AMZScout Skill + MCP

Market snapshot for an Amazon niche/keyword — top products by revenue plus computed aggregates (price/sales/revenue/review distributions, revenue concentration, brand spread). Pure data fetch (no AI analysis) — reason over the returned data yourself. How to use: judge niche attractiveness — demand concentration (revenueTop5SharePercent: high = winner-takes-all, low = fragmented/open), price bands and where the money sits, review counts as entry moats, brand dominance vs no-name spread, and standout products (high sales + weak rating/reviews = displacement opportunity).

Full raw data for a single Amazon product by ASIN — price, estimated sales/revenue, reviews, rating, listing quality, sellers, plus sales/price/revenue history when available. Pure data fetch (no AI analysis) — reason over the returned data yourself. How to use: audit the product like a sourcing analyst — demand trend & seasonality from sales history, pricing direction & margin risk from price history and FBA fees, competition from sellers/reviews, listing quality from LQS, then conclude whether a new seller should enter (GO / NO-GO and what it would take).

Raw data across an explicit set of 2–100 ASINs — product rows plus computed aggregates (price/sales/revenue/review distributions, revenue concentration, brand spread). Pure data fetch (no AI analysis) — reason over the returned data yourself. To discover products from a keyword instead, analyzeNiche is the equivalent. How to use: treat the set as a mini-market — segment products into groups, spot where demand concentrates, flag outliers (price, sales, review anomalies), and summarize group-level signals.

Raw head-to-head data for 2–5 Amazon niches / category keywords — per-niche product sets plus computed aggregates (price/sales/revenue distributions, revenue concentration, brand spread). Pure data fetch (no AI analysis) — do the comparison yourself. For ASINs, compareProducts is the equivalent. How to use: weigh demand (total est. revenue/sales) against competition (review levels, brand concentration) and price levels per niche, then give a verdict on which niche is the better opportunity for a new seller and under what conditions.

Side-by-side raw data for 2–5 Amazon products by ASIN — price, sales/revenue estimates, reviews, listing quality, plus history when available. Pure data fetch (no AI analysis) — do the comparison yourself. For a single ASIN, analyzeProduct is the equivalent. How to use: compare demand (est. sales), revenue, review moat and rating, price positioning, listing quality, and history trends (growing vs declining), then give a verdict on which product is the stronger opportunity and why.

List products under a specific Amazon brand. Pre-validates the brand name via cached AI check, then filters keyword-search results to rows whose `brand` field actually matches. On no-match, returns the brands that did appear in the keyword pool so callers can suggest alternatives. How to use: assess the brand's Amazon footprint — lineup breadth, price range, which products carry the revenue, and how strong its review moat is.

Amazon keyword / SEO / PPC data for either a single product (ASIN-scope — terms the product ranks for) or a niche/category (keyword-scope — search data around the term). Returns keyword rows with search volume, CPC, and competition where available. Pure data fetch (no AI analysis). How to use: pick high-volume / low-competition terms for SEO and PPC targeting, use CPC as ad-cost pressure, sum search volumes to gauge niche demand, and for ASIN-scope check organic vs sponsored ranks to spot listing-optimization gaps.

Given a user use-case, returns the AMZScout tools & Sellerhook services catalog (with tracking links) so you can recommend the right AMZScout product/feature. Use for "which AMZScout tool should I use for X" questions.

TF-IDF search across the AMZScout knowledge base (Amazon-seller tutorials, brand reference, glossary). Returns the top-K relevant chunks with title, source URL and text. Use this to ground answers in factual material.

Keyword search against Amazon — returns the top N products with price, sales, revenue, reviews, rating. Pure data fetch (no AI analysis). Best when you need raw product rows (specific sort order or filters); analyzeNiche additionally returns computed market aggregates on top of the rows. How to use: scan the rows for demand leaders, price clusters, and low-review listings that still sell — those are the entry-opportunity signals.

The caller's AMZScout AI-agents token balance — remaining, used, and limit. Free — no tokens are charged for this call. How to use: answer "how many tokens do I have left", "what's my usage / balance / limit", or when a call fails on quota. Report the remaining figure first.

AMZScout all-in-one Amazon research assistant. Ask anything in natural language ("Is B07GQF9D1Z worth selling?", "Analyze the yoga mat niche", "Find products for brand Anker") and it returns a finished analysis — it pulls live Amazon data and runs the right analyses internally, so no sub-tool selection is needed. Best for a hands-off answer; the granular amzscout_* tools are the alternative when step-by-step orchestration is preferred. Returns a complete, user-ready report.