination reference:
| Endpoint | Pagination method | Notes |
|---|
INLINECODE102 , products_by_category, best_seller, INLINECODE105 | INLINECODE106 (int, 1-indexed) | Standard page-based pagination |
| INLINECODE107 |
page (int, 1-indexed) | Also supports
cookie for session continuity |
|
product_offers |
page (int, 1-indexed) | Default
limit is 100 offers |
|
seller_reviews,
seller_products |
page (int) | Optional, starts at 1 |
|
deals |
offset (int) | Multiples of 30: 0, 30, 60, 90… |
|
influencer_posts,
influencer_post_products |
cursor (string) | Pass cursor from previous response; omit for first call |
|
product_details,
top_product_reviews,
seller_profile,
promo_code_detail,
asin_to_gtin,
product_category_list | — | Single-call or no pagination |
product_search and products_by_category sort options:
| Value | Description |
|---|
| INLINECODE129 | Default — most relevant results |
| INLINECODE130 |
Cheapest first |
|
HIGHEST_PRICE | Most expensive first |
|
REVIEWS | Most reviewed |
|
NEWEST | Most recently listed |
|
BEST_SELLERS | Best-selling products first |
product_search and products_by_category condition options (product_condition):
| Value | Description |
|---|
| INLINECODE138 | All conditions (default) |
| INLINECODE139 |
New products only |
|
USED | Used products only |
|
RENEWED | Renewed/refurbished products |
|
COLLECTIBLE | Collectible items |
product_search and products_by_category deals filter (deals_and_discounts):
| Value | Description |
|---|
| INLINECODE146 | No filter (default) |
| INLINECODE147 |
Any discounted product |
|
TODAYS_DEALS | Today's deals only |
product_offers condition options (product_condition):
Pass as comma-separated values: NEW, USED_LIKE_NEW, USED_VERY_GOOD, USED_GOOD, INLINECODE155
Cache directory structure:
CODEBLOCK11
Cache-first policy:
Before every API call, check whether a cached result already exists for the given parameters. If a valid cache file exists, load from disk and skip the API call.
Step 4 — Synthesize and Report Findings
After collecting all API responses, produce a structured e-commerce intelligence report:
For product research:
- 1. Product Overview — Title, ASIN, brand, category, price range, availability, Prime status, rating, review count.
- Competitive Landscape — Price comparison across sellers and conditions, Buy Box holder, offer distribution.
- Review Intelligence — Star rating distribution, top positive and critical themes, verified purchase ratio.
- Market Positioning — Best seller rank, category placement, variant availability.
For seller research:
- 1. Seller Profile — Business name, overall rating, response rate, storefront description.
- Reputation Signals — Review distribution (positive/neutral/negative), recurring feedback themes.
- Product Assortment — Catalog size, category coverage, pricing patterns.
For deal and promotion monitoring:
- 1. Active Deals — Deal type distribution (Lightning, Prime Exclusive, Prime Early Access), discount depth, category distribution.
- Promo Intelligence — Applicable products, discount amounts, validity periods.
For influencer research:
- 1. Storefront Overview — Influencer name, follower count, bio, post count.
- Content Analysis — Post type breakdown (lists, photos, videos), keyword themes, scope categories.
- Product Attribution — Products promoted, category alignment, affiliate depth.
author: KeyAPI
license: MIT
repository: https://github.com/EchoSell/keyapi-skills
Common Rules
| Rule | Detail |
|---|
| Multi-ASIN calls | INLINECODE156 and product_offers accept up to 10 comma-separated ASINs in a single asin parameter. |
country parameter |
Defaults to
us. Pass a two-letter country code to target other Amazon marketplaces (24 supported). |
|
fields projection | Most endpoints accept
fields (comma-separated field names) to reduce response size. Use it to fetch only the data you need. |
|
best_seller category | Use the URL path from Amazon's Best Sellers page as the
category value (e.g.,
electronics,
software). |
|
deals pagination | Uses
offset (not
page). Increment by 30 for each page: 0, 30, 60, 90… |
|
Influencer post type |
influencer_post_products only works for posts with type
list. Check post type in
influencer_posts response first. |
|
product_offers condition |
product_condition accepts comma-separated values:
NEW,
USED_LIKE_NEW,
USED_VERY_GOOD,
USED_GOOD,
USED_ACCEPTABLE. |
|
Success check |
code = 0 → success. Any other value → failure. Always check the response code before processing data. |
|
Retry on 500 | If
code = 500, retry the identical request up to 3 times with a 2–3 second pause between attempts before reporting the error. |
|
Cache first | Always check the local
.keyapi-cache/ directory before issuing a live API call. |
author: KeyAPI
license: MIT
repository: https://github.com/EchoSell/keyapi-skills
Error Handling
| Code | Meaning | Action |
|---|
| INLINECODE183 | Success | Continue workflow normally |
| INLINECODE184 |
Bad request — invalid or missing parameters | Validate ASIN format, check batch size limit (max 10), verify
country code |
|
401 | Unauthorized — token missing or expired | Confirm
KEYAPI_TOKEN is set correctly; visit
keyapi.ai to renew |
|
403 | Forbidden — plan quota exceeded or feature restricted | Review plan limits at
keyapi.ai |
|
404 | Resource not found — product, seller, or influencer may not exist | Verify ASIN,
seller_id, or
influencer_name; product may have been delisted |
|
429 | Rate limit exceeded | Wait 60 seconds, then retry |
|
500 | Internal server error | Retry up to 3 times with a 2–3 second pause; if it persists, log the full request and response and skip this node |
| Other non-0 | Unexpected error | Log the full response body and surface the error message to the user |
keyapi-amazon-ecommerce
大规模探索和分析亚马逊电商数据——从产品发现、竞争定价情报,到卖家画像、评论分析以及网红店铺研究。
本技能通过KeyAPI MCP服务提供全面的亚马逊市场情报。它支持产品搜索与发现、品类级浏览、多ASIN详情检索、畅销品与促销监控、卖家画像与评论分析、网红帖子与产品研究,以及标识符转换——全部通过统一的、优先使用缓存的流程实现,支持24个亚马逊市场。
在以下场景使用本技能:
- - 通过关键词、品类、品牌、价格范围或卖家搜索和发现亚马逊产品
- 每次调用检索最多10个ASIN的详细产品信息、库存情况和变体数据
- 通过丰富的筛选选项监控畅销品、新品发布、热门产品和活跃促销
- 分析卖家画像、卖家评论和卖家产品目录
- 研究亚马逊网红店铺——帖子、创意列表和推荐产品
- 通过星级评分和已验证购买筛选器检索和分析客户评论
- 比较不同状况(全新、二手、翻新)和配送选项下的产品报价
- 将亚马逊ASIN转换为全球贸易项目代码(GTIN)
作者:KeyAPI
许可证:MIT
仓库:https://github.com/EchoSell/keyapi-skills
前提条件
| 要求 | 详情 |
|---|
| KEYAPITOKEN | 来自keyapi.ai的有效API令牌。在网站注册以获取免费令牌。将其设置为环境变量:export KEYAPITOKEN=yourtoken_here |
| Node.js |
v18或更高版本 |
|
依赖项 | 在技能目录中运行npm install以安装@modelcontextprotocol/sdk |
作者:KeyAPI
许可证:MIT
仓库:https://github.com/EchoSell/keyapi-skills
MCP服务器配置
本技能中的所有工具调用均指向KeyAPI亚马逊MCP服务器:
服务器URL : https://mcp.keyapi.ai/amazon/mcp
认证头: Authorization: Bearer $KEYAPI_TOKEN
设置(一次性):
bash
1. 安装依赖项
npm install
2. 设置您的API令牌(在https://keyapi.ai/免费获取)
export KEYAPI
TOKEN=yourtoken_here
3. 列出所有可用工具以验证连接
node scripts/run.js --platform amazon --list-tools
作者:KeyAPI
许可证:MIT
仓库:https://github.com/EchoSell/keyapi-skills
分析场景
| 用户需求 | 节点 | 最佳用途 |
|---|
| 基于关键词或ASIN的产品搜索 | productsearch | 产品发现、竞争研究 |
| 浏览特定品类中的产品 |
productsby_category | 品类级分析、品类研究 |
| 1-10个ASIN的详细产品信息 | product_details | 产品审计、价格/规格比较 |
| 市场顶级品类列表 | product
categorylist | 品类层级发现 |
| 畅销品、新品发布、热门商品 | best_seller | 市场趋势监控、顶级表现者基准测试 |
| 将ASIN转换为GTIN/EAN/UPC | asin
togtin | 跨市场标识符映射 |
| 产品的客户评论 | product_reviews | 情感分析、质量信号 |
| 特定评论的完整详情 | product
reviewdetails | 深度评论审计、评论者画像 |
| 排名最高的有用评论 | top
productreviews | 快速质量评估、最佳评论抽样 |
| 可用的购买报价(全新/二手/翻新) | product_offers | 价格比较、购物车情报 |
| 带筛选器的活跃促销 | deals | 促销监控、促销情报 |
| 特定促销中的产品 | deal_products | 促销内容分析 |
| 促销码折扣详情 | promo
codedetail | 优惠券验证、折扣研究 |
| 卖家画像和商业信息 | seller_profile | 卖家可信度评估 |
| 卖家客户评论 | seller_reviews | 卖家声誉分析 |
| 卖家产品目录 | seller_products | 卖家品类研究 |
| 亚马逊网红店铺画像 | influencer_profile | 网红发现、粉丝/简介数据 |
| 网红帖子(列表、照片、视频) | influencer_posts | 内容审计、产品推广模式 |
| 网红列表帖子中的产品 | influencer
postproducts | 产品归因、联盟研究 |
作者:KeyAPI
许可证:MIT
仓库:https://github.com/EchoSell/keyapi-skills
工作流程
步骤1 — 确定分析目标并选择节点
明确研究目标并将其映射到一个或多个节点。典型入口点:
- - 产品发现:使用带关键词+筛选器的productsearch → 使用productdetails深入特定ASIN。
- 品类分析:使用productcategorylist查找categoryid → 使用productsbycategory浏览列表。
- 竞争定价:结合productdetails + productoffers比较价格和状况选项。
- 畅销品研究:使用带category路径和type参数的bestseller。
- 促销监控:使用带筛选器的deals → 使用dealproducts获取特定促销内容。
- 卖家情报:使用sellerprofile → sellerreviews + sellerproducts进行全面卖家审计。
- 评论分析:使用带starrating和verifiedpurchasesonly筛选器的productreviews;使用topproductreviews进行快速抽样。
- 网红研究:使用influencerprofile → influencerposts → influencerpostproducts进行产品归因。
多ASIN批量调用
productdetails和productoffers在asin参数中接受逗号分隔的ASIN列表(每次调用最多10个ASIN)。这允许在单个请求中高效批量检索。
示例:asin: B07ZPKBL9V,B09SM24S8C,B08N5WRWNW
country参数 — 多市场支持
大多数端点接受country参数(默认值:us)。设置它以定位特定的亚马逊市场:
us, uk, de, fr, it, es, ca, jp, au, in, mx, br, sg, ae, sa, tr, nl, pl, se, be, eg, za, ng, ke
fields投影
大多数端点接受可选的fields参数——一个逗号分隔的字段列表。使用它可以减少响应负载大小,仅关注所需数据。
best_seller品类路径
category参数使用亚马逊畅销品页面的URL路径。例如,software映射到https://www.amazon.com/Best-Sellers-Software/zgbs/software。type参数控制列表:BESTSELLERS, NEWRELEASES, MOVERSANDSHAKERS, MOSTWISHEDFOR, GIFT_IDEAS。
deals分页
deals使用offset(而非page)进行分页。每页增加30:0, 30, 60, 90等。
网红帖子工作流程
从网红帖子中检索产品:
- 1. 使用influencername调用influencerposts列出帖子——记录postid值。
- 使用influencername + postid调用influencerpost_products(仅适用于类型为list的帖子)。
步骤2 — 检索API模式
在调用任何节点之前,检查其输入模式以确认必需参数和可用选项:
bash
node scripts/run.js --platform amazon --schema
示例
node scripts/run.js --platform amazon --schema product_search
node scripts/run.js --platform amazon --schema best_seller
node scripts/run.js --platform amazon --schema deals
步骤3 — 调用API并在本地缓存结果
执行工具调用并将响应持久化到本地缓存,以避免冗余的API调用。
调用工具:
bash
单次调用,美化输出
node scripts/run.js --platform amazon --tool
\
--params --pretty
强制获取新数据,跳过缓存
node scripts/run.js --platform amazon --tool \
--params --no-cache --pretty
示例 — 带筛选器的产品搜索:
bash
node scripts/run.js --platform amazon --tool product_search \
--