de en es ja ko ru zh

amazon-scraper-api

Name: amazon-scraper-api
Author: ChocoData-com

by ChocoData-com

Overview Schema Related Servers Score Discussions

JavaScript

Remote

amazon-scraper-api-mcp

npm npm downloads license

MCP-сервер (Model Context Protocol) для Amazon Scraper API. Подключается к Claude Desktop, Cursor, Claude Code, Continue или любому другому AI-клиенту с поддержкой MCP. Предоставляет вашей модели актуальные данные о товарах Amazon в качестве инструмента для вызова.

Что это дает

"Найди мне самые высокооцененные беспроводные наушники дешевле $150 на amazon.com, а затем проверь, не дешевле ли они на amazon.de"

Это один запрос. Без MCP ваш ИИ не может открывать страницы Amazon (Amazon блокирует просмотр для LLM) и не имеет актуальной информации о ценах и наличии. С этим MCP-сервером он напрямую вызывает amazon_search + amazon_product и получает структурированные данные от Amazon Scraper API.

Related MCP server: Amazon Product Intelligence Agent

Доступные инструменты

Инструмент	Что он делает	Типичное использование
`amazon_product`	Получение одного товара по ASIN или URL	"получить цену + рейтинг для B09HN3Q81F"
`amazon_search`	Поиск по ключевым словам с сортировкой/фильтрацией	"топ 10 чугунных сковородок дешевле $50"
`amazon_batch_create`	Постановка в очередь до 1000 ASIN для асинхронного парсинга	"пропарсить все 500 товаров в моем каталоге, отправь вебхук по завершении"
`amazon_batch_status`	Проверка прогресса пакетной задачи	"какая часть пакета xyz уже готова?"

Каждый инструмент возвращает структурированный JSON: название, цена, рейтинг, количество отзывов, наличие, блок покупки (buybox), варианты, изображения, маркированные списки, категории, таблицы характеристик.

Бенчмарк (реальная эксплуатация, 04.2026)

Метрика	Значение
Медианная задержка (товар, США)	~2.6 с
Задержка P95	~6 с
Цена / 1,000 запросов	$0.50 фиксировано
Маркетплейсы	20+

Настройка Claude Desktop

Отредактируйте ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) или %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "amazon-scraper": {
      "command": "npx",
      "args": ["-y", "amazon-scraper-api-mcp"],
      "env": {
        "ASA_API_KEY": "asa_live_..."
      }
    }
  }
}

Перезапустите Claude Desktop. Инструменты появятся под иконкой MCP в окне чата.

Настройка Cursor

Настройки → MCP → Добавить сервер:

Команда: npx -y amazon-scraper-api-mcp
Окружение: ASA_API_KEY=asa_live_...

Настройка Claude Code

claude mcp add amazon-scraper -- npx -y amazon-scraper-api-mcp
# then set ASA_API_KEY in the environment Claude Code runs in

Пример диалога (Claude Desktop)

Вы: Какая сейчас цена на AirPods Pro 3-го поколения на amazon.com?
Claude: [вызывает amazon_product с ASIN B09HN3Q81F]
AirPods Pro 3-го поколения сейчас стоят $199.00 на amazon.com, вместо $249.00 (скидка 20%). У них рейтинг 4.7 звезды на основе 58,214 отзывов, товар в наличии, продавец и отправитель Amazon.com с Prime.

Вы: Сравни это с немецким листингом Amazon.
Claude: [вызывает amazon_product с query=B09HN3Q81F, domain=de]
На amazon.de тот же товар стоит 229.00 EUR. По сегодняшнему курсу это около $245, что примерно на 23% дороже, чем цена в США. Немецкий листинг отправляется Amazon и доступен для доставки Prime.

Почему это лучше, чем обычный MCP для "просмотра веб-страниц"

Обычные инструменты браузера, пытающиеся загрузить amazon.com, обычно блокируются (проверка на робота) или выдают мобильную версию страницы. Этот сервер направляет каждый вызов через Amazon Scraper API, который:

Обнаруживает и повторяет попытки для страниц с роботами/CAPTCHA через эскалацию прокси-уровней
Возвращает структурированный JSON (типизированные поля), а не HTML-код
Поддерживает 20+ маркетплейсов с использованием резидентских IP-адресов, соответствующих стране
Обрабатывает пакеты (от сотен до тысяч ASIN) с доставкой через вебхуки
Имеет встроенную систему ограничения частоты запросов (backoff)

Обработка ошибок

Ошибки передаются модели как ошибки инструмента с полем code и подсказкой. Модель затем решает, повторить попытку или отказаться от подзадачи. Вам не нужно писать логику обработки ошибок самостоятельно.

Распространенные коды: INVALID_API_KEY, INSUFFICIENT_CREDITS, RATE_LIMITED, target_unreachable, amazon-robot-or-human, extraction_failed, SERVICE_OVERLOADED. Полная таблица: amazonscraperapi.com/docs/errors.

Получить API-ключ

app.amazonscraperapi.com. 1,000 бесплатных запросов при регистрации, кредитная карта не требуется. Этого достаточно, чтобы протестировать каждый инструмент, предоставляемый этим MCP, плюс несколько десятков продуктивных чатов.

Ссылки

Веб-сайт: https://www.amazonscraperapi.com/
Документация: https://amazonscraperapi.com/docs
Статус: https://amazonscraperapi.com/status
Цены: https://amazonscraperapi.com/pricing
Node SDK: amazon-scraper-api-sdk · Python SDK: amazonscraperapi-sdk · Go SDK: github.com/ChocoData-com/amazon-scraper-api-sdk-go · CLI: amazon-scraper-api-cli

Лицензия

MIT

Available Tools

4 tools

amazon_batch_createA

Queue up to 1000 ASINs or search queries for async processing. Returns a batch id - poll with amazon_batch_status or receive a webhook callback.

ParametersJSON Schema

Name	Required	Description
`endpoint`	Yes
`items`	Yes
`webhook_url`	No	Optional HTTPS callback URL

TDQS

A3.5/5.0

Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions async processing and result retrieval methods, but lacks details on side effects, auth, rate limits, or error handling. The description is insufficient for a tool with no structured behavioral hints.

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, well-structured sentence that front-loads the purpose and includes key details (limit, result method). No extraneous words.

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 missing annotations and output schema, the description covers main function and result retrieval. However, it lacks details on error handling, item validation, and batch id format. Adequate but with gaps.

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 coverage is 33%, only webhook_url has a description. The description adds context for items ('ASINs or search queries') but does not explain endpoint values or items structure beyond the schema. It partially compensates but not fully.

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 queues up to 1000 ASINs or search queries for async processing and returns a batch id. It distinguishes from siblings like amazon_batch_status (poll), amazon_product, and amazon_search (single lookups).

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 use for multiple items ('up to 1000 ASINs or search queries') and mentions polling or webhook, but does not explicitly state when to avoid this tool (e.g., for single queries) or contrast with siblings.

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

amazon_batch_statusB

Poll an async batch job for progress + results.

ParametersJSON Schema

Name	Required	Description	Default
`id`	Yes

TDQS

B3.4/5.0

Behavior3/5

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

With no annotations provided, the description must disclose behavioral traits. It indicates a read-only polling operation, but does not mention safety, rate limits, or whether it blocks or returns immediately. Adequate but minimal.

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?

A single sentence with no fluff, front-loaded with the action verb 'Poll'. Every word is necessary.

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?

The description is adequate for a simple polling tool, but lacks context on return values (no output schema) and lifecycle (e.g., relationship to batch creation). More detail would improve completeness.

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

Parameters2/5

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

The only parameter 'id' has no description in the schema (0% coverage) and the description does not explain what it represents (e.g., the job ID from amazon_batch_create). The description fails to add meaning beyond the schema.

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 uses a specific verb 'Poll' and resource 'async batch job', and specifies that it returns 'progress + results'. This clearly distinguishes it from sibling tools like amazon_batch_create (creation) and unrelated searches.

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?

No guidance is provided on when to use this tool versus alternatives, such as indicating that it should be called after creating a batch job with amazon_batch_create, or any prerequisites or polling behavior.

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

amazon_productB

Fetch structured data for a single Amazon product by ASIN. Returns ~55 fields including title, price, variations, reviews, category ladder, images.

ParametersJSON Schema

Name	Required	Description	Default
`query`	Yes	10-character Amazon ASIN, e.g. "B09HN3Q81F"
`domain`	No	Amazon marketplace TLD	com
`language`	No	Content language xx_YY (e.g. en_US, de_DE). Not all combos supported per marketplace.

TDQS

B3.4/5.0

Behavior2/5

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

No annotations provided; description leaves burden on text. States return fields but omits idempotency, rate limits, authentication needs, or any side effects. For a fetch tool, read-only behavior is implied but not explicit.

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?

Single sentence is efficient and front-loaded with action 'Fetch structured data'. No wasted words.

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?

With 3 parameters and no output schema, description mentions ~55 fields and examples. Lacks error handling, response format, or data shape beyond field list.

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 covers all parameters, description adds no extra meaning beyond 'by ASIN'. Baseline 3 applies due to full schema coverage.

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?

Description clearly states verb 'Fetch', resource 'structured data for a single Amazon product', and identifier 'by ASIN'. Lists sample fields, distinguishing from sibling batch and search tools.

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?

Implicitly for single product lookup by ASIN, but no explicit comparison with sibling tools (amazon_search, amazon_batch_*). Lacks when-not-to-use or alternative guidance.

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

amazon_searchA

Run an Amazon keyword search. Returns ranked product listings with organic/sponsored positions, prices, ratings, and image URLs.

ParametersJSON Schema

Name	Required	Description	Default
`query`	Yes	Search keywords
`domain`	No		com
`sort_by`	No		best_match
`start_page`	No
`pages`	No

TDQS

A3.6/5.0

Behavior3/5

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

No annotations are provided, so the description must disclose all behavioral traits. It mentions output includes organic/sponsored positions, prices, ratings, and image URLs, but does not address pagination behavior, result count per page, rate limits, or authentication needs. Adequate but not fully transparent.

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 with a clear verb and a returns clause. No wasted words; front-loaded with purpose.

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?

For a search tool with multiple parameters (sorting, pagination, domain) and no output schema, the description lacks details on result structure, items per page, and sorting behavior. It does not connect to sibling tools or mention prerequisites.

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

Parameters2/5

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

Schema description coverage is only 20% (only query has a description). The description does not explain parameters like domain, sort_by, start_page, or pages, leaving their semantics unclear. It adds minimal value beyond the schema defaults and enums.

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 runs an Amazon keyword search and lists the returned data: ranked product listings with organic/sponsored positions, prices, ratings, and image URLs. It distinguishes from siblings like amazon_product (single product) and batch tools.

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 searching Amazon, but does not explicitly state when to use this tool versus siblings like amazon_product for detail retrieval or batch tools for bulk operations. No guidance on when not to use it.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

4 tool updatesv0.1.5May 29, 2026
- First observedamazon_batch_create
- First observedamazon_batch_status
- First observedamazon_product
- First observedamazon_search

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A3.8/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: batch creation, batch status polling, single product fetch, and keyword search. No overlap between tools.

Naming Consistency4/5

All tools use snake_case and start with 'amazon_'. Three follow verb_noun pattern ('batch_create', 'batch_status', 'search'), while 'amazon_product' is a noun implying fetch. Minor inconsistency but overall predictable.

Tool Count5/5

Four tools cover the essential scraping operations: search, single product details, and async batch processing. The count is well-scoped for the server's purpose.

Completeness4/5

Covers search, single product, and batch processing. Missing dedicated review or category tools, but the product tool includes reviews. Minor gaps but core workflows are complete.

Maintenance

ActivityInactive

ResponsivenessSyncing

Resources

Need Help?

Related Servers

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

Canopy APIOAuth
co.canopyapi
Amazon product API & MCP server: search, prices, reviews, sellers, deals, and ASIN lookup by Canopy.
E-commerce & Retail Search
12
ReefAPI A
io.github.reefapi
One MCP server for 180+ live web-data APIs returning clean JSON from sites that block scrapers.
Web Scraping Browser Automation
523
Apiguru Amazon Data A
app.apiguru
Live Amazon product, review, search, deal, offer/stock and seller data across 20 marketplaces.
E-commerce & Retail Search
121
ListingGood MCP A
io.github.ryanyang828
Get recommended by Amazon's AI. Hosted MCP server for Amazon listing compliance & generation.
E-commerce & Retail Marketing
821

Related MCP Servers

MCP4Amazon
E-commerce & Retail Web Scraping
Jeremy-33456
F
license
Not graded
quality
D
maintenance
An MCP server that enables large language models to directly access and analyze Amazon product information, including product details, variants, and reviews.
Updated a year ago (2025-06-03 09:15 UTC)
-
Amazon Product Intelligence
Web Scraping E-commerce & Retail Data Visualization
explorellm32-usr
F
license
Not graded
quality
D
maintenance
This MCP server scrapes Amazon product details and reviews, manages a local JSON database, and visualizes intelligence data through a rich dashboard.
Updated 4 months ago (2026-05-02 06:35 UTC)
-
sscofficial
E-commerce & Retail Web Scraping
ShoppingResult
A
license
Not graded
quality
D
maintenance
MCP server for scraping product prices, offers, reviews, and details from Amazon, Google Shopping, Bol.com, and Coolblue via natural language commands, with spend-cap protections.
Updated a month ago (2026-07-23 17:33 UTC)
32
1
MIT
Amazon Price Tracker MCP
aravindtri
F
license
A
quality
C
maintenance
MCP server for tracking Amazon product prices via camelcamelcamel.com, using headless Chromium to bypass Cloudflare protection.
Updated 2 months ago (2026-07-18 17:33 UTC)
3
-

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ChocoData-com/amazon-scraper-api-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server

amazon-scraper-api-mcp

Что это дает

Доступные инструменты

Бенчмарк (реальная эксплуатация, 04.2026)

Настройка Claude Desktop

Настройка Cursor

Настройка Claude Code

Пример диалога (Claude Desktop)

Почему это лучше, чем обычный MCP для "просмотра веб-страниц"

Обработка ошибок

Получить API-ключ

Ссылки

Лицензия

Available Tools

Tool Schema Changelog

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

sscofficial

Latest Blog Posts

MCP directory API