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Pangolinfo Amazon Data MCP

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TypeScript
Remote

search_amazon

Run a real Amazon keyword search and get the first-page ASIN list to find top results, sellers, or competitors for a keyword.

Instructions

[Amazon SERP scrape] Run a real Amazon keyword search and return the first-page ASIN list. Use when: user says "search Amazon for X" / "who sells X" / "top results for keyword X" / "competitors for X"; or you need a list of ASINs for a keyword as upstream input to deeper analysis. Don't use: for a single ASIN detail (use get_amazon_product); for category bestseller ranks (use list_bestsellers); for Google/external demand on the term (use ai_search or keyword_trends). Returns (format='json', default): data.json[0].data.{ pageIndex, nextPage, keyword, results[{ asin, title, price, star, rating, sales, badge, rank, sponsored, image, delivery }] } — ~22 rows/page. Pagination: use the 'page' param (default 1, 1-based); response's 'nextPage' holds the next page number, 'nextPage=null' means last page reached. Pair with: ↓ feed results[].asin into get_amazon_product / get_amazon_reviews for single-product deep-dive; ↓ feed the same keyword into keyword_trends to compare in-site vs external demand. Cost: ~1 point/page, ~5s. Only paginate when the user explicitly asks for more / Top-N (N>22) / all results — otherwise the first page is enough.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number, 1-based. ~22 ASINs per page. Use response's pageIndex/nextPage to decide whether to continue: nextPage holds the next page number; nextPage=null (or absent) means last page reached. **Only paginate when the user explicitly asks for more / Top-N where N exceeds one page / all results** — otherwise the first page is enough.
siteNoAmazon marketplace. Defaults to 'amz_us' (US).amz_us
formatNoResponse format. Defaults to 'json' — structured search rows (asin, title, price, star, rating, sales, badge, rank, ...) ready for programmatic use. Use 'markdown' if you want the rendered SERP text instead.json
keywordYesSearch keyword. Examples: 'wireless earbuds' / 'stanley quencher' / 'iphone 16 case' / 'kitchen knife set'.
zipcodeNoZIP code that must match the site country (amz_us → US zip, amz_jp → JP zip, ...). Optional; backend picks a random one from the per-country pool when omitted. Cross-country zips (e.g. amz_us + JP zip) are rejected by the backend. Examples: 10001 (NY) / 90001 (LA) / 100-0001 (Tokyo).
clientSourceNo调用来源标记。仅由 Pangolinfo Skill 传 skill；普通 MCP 调用省略即可。

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description carries the full burden and does so well: it discloses cost (~1 point/page, ~5s), page size (~22 rows), pagination semantics via nextPage, and the operational discipline 'only paginate when the user explicitly asks'. This is the behavioral context an agent needs that no structured field provides.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well front-loaded with labeled sections (Use when / Don't use / Returns / Pair with / Cost) that make scanning easy. Slightly verbose — pagination guidance is repeated in both the body and the schema's 'page' description — but every section earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description fully specifies the return structure (data.json[0].data.{...results[]}), page size, cost, pagination, and downstream pairing. Nothing an agent needs to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3, but the description adds meaning beyond the schema: it explains the format='json' default and its row shape, and reinforces pagination intent with nextPage semantics. It does not contradict the schema's own parameter docs, though there is some overlap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Run a real Amazon keyword search and return the first-page ASIN list') and explicitly names the sibling tools it is not (get_amazon_product, list_bestsellers, ai_search, keyword_trends). An agent can distinguish it from every related tool without opening a schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit 'Use when' triggers (user says 'search Amazon for X', need ASINs as upstream input) and 'Don't use' exclusions that route to the correct alternatives per scenario. When-to-use, when-not, and alternatives are all covered.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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