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E-commerce Sales Signals MCP

E-commerce Sales Signals MCP

by GeminiGeorge22
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Hybrid

Amazon Reviews Scraper

junglee--amazon-reviews-scraper
Read-only

Scrape Amazon product reviews—ratings, text, dates, reactions, and images—for customer feedback analysis and export as structured data.

Instructions

Amazon Reviews Scraper. Amazon scraper to extract reviews from Amazon products. Scrape and download detailed reviews without using the Amazon API, including rating score, review description, reactions and images. Download your data as HTML table, JSON, CSV, Excel, XML.

When to use: Use when you need the customer reviews of specific Amazon products (rating, text, date, reactions, images).

Returns: the run's dataset rows as structured JSON (one row per result), plus the run and dataset IDs for paging the full dataset.

Behavior: starts one run of the Apify Actor "junglee/amazon-reviews-scraper" through Apify's hosted MCP gateway (mcp.apify.com) with your own APIFY_TOKEN; the run uses your Apify account. Read-only: it only reads public web data and changes nothing on the source site. Docs: https://apify.com/junglee/amazon-reviews-scraper

Input Schema

NameRequiredDescriptionDefault
sortNoSelect by which criteria the reviews should be sorted. Amazon's default is Top reviews . Options: helpful = Top reviews; recent = Most recent.helpful
maxReviewsNoType in the amount of reviews to be scraped. Leave this blank if you want to extract all reviews. ❗ Note: max amount of reviews per product is 100 per star count (e.g. 100 reviews per 1 star), 100 per product variant (100 reviews per each product variant), several hundred for media reviews, ~20 for keyword search Example: 100
productUrlsYesEnter the URL or URLs of the Amazon products you want to scrape. (E.g., https://www.amazon.com/Sceptre-E248W-19203R-Monitor-Speakers-Metallic/dp/B0773ZY26F). Click + Add to scrape multiple URLs at once. Example: [{"url":"https://www.amazon.com/dp/B0CWXNS552"}]
filterByRatingsNoSelect the number of stars by which reviews should be filtered. Amazon's default is All stars . Duplicate reviews, caused by selecting overlapping ratings (e.g., 1 star and All critical ) will be filtered out.
reviewsCutoffDateNoLimit how far back to the history the scraper should go. The date should be in YYYY-MM-DD or full ISO absolute format or in relative format e.g. 1 days, 2 months, 3 years. All time values are taken in UTC timezone. The maximum reviews per product we can get is 100.
includeGdprSensitiveNoPersonal data such as name, ID or profile image is protected by GDPR in the European Union and by other regulations around the world. You should not scrape personal data unless you have a legitimate reason to do so. [Read more](https://blog.apify.com/is-web-scraping-legal/).
scrapeProductDetailsNoIf enabled, the Actor will scrape and save product data the same way as the [Amazon Product Scraper](https://console.apify.com/actors/BG3WDrGdteHgZgbPK). The product details will be available in the `product` output field.
reviewsFilterByKeywordsNoEnter keywords by which the reviews should be filtered. Each keyword is scraped separately, in a combination with Filter by ratings option. Duplicate reviews, caused by selecting overlapping keywords, will be filtered out. Example keywords that get lots of results: `["Good", "Bad", "Great", "Quality", "Useful", "Love", "Price"]`
deduplicateRedirectedAsinsNoIf set, the crawler will deduplicate the redirected ASINs (by default). Otherwise the product will be saved multiple times under the ASINs it was redirected to.
reviewsAlwaysSaveCategoryDataNoIf enabled, the Actor will return the review category data even if there are no reviews. - Normally, only reviews are saved, if there are no reviews, the review category data is not saved. - With this option, the review category data will be saved alone, if there are no reviews (review fields are empty).
reviewsUseProductVariantFilterNoIf the product has multiple variants (e.g., different colors, sizes, etc.) , you can scrape reviews only for the selected variant.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

A4.3/5.0
Behavior5/5

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

Beyond the supplied annotations, the description discloses that each call starts a run of the 'junglee/amazon-reviews-scraper' Actor through Apify's hosted MCP gateway, requires the caller's own APIFY_TOKEN, and consumes the caller's Apify account — operationally critical context not implied by readOnlyHint/openWorldHint. It also explains the return shape (dataset rows plus run and dataset IDs for paging the full dataset) and the read-only nature of the scrape, with no conflict against destructiveHint=false or idempotentHint=false.

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 labeled 'When to use / Returns / Behavior' structure is front-loaded and easy to scan. There is minor waste: the first sentence restates the title, and the 'download as HTML, JSON, CSV, Excel, XML' export list is irrelevant noise for an MCP call that returns JSON dataset rows.

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

Completeness4/5

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

For an 11-parameter scraper with no output schema, the description covers purpose, selection criteria, return shape, auth requirement, and account impact — enough to invoke it correctly. What remains thin is how long a run takes or whether the caller must poll using the returned run/dataset IDs, which an agent running this tool would want to know.

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% and all 11 parameters carry titles, defaults, enums, and explanatory text (maxReviews limits, reviewsCutoffDate formats, keyword duplication behavior). The description adds no parameter-level guidance of its own, so the baseline 3 applies — the schema is doing nearly all the work.

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 names a specific verb and resource ('extract reviews from Amazon products') and enumerates the extracted fields (rating score, review description, reactions, images). Against the nearest sibling, junglee--amazon-bestsellers, the resource distinction (reviews vs. bestseller rankings) is obvious, so an agent can route correctly without opening either schema.

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

Usage Guidelines4/5

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

It provides an explicit 'When to use' line scoped to 'customer reviews of specific Amazon products', which is clear positive guidance. It stops short of naming alternatives or exclusions (e.g., when to prefer the bestsellers tool or the product-details option), so it earns a 4 rather than a 5.

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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