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amazon-scraper-api

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amazon-scraper-api-mcp

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Amazon Scraper API 用の MCP (Model Context Protocol) サーバーです。 Claude Desktop、Cursor、Claude Code、Continue、またはMCP互換のAIクライアントにプラグインできます。モデルに対して、Amazonのライブ製品データをファーストクラスのツール呼び出しとして提供します。

何ができるようになるのか

「amazon.comで評価が最も高い150ドル以下のワイヤレスイヤホンを探して、amazon.deでそれより安いかどうか確認して」

これは1つのプロンプトで完結します。MCPがない場合、AIはAmazonのページを取得できず（AmazonはLLMのブラウジングをブロックします）、価格や在庫の最新情報も持っていません。このMCPサーバーを使えば、amazon_search + amazon_product を直接呼び出し、Amazon Scraper API から構造化されたデータを受け取ることができます。

Related MCP server: Amazon Price Tracker MCP

公開されているツール

ツール

機能

一般的な用途

amazon_product

ASINまたはURLで製品を1つ取得

「B09HN3Q81Fの価格と評価を取得」

amazon_search

並べ替え/フィルタリング付きのキーワード検索

「50ドル以下の鋳鉄製スキレット トップ10」

amazon_batch_create

非同期スクレイピング用に最大1000個のASINをキューイング

「カタログ内の全500製品をスクレイピングし、完了したらWebhookで通知」

amazon_batch_status

バッチの進捗状況をポーリング

「バッチxyzはどれくらい完了した？」

それぞれ、タイトル、価格、評価、レビュー数、在庫状況、購入ボックス、バリエーション、画像、箇条書き、カテゴリ、スペック表などの構造化されたJSONを返します。

ベンチマーク (ライブ本番環境、2026年4月)

指標

値

中央値レイテンシ (製品、米国)

約2.6秒

P95レイテンシ

約6秒

価格 / 1,000リクエスト

0.50ドル (固定)

対応マーケットプレイス

20以上

Claude Desktopの設定

~/Library/Application Support/Claude/claude_desktop_config.json (macOS) または %APPDATA%\Claude\claude_desktop_config.json (Windows) を編集します：

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

Claude Desktopを再起動します。チャットコンポーザーのMCPアイコンの下にツールが表示されます。

Cursorの設定

設定 → MCP → サーバーを追加：

  • コマンド: npx -y amazon-scraper-api-mcp

  • 環境変数: ASA_API_KEY=asa_live_...

Claude Codeの設定

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

会話例 (Claude Desktop)

あなた: amazon.comでのAirPods Pro 第3世代の現在の価格は？

Claude: [ASIN B09HN3Q81Fで amazon_product を呼び出し]

AirPods Pro 第3世代は現在、amazon.comで 199.00ドル です。元の249.00ドルから20%オフになっています。58,214件のレビューで4.7の評価を得ており、在庫があり、Amazon.comから発送・販売され、Prime対象です。

あなた: それをドイツのAmazonのリストと比較して。

Claude: [query=B09HN3Q81F, domain=de で amazon_product を呼び出し]

amazon.de では、同じ製品が 229.00 EUR でリストされています。本日の為替レートでは約245ドルとなり、米国の価格より約23%高くなっています。ドイツのリストはAmazonから発送され、Prime配送の対象です。

汎用的な「ウェブブラウズ」MCPとの違い

amazon.com を読み込もうとする汎用的なブラウザツールは、通常ブロックされるか（ロボットチェック）、モバイル用に簡略化されたページが表示されます。このサーバーはすべての呼び出しを Amazon Scraper API 経由でルーティングするため、以下の利点があります：

  • ロボット/CAPTCHAページをプロキシ層の昇格を通じて検出し、再試行します

  • HTMLの塊ではなく、構造化されたJSON（型付きフィールド）を返します

  • 国別にマッチした住宅用IPを使用して20以上のマーケットプレイスをサポートします

  • Webhook配信によるバッチ処理（数百から数千のASIN）を処理します

  • レート制限のバックオフ機能が組み込まれています

エラーハンドリング

エラーは code フィールドとヒントを含むツールエラーとしてモデルに通知されます。モデルは、再試行するかサブタスクを放棄するかを判断します。自分でエラーハンドリングのロジックを書く必要はありません。

一般的なコード: INVALID_API_KEY, INSUFFICIENT_CREDITS, RATE_LIMITED, target_unreachable, amazon-robot-or-human, extraction_failed, SERVICE_OVERLOADED。完全な表: amazonscraperapi.com/docs/errors。

APIキーの取得

app.amazonscraperapi.com。サインアップ時に1,000リクエストが無料、クレジットカード不要です。 このMCPが公開するすべてのツールをテストし、さらに数十回の生産的なチャットを行うのに十分な量です。

リンク

ライセンス

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.

  1. 4 tool updatesv0.1.5
    • First observedamazon_batch_create
    • First observedamazon_batch_status
    • First observedamazon_product
    • First observedamazon_search
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.8/5.0

Scored across 4 tools

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
ResponsivenessNo issues

Resources

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