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amazon_search_scraper

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Scrape Amazon search results with customizable domain, geolocation, category, and currency parameters. Get parsed data or raw output for efficient extraction.

Instructions

Scrape Amazon search results.

Supports content parsing, different user agent types, pagination, domain, geolocation, locale parameters and different output formats. Supports Amazon specific parameters such as category id, merchant id, currency.

Input Schema

TableJSON Schema

Name	Required	Description
`pages`	No	Number of pages to retrieve.
`parse`	No	Should result be parsed. If the result is not parsed, the output_format parameter is applied.
`query`	Yes	Keyword to search for.
`domain`	No	Domain localization for Google. Use country top level domains. For example: - 'co.uk' for United Kingdom - 'us' for United States - 'fr' for France
`locale`	No	Set 'Accept-Language' header value which changes your Google search page web interface language. Examples: - 'en-US' for English, United States - 'de-AT' for German, Austria - 'fr-FR' for French, France
`render`	No	Whether a headless browser should be used to render the page. For example: - 'html' when browser is required to render the page.
`currency`	No	Currency that will be used to display the prices.
`start_page`	No	Starting page number.
`category_id`	No	Search for items in a particular browse node (product category).
`merchant_id`	No	Search for items sold by a particular seller.
`geo_location`	No	The geographical location that the result should be adapted for. Use ISO-3166 country codes. Examples: - 'California, United States' - 'Mexico' - 'US' for United States - 'DE' for Germany - 'FR' for France
`output_format`	No	The format of the output. Works only when parse parameter is false. - links - Most efficient when the goal is navigation or finding specific URLs. Use this first when you need to locate a specific page within a website. - md - Best for extracting and reading visible content once you've found the right page. Use this to get structured content that's easy to read and process. - html - Should be used sparingly only when you need the raw HTML structure, JavaScript code, or styling information.
`user_agent_type`	No	Device type and browser that will be used to determine User-Agent header value.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.8.1May 6, 2026

TDQS

B3.4/5.0

Behavior2/5

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

The description only lists supported features (parsing, pagination, user agents, etc.) and does not disclose behavioral traits such as response format, rate limits, or edge cases. The readOnlyHint annotation already indicates a safe read operation, but the description adds little beyond what the schema and annotation provide.

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?

The description is concise, with a clear opening statement followed by a feature list in two sentences. While every sentence provides relevant information, the list format is somewhat generic and could be better structured by separating capabilities into categories.

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

Completeness3/5

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

Given the tool's complexity (13 parameters) and the presence of a full output schema, the description offers an adequate high-level overview. However, it omits practical context like when to use parse=false or how pagination behaves, relying on the detailed schema descriptions to cover specifics.

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?

Schema description coverage is 100%, so the baseline is 3. The description redundantly mentions parameter groups already documented in the schema (e.g., pagination, user agent types, currency) without adding nuanced meaning or context beyond what the schema descriptions offer.

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 begins with a specific verb and resource: 'Scrape Amazon search results.' This clearly distinguishes it from siblings like google_search_scraper and amazon_product_scraper, which target different resources. It further lists Amazon-specific parameters, reinforcing its focused purpose.

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

Usage Guidelines3/5

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

The description implies usage for Amazon search result scraping but provides no explicit guidance on when to choose this tool over alternatives. It does not mention exclusions or recommend siblings for related tasks, leaving the decision to inference from the tool name and capability list.

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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