Keyapi Amazon Ecommerce

Data & APIs

Explore and analyze Amazon e-commerce data at scale — product search, category browsing, product details, best sellers, deals, seller intelligence, influencer storefronts, reviews, and ASIN/GTIN conversion across 24 Amazon marketplaces.

Install

openclaw skills install keyapi-amazon-ecommerce

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
KEYAPI_TOKEN	A valid API token from keyapi.ai. Register at the site to obtain your free token. Set it as an environment variable: `export KEYAPI_TOKEN=your_token_here`
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:

Server URL : https://mcp.keyapi.ai/amazon/mcp
Auth Header: Authorization: Bearer $KEYAPI_TOKEN

Setup (one-time):

# 1. Install dependencies
npm install

# 2. Set your API token (get one free at https://keyapi.ai/)
export KEYAPI_TOKEN=your_token_here

# 3. List all available tools to verify the connection
node scripts/run.js --platform amazon --list-tools

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	`product_search`	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: "asin": "B07ZPKBL9V,B09SM24S8C,B08N5WRWNW"

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, ke

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:

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:

node scripts/run.js --platform amazon --schema <tool_name>

# Examples
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

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:

# Single call with pretty output
node scripts/run.js --platform amazon --tool <tool_name> \
  --params '<json_args>' --pretty

# Force fresh data, skip cache
node scripts/run.js --platform amazon --tool <tool_name> \
  --params '<json_args>' --no-cache --pretty

Example — search products with filters:

node scripts/run.js --platform amazon --tool product_search \
  --params '{"query":"wireless earbuds","country":"us","sort_by":"REVIEWS","min_price":20,"max_price":100,"is_prime":true}' --pretty

Example — get details for multiple ASINs in one call:

node scripts/run.js --platform amazon --tool product_details \
  --params '{"asin":"B07ZPKBL9V,B09SM24S8C","country":"us"}' --pretty

Example — get best sellers:

node scripts/run.js --platform amazon --tool best_seller \
  --params '{"category":"electronics","type":"BEST_SELLERS","country":"us","page":1}' --pretty

Example — get active deals:

node scripts/run.js --platform amazon --tool deals \
  --params '{"country":"us","discount_range":"3","min_product_star_rating":"4"}' --pretty

Example — get product reviews filtered by star rating:

node scripts/run.js --platform amazon --tool product_reviews \
  --params '{"asin":"B00939I7EK","sort_by":"TOP_REVIEWS","star_rating":"5_STAR","verified_purchases_only":true,"page":1}' --pretty

Example — get seller profile and reviews:

node scripts/run.js --platform amazon --tool seller_profile \
  --params '{"seller_id":"A02211013Q5HP3OMSZC7W","country":"us"}' --pretty

node scripts/run.js --platform amazon --tool seller_reviews \
  --params '{"seller_id":"A02211013Q5HP3OMSZC7W","star_rating":"NEGATIVE","country":"us"}' --pretty

Example — get influencer posts then products:

# Step 1: get posts
node scripts/run.js --platform amazon --tool influencer_posts \
  --params '{"influencer_name":"tastemade","limit":20}' --pretty

# Step 2: get products from a specific list post
node scripts/run.js --platform amazon --tool influencer_post_products \
  --params '{"influencer_name":"madison.lecroy","post_id":"amzn1.ideas.382NVFBNK3GGQ"}' --pretty

Pagination reference:

Endpoint	Pagination method	Notes
`product_search`, `products_by_category`, `best_seller`, `deal_products`	`page` (int, 1-indexed)	Standard page-based pagination
`product_reviews`	`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
`RELEVANCE`	Default — most relevant results
`LOWEST_PRICE`	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
`ALL`	All conditions (default)
`NEW`	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
`NONE`	No filter (default)
`ALL_DISCOUNTS`	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, USED_ACCEPTABLE

Cache directory structure:

.keyapi-cache/
└── YYYY-MM-DD/
    ├── product_search/
    │   └── {params_hash}.json
    ├── products_by_category/
    │   └── {params_hash}.json
    ├── product_details/
    │   └── {params_hash}.json
    ├── product_category_list/
    │   └── {params_hash}.json
    ├── best_seller/
    │   └── {params_hash}.json
    ├── asin_to_gtin/
    │   └── {params_hash}.json
    ├── product_reviews/
    │   └── {params_hash}.json
    ├── product_review_details/
    │   └── {params_hash}.json
    ├── top_product_reviews/
    │   └── {params_hash}.json
    ├── product_offers/
    │   └── {params_hash}.json
    ├── deals/
    │   └── {params_hash}.json
    ├── deal_products/
    │   └── {params_hash}.json
    ├── promo_code_detail/
    │   └── {params_hash}.json
    ├── seller_profile/
    │   └── {params_hash}.json
    ├── seller_reviews/
    │   └── {params_hash}.json
    ├── seller_products/
    │   └── {params_hash}.json
    ├── influencer_profile/
    │   └── {params_hash}.json
    ├── influencer_posts/
    │   └── {params_hash}.json
    └── influencer_post_products/
        └── {params_hash}.json

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:

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:

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:

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:

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	`product_details` 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
`0`	Success	Continue workflow normally
`400`	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

Install

keyapi-amazon-ecommerce

author: KeyAPI license: MIT repository: https://github.com/EchoSell/keyapi-skills

Prerequisites

author: KeyAPI license: MIT repository: https://github.com/EchoSell/keyapi-skills

MCP Server Configuration

author: KeyAPI license: MIT repository: https://github.com/EchoSell/keyapi-skills

Analysis Scenarios

author: KeyAPI license: MIT repository: https://github.com/EchoSell/keyapi-skills

Workflow

Step 1 — Identify Analysis Targets and Select Nodes

Step 2 — Retrieve API Schema

Step 3 — Call APIs and Cache Results Locally

Step 4 — Synthesize and Report Findings

author: KeyAPI license: MIT repository: https://github.com/EchoSell/keyapi-skills

Common Rules

author: KeyAPI license: MIT repository: https://github.com/EchoSell/keyapi-skills

Error Handling

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