Skip to main content
Glama
deenesjakoruzh
ChocoData-com

amazon-scraper-api

by ChocoData-com
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

amazon-scraper-api-mcp

npm npm downloads license Glama MCP server

MCP (Model Context Protocol) server for Amazon Scraper API. Plugs into Claude Desktop, Cursor, Claude Code, Continue, or any MCP-compatible AI client. Gives your model live Amazon product data as a first-class tool call.

What it unlocks

"Find me the highest-rated wireless earbuds under $150 on amazon.com, then check if they're cheaper on amazon.de"

That's one prompt. Without MCP, your AI can't fetch Amazon pages (Amazon blocks LLM browsing) and has zero recency for prices and stock. With this MCP server, it calls amazon_search + amazon_product directly and comes back with structured data from Amazon Scraper API.

Related MCP server: Amazon Product Intelligence Agent

Tools exposed

Tool

What it does

Typical use

amazon_product

Fetch one product by ASIN or URL

"get price + rating for B09HN3Q81F"

amazon_search

Keyword search with sort/filter

"top 10 cast iron skillets under $50"

amazon_batch_create

Queue up to 1000 ASINs for async scrape

"scrape all 500 products in my catalog, webhook me when done"

amazon_batch_status

Poll a batch's progress

"how much of batch xyz is done?"

Each returns structured JSON: title, price, rating, reviews count, availability, buybox, variants, images, bullets, categories, spec tables.

Benchmark (live production, 2026-04)

Metric

Value

Median latency (product, US)

~2.6 s

P95 latency

~6 s

Price / 1,000 requests

$0.50 flat

Marketplaces

20+

Claude Desktop setup

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

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

Restart Claude Desktop. Tools appear under the MCP icon in the chat composer.

Cursor setup

Settings → MCP → Add server:

  • Command: npx -y amazon-scraper-api-mcp

  • Env: ASA_API_KEY=asa_live_...

Claude Code setup

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

Example conversation (Claude Desktop)

You: What's the current price of AirPods Pro 3rd gen on amazon.com?

Claude: [calls amazon_product with ASIN B09HN3Q81F]

The AirPods Pro 3rd gen are currently $199.00 on amazon.com, down from $249.00 (20% off). They have a 4.7-star rating from 58,214 reviews and are in stock, shipping from and sold by Amazon.com with Prime.

You: Compare that to the German Amazon listing.

Claude: [calls amazon_product with query=B09HN3Q81F, domain=de]

On amazon.de the same product is listed at 229.00 EUR. At today's exchange rate that's about $245, roughly 23% more than the US price. German listing ships from Amazon and qualifies for Prime delivery.

Why this vs. a generic "browse the web" MCP

Generic browser tools that try to load amazon.com typically get blocked (robot check) or serve a mobile-stripped page. This server routes every call through Amazon Scraper API, which:

  • Detects and retries robot/CAPTCHA pages through escalating proxy tiers

  • Returns structured JSON (typed fields), not HTML soup

  • Supports 20+ marketplaces with country-matched residential IPs

  • Handles batch (hundreds to thousands of ASINs) with webhook delivery

  • Built-in rate-limit backoff

Error handling

Errors are surfaced to the model as tool errors with a code field and a hint. The model then decides whether to retry or abandon the sub-task. You don't have to write error-handling logic yourself.

Common codes: INVALID_API_KEY, INSUFFICIENT_CREDITS, RATE_LIMITED, target_unreachable, amazon-robot-or-human, extraction_failed, SERVICE_OVERLOADED. Full table: amazonscraperapi.com/docs/errors.

Get an API key

app.amazonscraperapi.com. 1,000 free requests on signup, no credit card required. Enough to test every tool this MCP exposes plus a few dozen productive chats.

Links

License

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
NameRequiredDescriptionDefault
endpointYes
itemsYes
webhook_urlNoOptional 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
NameRequiredDescriptionDefault
idYes

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
NameRequiredDescriptionDefault
queryYes10-character Amazon ASIN, e.g. "B09HN3Q81F"
domainNoAmazon marketplace TLDcom
languageNoContent 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.

Tool Schema Changelog

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

  1. 4 tool updatesv0.1.5
    • 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

Unclaimed servers have limited discoverability.

Looking for Admin?

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

Related MCP Connectors

Related MCP Servers

  • MCP4Amazon

    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.
    -
  • ssc by ShoppingResult

    sscofficial

    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.
    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.
    3
    -

Latest Blog Posts

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