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pangolinfo

Amazon All-in-One Scrape MCP

Official

ai_search

Perform advanced Google searches to retrieve AI Overview, organic results, and related searches for off-Amazon market research and demand analysis.

Instructions

[AI Search via Google SERP] Scrape publicly-available Google search results (data source: Google; use must comply with Google Terms of Service) with top AI Overview, organic results, and related searches. Two modes: overview (standard SERP) / ai_mode (immersive multi-turn conversational search). Use when: user says "Google for me" / "external demand" / "what do people say about X" / "Reddit/Quora pain points" / "will my content be cited in AI search" / "find user complaints for keyword X"; "consumer voice" step in scouting SOPs; verifying whether a new product concept has off-Amazon demand. Don't use: for on-Amazon search (use search_amazon); when only the trend curve matters (use keyword_trends — cheaper and tighter). Returns: data.{ results_num, ai_overview, json.items[ { type:'ai_overview', items:[{content:[...], references:[{title,url,domain}]}] }, { type:'organic', items:[{title,url,text}] }, { type:'related_searches', items:[...] } ], screenshot, taskId }. Pair with: ↑ query inferred from user; in 'ai_mode' pass followups[1..5] for multi-turn; ↓ ai_overview.references[].url for authoritative external sources, organic items for content-competition analysis. Cost: ~2 points/call, ~30s (slow — Google AI render time). Tips: prefer overview for single queries (cheaper); use ai_mode only when you need decomposed multi-turn investigation. Followups > 5 visibly slow down responses.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch keyword or question. Examples: 'wireless earbuds reviews' (single keyword) / 'how does noise cancellation work' (question) / 'what do people complain about Stanley Quencher' (user pain point).
modeNoSearch mode: 'overview' (default) = standard Google SERP with AI Overview at the top, best for one-shot queries; 'ai_mode' = Google AI Mode immersive search (udm=50), best for complex multi-step questions with follow-ups.overview
followupsNoFollow-up question list (only honored when mode='ai_mode'). Each item is a follow-up question on the previous answer. **More than 5 entries significantly degrades response time.**
screenshotNoWhether to return a screenshot URL of the rendered search page. Defaults to false.
Behavior5/5

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

Discloses cost (~2 points), slowness (~30s), return format structure, behavioral differences between modes, followups limit, and legal compliance. No annotations exist, so description carries full burden and meets it comprehensively.

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 moderately long but well-structured with sections. Every sentence adds value, though a minor reduction could improve conciseness without losing information.

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 4 parameters and no output schema, the description covers input semantics, behavioral traits, return format, cost, usage guidance, and integration hints. No gaps identified.

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

Parameters5/5

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

Schema coverage is 100%, but description adds significant value: query examples, mode behavior explanation, followups degradation warning, screenshot purpose. This goes beyond the schema's basic descriptions.

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 the tool scrapes Google search results with two modes. It distinguishes from siblings by mentioning on-Amazon vs external search and trend-curve tools.

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?

Explicit use cases are listed (e.g., 'Google for me', 'consumer voice' step) and explicit non-use cases (on-Amazon, trend curve) with alternative tools named. Also provides pairing guidance.

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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