# AMZScout Skill + MCP (remote · chatbot.amzscout.net)

Amazon research from AMZScout data: analyze products & niches, keywords/PPC, and brand catalogs.

- Trust score: 81/100 (high trust)
- Change this week: +4
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-12

## Components

- remote · `chatbot.amzscout.net`: 81/100 (this document), [markdown](https://verifymcp.io/servers/amzscout-corp-amzscout-skill-mcp/chatbot.md), [page](https://verifymcp.io/servers/amzscout-corp-amzscout-skill-mcp/chatbot)

## Channel facts

- Endpoint: `https://chatbot.amzscout.net/mcp`
- Transports: `streamable-http`
- Auth: `none`
- Version: `1.0.0`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-08-12.

- **Endpoint Security**: 91/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - Authorisation is enforced on tool calls, advertised via RFC 9728 protected-resource metadata. Discovery is public, which costs nothing: no tool can be invoked without a token.
  - HTTPS is enforced; there's no plaintext access path.
  - HSTS check failed: the Strict-Transport-Security header is absent.
  - DNSSEC is configured correctly; the domain's records validate against the full chain to the root.
  - The authorisation server offers only Dynamic Client Registration (RFC 7591), which MCP 2026-07-28 deprecated in favour of Client ID Metadata Documents.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 80/100
  - 100% of prompts and resources have a non-trivial description (not blank, and not just the item's name).
  - AI-judged instruction clarity (excellent).
  - Context-footprint check failed: tool/resource definitions use about 2207 tokens (~183/item across 12 items; 12 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 27/100
  - Stability observed for 8 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 99/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 97% of tool parameters carry a description.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### Claude

```bash
claude mcp add --transport http amzscout-corp-amzscout-skill-mcp https://chatbot.amzscout.net/mcp
```

### Codex

```toml
[mcp_servers.amzscout-corp-amzscout-skill-mcp]
url = "https://chatbot.amzscout.net/mcp"
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "amzscout-corp-amzscout-skill-mcp": {
      "type": "remote",
      "url": "https://chatbot.amzscout.net/mcp",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add amzscout-corp-amzscout-skill-mcp --url https://chatbot.amzscout.net/mcp --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  amzscout-corp-amzscout-skill-mcp:
    url: "https://chatbot.amzscout.net/mcp"
```

### Other

```json
{
  "mcpServers": {
    "amzscout-corp-amzscout-skill-mcp": {
      "type": "http",
      "url": "https://chatbot.amzscout.net/mcp"
    }
  }
}
```

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-08-11 (score 81, +1)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-08-10 (score 80, +1)

No change was recorded against any check on this day. Stability & Change Management went from 17 to 20. That category is still filling its 30-day observation window: 5 days of observed history at the previous scan, 6 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-08-08 (score 79, +1)

No change was recorded against any check on this day. Stability & Change Management went from 10 to 13. That category is still filling its 30-day observation window: 3 days of observed history at the previous scan, 4 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-08-06 (score 78, +1)

No change was recorded against any check on this day. Stability & Change Management went from 3 to 7. That category is still filling its 30-day observation window: 1 days of observed history at the previous scan, 2 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-08-05 (score 77, 0)

- [functional improvement] Stability: unverified → 0.03

### 2026-08-04 (score 77)

First indexed and scored.

## MCP tools (12)

### `amzscout_analyze_product` (~160 tokens)

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).

Input parameters:

- `asin` (string, required): Amazon Standard Identification Number
- `marketplace` (string): Amazon marketplace code. Default COM (United States).

### `amzscout_analyze_niche` (~199 tokens)

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).

Input parameters:

- `count` (integer): How many top products to pull from Amazon (5–100).
- `filters` (object): Filter products by price / sales / revenue / reviews / rating
- `keyword` (string, required): Niche, category, or product search keyword
- `marketplace` (string): Amazon marketplace code. Default COM (United States).

### `amzscout_compare_products` (~176 tokens)

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.

Input parameters:

- `asins` (array, required): 2–5 ASINs to compare. Each must be a real Amazon ASIN (B0XXXXXXXX). 0/O-swapped prefixes are auto-corrected.
- `marketplace` (string): Amazon marketplace code. Default COM (United States).

### `amzscout_compare_niches` (~190 tokens)

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.

Input parameters:

- `count` (integer): Products fetched per niche (default 10).
- `keywords` (array, required): 2–5 niches / category keywords to compare head-to-head, e.g. ["yoga mat", "resistance bands"].
- `marketplace` (string): Amazon marketplace code. Default COM (United States).

### `amzscout_analyze_product_set` (~172 tokens)

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.

Input parameters:

- `asins` (array, required): 2–100 ASINs to fetch as a set (B0XXXXXXXX). 0/O-swapped prefixes are auto-corrected.
- `marketplace` (string): Amazon marketplace code. Default COM (United States).

### `amzscout_search_products` (~190 tokens)

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.

Input parameters:

- `count` (integer): How many products to return (1–100)
- `filters` (object): Filter products by price / sales / revenue / reviews / rating
- `marketplace` (string): Amazon marketplace code. Default COM (United States).
- `query` (string, required): Search keyword / phrase.
- `sort` (string): Result sort order. revenue/sales/rating/reviews descending; price-low ascending; newest by first-listed date.

### `amzscout_find_by_brand` (~169 tokens)

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.

Input parameters:

- `brand` (string, required): Amazon brand name to search by
- `count` (integer): How many products to return (1–100)
- `filters` (object): Filter products by price / sales / revenue / reviews / rating
- `marketplace` (string): Amazon marketplace code. Default COM (United States).
- `sort` (string)

### `amzscout_get_keywords` (~174 tokens)

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.

Input parameters:

- `asin` (string): ASIN-scope: keywords this product ranks for.
- `keyword` (string): Keyword-scope: search data around this niche term.
- `marketplace` (string): Amazon marketplace code. Default COM (United States).

### `amzscout_search_knowledge` (~91 tokens)

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.

Input parameters:

- `query` (string, required): Search phrase. 2-300 chars.
- `topK` (integer): How many knowledge chunks to return (1–20)

### `amzscout_recommend_tool` (~99 tokens)

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.

Input parameters:

- `useCase` (string, required): What the user is trying to do (e.g. "find low-competition products", "validate a supplier", "track BSR").

### `amzscout_usage` (~78 tokens)

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` (~148 tokens)

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.

Input parameters:

- `history` (array): Optional prior turns for multi-turn context, oldest first.
- `message` (string, required): The question or request in natural language.

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/amzscout-corp-amzscout-skill-mcp/chatbot#diagnostics

## Score history

- 2026-08-12: 81
- 2026-08-11: 81
- 2026-08-10: 80
- 2026-08-09: 79
- 2026-08-08: 79
- 2026-08-07: 78
- 2026-08-06: 78
- 2026-08-05: 77
- 2026-08-04: 77

## Links

- Remote endpoint: https://chatbot.amzscout.net/mcp
- Repository: https://github.com/amzscout-corp/amzscout-skill-mcp
- Website: https://learn.amzscout.net/amazon-product-api-for-ai-agents
- Changelog RSS feed: https://verifymcp.io/servers/amzscout-corp-amzscout-skill-mcp/chatbot.xml
- Changelog JSON feed: https://verifymcp.io/servers/amzscout-corp-amzscout-skill-mcp/chatbot.json
- HTML version of this page: https://verifymcp.io/servers/amzscout-corp-amzscout-skill-mcp/chatbot