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amazon-scraper-api

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amazon-scraper-api-mcp

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MCP (Model Context Protocol) Server für die Amazon Scraper API. Lässt sich in Claude Desktop, Cursor, Claude Code, Continue oder jeden anderen MCP-kompatiblen KI-Client integrieren. Stellt Ihrem Modell Live-Amazon-Produktdaten als erstklassigen Tool-Aufruf zur Verfügung.

Was es ermöglicht

"Finde die am besten bewerteten kabellosen Ohrhörer unter 150 $ auf amazon.com und prüfe dann, ob sie auf amazon.de günstiger sind."

Das ist ein Prompt. Ohne MCP kann Ihre KI keine Amazon-Seiten abrufen (Amazon blockiert LLM-Browsing) und hat keinerlei Aktualität bei Preisen und Lagerbeständen. Mit diesem MCP-Server ruft sie direkt amazon_search + amazon_product auf und erhält strukturierte Daten von der Amazon Scraper API.

Related MCP server: Amazon Price Tracker MCP

Verfügbare Tools

Tool

Was es tut

Typische Verwendung

amazon_product

Ruft ein Produkt per ASIN oder URL ab

"Preis + Bewertung für B09HN3Q81F abrufen"

amazon_search

Stichwortsuche mit Sortierung/Filter

"Top 10 gusseiserne Pfannen unter 50 $"

amazon_batch_create

Warteschlange für bis zu 1000 ASINs für asynchrones Scraping

"Scrape alle 500 Produkte in meinem Katalog, benachrichtige mich per Webhook, wenn fertig"

amazon_batch_status

Status eines Batches abfragen

"Wie viel von Batch xyz ist fertig?"

Jedes Tool gibt strukturiertes JSON zurück: Titel, Preis, Bewertung, Anzahl der Rezensionen, Verfügbarkeit, Buybox, Varianten, Bilder, Bullet-Points, Kategorien, Spezifikationstabellen.

Benchmark (Live-Produktion, 04/2026)

Metrik

Wert

Median-Latenz (Produkt, US)

~2,6 s

P95-Latenz

~6 s

Preis / 1.000 Anfragen

0,50 $ pauschal

Marktplätze

20+

Einrichtung für Claude Desktop

Bearbeiten Sie ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) oder %APPDATA%\Claude\claude_desktop_config.json (Windows):

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

Starten Sie Claude Desktop neu. Die Tools erscheinen unter dem MCP-Symbol im Chat-Composer.

Einrichtung für Cursor

Einstellungen → MCP → Server hinzufügen:

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

  • Umgebung: ASA_API_KEY=asa_live_...

Einrichtung für 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

Beispielkonversation (Claude Desktop)

Sie: Was ist der aktuelle Preis der AirPods Pro der 3. Generation auf amazon.com?

Claude: [ruft amazon_product mit ASIN B09HN3Q81F auf]

Die AirPods Pro der 3. Generation kosten derzeit 199,00 $ auf amazon.com, reduziert von 249,00 $ (20 % Rabatt). Sie haben eine 4,7-Sterne-Bewertung bei 58.214 Rezensionen und sind auf Lager, versandt und verkauft von Amazon.com mit Prime.

Sie: Vergleiche das mit dem deutschen Amazon-Angebot.

Claude: [ruft amazon_product mit query=B09HN3Q81F, domain=de auf]

Auf amazon.de ist dasselbe Produkt für 229,00 EUR gelistet. Zum heutigen Wechselkurs sind das etwa 245 $, also rund 23 % mehr als der US-Preis. Das deutsche Angebot wird von Amazon versandt und ist für Prime-Lieferung qualifiziert.

Warum dieses Tool statt eines allgemeinen "Web-Browsing"-MCP?

Allgemeine Browser-Tools, die versuchen, amazon.com zu laden, werden normalerweise blockiert (Roboter-Check) oder erhalten eine für Mobilgeräte reduzierte Seite. Dieser Server leitet jeden Aufruf über die Amazon Scraper API, welche:

  • Roboter-/CAPTCHA-Seiten erkennt und über eskalierende Proxy-Ebenen erneut versucht

  • Strukturiertes JSON (typisierte Felder) zurückgibt, keinen HTML-Salat

  • 20+ Marktplätze mit länderspezifischen Residential-IPs unterstützt

  • Batches (Hunderte bis Tausende von ASINs) mit Webhook-Zustellung verarbeitet

  • Integriertes Rate-Limit-Backoff besitzt

Fehlerbehandlung

Fehler werden dem Modell als Tool-Fehler mit einem code-Feld und einem Hinweis angezeigt. Das Modell entscheidet dann, ob es die Teilaufgabe wiederholen oder abbrechen soll. Sie müssen die Fehlerbehandlungslogik nicht selbst schreiben.

Allgemeine Codes: INVALID_API_KEY, INSUFFICIENT_CREDITS, RATE_LIMITED, target_unreachable, amazon-robot-or-human, extraction_failed, SERVICE_OVERLOADED. Vollständige Tabelle: amazonscraperapi.com/docs/errors.

API-Schlüssel erhalten

app.amazonscraperapi.com. 1.000 kostenlose Anfragen bei der Registrierung, keine Kreditkarte erforderlich. Genug, um jedes Tool, das dieses MCP bereitstellt, sowie ein paar Dutzend produktive Chats zu testen.

Links

Lizenz

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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