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Darshan972

Scrapingdog MCP Server

by Darshan972

Amazon Search API

amazon_search

Scrape Amazon search-result listings for a query, with options for domain, country, page, language, and postal code. Retrieve paginated product data for market research or price tracking.

Instructions

Scrape Amazon search-result listings for a query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageYesPage number, starting at 1. (API default: 1)
queryYesAmazon search query.
domainYesAmazon TLD, e.g. com, in, de, co.uk. (API default: com)
countryYesTwo-letter ISO marketplace country code. (API default: us)
premiumNoUse premium proxies (raises success rate; extra credits). (API default: false)
languageNoResult language code (e.g. en, es, fr, de). (API default: en)
postal_codeNoPostal/ZIP code to localize results.
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'scrape' which suggests an unofficial/hard-scrape nature, but it does not disclose specifics like rate limits, proxy usage (despite the premium param), pagination behavior, or success rates. The description provides minimal transparency beyond the action itself.

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

Conciseness5/5

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

The description is a single sentence, front-loaded with the key action and resource. It is concise and free of filler, making it easy to parse quickly. It earns a perfect score for conciseness and structure.

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

Completeness1/5

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

With 7 parameters, no output schema, and no annotations, the description is far from complete. It lacks any information about return format, result structure, error handling, or behavioral nuances. For a scraping tool with low-level parameters, this is critically insufficient.

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

Parameters3/5

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

The input schema covers 100% of parameters with descriptions, so the baseline is 3. The description only mentions 'query' implicitly via 'for a query', adding no additional meaning beyond what the schema already provides. It does not explain how parameters like domain, country, or premium interact.

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?

The description clearly states the tool scrapes Amazon search-result listings for a query. The verb 'scrape' and resource 'Amazon search-result listings' are specific, and the phrase 'search-result listings' helps distinguish it from sibling tools like amazon_product, amazon_reviews, and amazon_offers.

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

Usage Guidelines2/5

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

The description gives no guidance on when to use this tool versus alternatives (e.g., google_search, amazon_product). It does not mention any context, prerequisites, or exclusions. The only implied usage is 'for a query', but there is no explicit comparative guidance.

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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