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pangolinfo

Amazon All-in-One Scrape MCP

Official

search_amazon_alexa

Ask Amazon's AI shopping assistant Rufus in natural language to get structured product recommendations with follow-up questions for scene-based or open-ended sourcing.

Instructions

[Amazon Rufus AI conversational recommendations] Ask Amazon's AI shopping assistant Rufus in natural language, get grouped structured product recommendations + Rufus text reply + follow-up questions. Use when: user says "ask Amazon AI X" / "Rufus recommendations" / "find products conversationally" / "products for a scene (gifting / camping / moving)" / "open-ended sourcing" / "I have no keyword, just a scenario". Don't use: when you already have a clear keyword and want SERP (use search_amazon); category bestseller ranks (use list_bestsellers); single-ASIN detail (use get_amazon_product); Google-side AI search (use ai_search). Returns: data.json[{ prompt, content, products[{ title, items[{ asin,url,title,cover,score,ratingsCount,price,originalPrice,describe }] }], follow_up_questions[], screenshot }] + top-level taskId / url / screenshot. Note: follow_up_questions is snake_case (passed through from backend verbatim). Pair with: ↓ feed asin into get_amazon_product / get_amazon_reviews for deep-dive; follow_up_questions can seed the next round's prompts for multi-turn exploration. Cost: 6 points PER PROMPT (billed by prompts count, NOT a flat 6 per call; N prompts = N×6 points). ⚠️ Slow tool: strongly prefer sending exactly 1 prompt per call. A single prompt typically takes 60–90s (Rufus generates the conversation live — far slower than a normal scrape); multiple prompts add up linearly and can exceed 200s, costing both time and points. Treat this as a long-running call: set a generous timeout, and do NOT retry or fire concurrent duplicate calls just because it didn't return instantly. For several needs, make several single-prompt calls rather than batching them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptsYesConversation prompts (zh or en). Each item is sent to Rufus independently and returns its own grouped results. **Billed per prompt: 6 points each** (N prompts = N×6 points, NOT a flat 6 per call). **Strongly prefer exactly 1 prompt per call**: this is a slow tool — 60–90s for one, and multiple add up linearly and can exceed 200s. Max 5, but multiple is both slow and costly; for several needs make several single-prompt calls. Examples: ['gifts for a 5-year-old who loves dinosaurs'] / ['camping gear under $50'].
screenshotNoReturn the Rufus conversation screenshot URL. Defaults to false. Setting true adds backend load; only enable when you need an image proof for end users.
Behavior5/5

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

No annotations provided, so description carries full burden. Details: 6 points per prompt (not per call), slow (60-90s per prompt), linear scaling, do not retry, output structure including snake_case follow_up_questions.

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?

Description is well-structured with clear sections but somewhat verbose. Front-loaded purpose and usage, but could be trimmed without losing essential info.

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

Completeness5/5

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

Given complexity (cost, slowness, output structure) and no output schema, description is thorough: explains return format, cost billing, pairing, and warnings.

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

Parameters4/5

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

Schema coverage is 100% with detailed descriptions. Description adds value by emphasizing cost, slowness, and usage recommendations beyond 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?

Description clearly states it uses Amazon Rufus AI for conversational product recommendations. Explicitly distinguishes from siblings: lists when not to use and alternatives (search_amazon, list_bestsellers, get_amazon_product, ai_search).

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

Usage Guidelines5/5

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

Provides explicit 'Use when' and 'Don't use' sections. Also gives strong guidance on preferring 1 prompt per call due to cost and slowness.

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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