mcp server
AMZScout Skill + MCP
Amazon research from AMZScout data: analyze products & niches, keywords/PPC, and brand catalogs.
Description as published by the maintainer. Source
- version 1.0.0
- active
- retrieval
active — Most recent push to the repository was 2026-08-06. Dashed tags are derived by ZBS Index from the published description, not stated by the maintainer.
What this server can do
12 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.
amzscout_analyze_niche(count, filters, keyword, marketplace)- 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). Required: keyword.
amzscout_analyze_product(asin, marketplace)- 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). Required: asin.
amzscout_analyze_product_set(asins, marketplace)- 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. Required: asins.
amzscout_compare_niches(count, keywords, marketplace)- 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. Required: keywords.
amzscout_compare_products(asins, marketplace)- 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. Required: asins.
amzscout_find_by_brand(sort, brand, count, filters, marketplace)- 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. Required: brand.
amzscout_get_keywords(asin, keyword, marketplace)- 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.
amzscout_recommend_tool(useCase)- 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. Required: useCase.
amzscout_search_knowledge(topK, query)- 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. Required: query.
amzscout_search_products(sort, count, query, filters, marketplace)- 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. Required: query.
amzscout_usage- 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-agent(history, message)- 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. Required: message.
Last successful function declaration observed on . Source: https://chatbot.amzscout.net/mcp. We list what the server declared; we do not call any of these functions.
Endpoint status observed on . Source: https://chatbot.amzscout.net/mcp.
Signals
These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.
| Signal | Value | What it measures | Window | Observed | Source |
|---|---|---|---|---|---|
| GitHub stars | 0 | Number of GitHub accounts that bookmarked this repository since it was created. It is a bookmark count, not installs, not active users and not quality. | cumulative, all time | GitHub | |
| Last commit | 2026-08-06 | Date of the most recent push to any branch. This is the strongest cheap indicator of whether the project is still maintained. | point in time | GitHub | |
| Open issues | 0 | Open issues plus open pull requests, as GitHub counts them together. A high number can mean an active project or an abandoned one. | as of fetch | GitHub | |
| Latest published version | 1.0.0 | Latest version string the maintainer published to the registry. | as of fetch | Model Context Protocol | |
| Registry record last updated | 2026-08-04 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| License | MIT | Licence GitHub detected in the repository. Detection can be wrong; the LICENSE file is authoritative. | as of fetch | GitHub | |
| First listed in the MCP Registry | 2026-08-04 | Date this server was first published to the official MCP Registry. Not a usage or quality measure. | point in time | Model Context Protocol | |
| repository status | active | The repository exists on GitHub and is not archived. This says nothing about how recently it was worked on. | as of fetch | GitHub | |
| mcp tools declared | 12 tools | Number of functions the server itself declared when asked to list them. This is what the server offers an agent, not a measure of how well any of them work. | as of probe | chatbot.amzscout.net | |
| mcp endpoint status | ok | The server listed 12 functions when asked. | as of probe | chatbot.amzscout.net |
Where to get it
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These share tags the maintainers applied themselves, such as amazon, amazon-seller, ecommerce, market-research. Common tags like "mcp" or "ai" are ignored for this: agreeing with six hundred other projects is not a similarity.
This is not a recommendation and not a test result. It is a map of what the authors said their work is about.
How the author describes it
Topics the maintainer set on GitHub: ai-agent, amazon, amazon-fba, amazon-ppc, amazon-product-research, amazon-research, amazon-seller, chatgpt, claude, competitive-analysis, crewai, cursor, ecommerce, keyword-research, market-research, mcp, mcp-server, model-context-protocol, openclaw, product-research.
This record as data
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