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Pangolinfo Amazon Data MCP

ai_search

Scrape Google search results to reveal AI Overviews, organic listings, related searches, and competitor Shopping ads for off-Amazon demand and user pain points.

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; see which Google Shopping ads competitors run / their ad landing pages (the sponsered block). 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:[...] }, { type:'sponsered', items:[{type:'result', url, position:'top'|'bottom', title_of_page, title_above_url}] } ], screenshot, taskId }. ⚠️ The ad block's upstream type is literally spelled 'sponsered' (missing an o — not a typo on our side; match it verbatim, do NOT look for 'sponsored') — it carries Google ad (shopping + text) landing-page url, title (title_of_page), and displayed brand domain (title_above_url). position marks whether the ad appears at the top ('top') or bottom ('bottom') of the page — top ads carry higher exposure weight. 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, sponsered[].url + title_above_url for competitors' paid landing pages and brands, split by position into top/bottom ad slots. 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
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
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).
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.
clientSourceNo调用来源标记。仅由 Pangolinfo Skill 传 skill;普通 MCP 调用省略即可。

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden and does well: it discloses cost (~2 points/call), latency (~30s, flagged slow), mode trade-offs, the fact that followups >5 degrade performance, ToS-compliance constraints, and an upstream data quirk (the misspelled 'sponsered' key to match verbatim). It stops short of describing auth or error/empty-result behavior, so it is strong but not exhaustive.

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?

The content is front-loaded with purpose and organized under labeled sections (Use when / Don't use / Returns / Pair with / Cost / Tips), which aids scanning. It is dense and the Returns block is long, but that length substitutes for a missing output schema rather than being pure padding.

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?

There is no output schema, so the description must explain return values, and it does so in detail, enumerating the response shape, the AI Overview/organic/related/sponsered item types, and the meaning of the 'position' field. Combined with usage, cost, and mode guidance, nothing an agent needs to invoke it correctly is missing.

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 description coverage is 100%, so the schema already documents every parameter with examples, which sets a baseline of 3. The description adds selection guidance beyond the schema — preferring 'overview' for single queries and reserving 'ai_mode' for decomposed multi-turn work, plus the followups[1..5] direction — earning it above baseline.

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 states a specific verb and resource ('Scrape publicly-available Google search results') and immediately scopes the source (Google SERP, AI Overview, organic, related searches). It explicitly differentiates itself from siblings by naming search_amazon for on-Amazon needs and keyword_trends for trend-only work, so an agent can route without opening any schema.

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?

It provides an explicit 'Use when:' block with concrete trigger phrases ('Google for me', 'external demand', 'Reddit/Quora pain points') and a 'Don't use:' block that names the two alternative tools and the conditions selecting them. Both positive and negative routing are covered.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.