[Pangolinfo MCP self-introspection] One call to get the full capability catalog, canonical workflows, and usage tips — no backend call, free. Use when: an AI client first connects to pangolinfo-mcp and needs to quickly grasp "what tools exist" / "how do they chain" / "which workflow for which scene"; user asks "what can you do" / "what capabilities are there"; capability audit before SOP planning. Don't use: for the full description of one specific tool (use tools/list — the 'summary' mode here gives one-liners only); for account balance or remaining credits (CONTRACT §9 forbids exposing account endpoints via MCP). Returns: { version, locale, liveTools[{name, domain, oneLiner, cost}], workflows[{title, steps[], note}], tips[] }. Pair with: ↓ AI decides which concrete tool to call next; does not consume downstream tools. Cost: 0 points (local data, no backend round-trip).

[Amazon SERP scrape] Run a real Amazon keyword search and return the first-page ASIN list. Use when: user says "search Amazon for X" / "who sells X" / "top results for keyword X" / "competitors for X"; or you need a list of ASINs for a keyword as upstream input to deeper analysis. Don't use: for a single ASIN detail (use get_amazon_product); for category bestseller ranks (use list_bestsellers); for Google/external demand on the term (use ai_search or keyword_trends). Returns (format='json', default): data.json[0].data.{ pageIndex, nextPage, keyword, results[{ asin, title, price, star, rating, sales, badge, rank, sponsored, image, delivery }] } — ~22 rows/page. Pagination: use the 'page' param (default 1, 1-based); response's 'nextPage' holds the next page number, 'nextPage=null' means last page reached. Pair with: ↓ feed results[].asin into get_amazon_product / get_amazon_reviews for single-product deep-dive; ↓ feed the same keyword into keyword_trends to compare in-site vs external demand. Cost: ~1 point/page, ~5s. Only paginate when the user explicitly asks for more / Top-N (N>22) / all results — otherwise the first page is enough.

[Amazon single-product detail] Scrape the full PDP for one ASIN. Use when: user supplies a specific ASIN ("look at B0XXXXXXXX" / "check this product's price/rating/seller" / "analyse this competitor"); or as a SOP step after candidate ASINs are picked. Don't use: for many products at once (use search_amazon or list_* series for lists); for reviews only (use get_amazon_reviews — cheaper and more focused). Returns (format='json', default): data.json[0].data.results[0] = { asin, title, price, star, rating, brand, seller{name,id}, parentAsin, ratingDistribution[], aiReviewsSummary, bestSellersRankItems, reviews[{date,star,content,helpful,...}], productOverview[], features[], productDescription[], images[], variantDetails[], attributes[], category_id, breadCrumbs, ... } — 30+ fields (variantDetails summary included). Pair with: ↑ asin typically comes from search_amazon / list_bestsellers / filter_niches; ↓ feed the same asin into get_amazon_reviews for more reviews (the PDP carries only ~5-10). Cost: ~1 point/call, ~5s.

[Amazon review batch scrape] Page-fetch real buyer reviews for an ASIN. Filterable by star / sort / media type. Use when: user says "look at X's negative reviews" / "mine pain points" / "analyse competitor reviews" / "do VOC" / "find user complaints for Listing copy"; or pre-launch critical-review scan; or finding improvement points for listing optimization. Don't use: when the few reviews already in the PDP would suffice (get_amazon_product carries 5-10 reviews + aiReviewsSummary — enough for a quick read); for keyword search (use search_amazon). Returns: data.json[0].data.results[{ reviewId, date, country, star, title, content, author, authorId, authorLink, imgs[], videos, purchased, vineVoice, helpful, attributes }] — ~10 reviews per page. Pair with: ↑ asin typically from search_amazon / get_amazon_product / list_bestsellers; ↓ review text can be fed directly to an LLM for pain-point clustering and keyword extraction. Cost: 10 points per page (expensive). Start with pageCount=1 to confirm data, scale to 3-5 only when needed. Prefer filterByStar='critical' — highest signal density. Tips: filterByStar = all_stars / five_star ... one_star / positive / critical; sortBy = recent (default) | helpful; mediaType = all_contents (default) | media_reviews_only (with photos/videos, higher credibility).

[Amazon Best Sellers] Top-50 ranking for a category with 24h rank deltas. Use when: user says "X category bestsellers" / "who's #1 in X" / "any new entrants climbing" / "benchmark top sellers"; setting baseline products during niche scouting; tracking category leadership in competitor radars. Don't use: for new arrivals (use list_new_releases); for full category listings beyond top 50 (use list_category_products); when you only have a keyword (use search_categories first). Returns: data.json[0].data.{ reftag, recsList } — recsList is a JSON-string array (parse twice); each row { id, metadataMap.{ render.zg.rank, currentSalesRank, percentageChange, twentyFourHourOldSalesRank } }. Pair with: ↑ categorySlug from user or scene inference (e.g. 'electronics' / 'home-garden' / 'beauty'); ↓ feed id (ASIN) into get_amazon_product for single-product deep-dive. Cost: ~1 point/call, ~5s. Tips: categorySlug is the hyphenated English slug in amazon.com/Best-Sellers URL paths.

[Amazon New Releases] Best-selling Top-50 ASINs that hit the market within the last 30 days for a category (backend cap; not 100). Use when: user says "new arrivals in X" / "any breakout new products" / "newly-launched that sell well" / "trending new directions" / "new entrants to monitor"; GTM scouting for new angles; competitor radar catching new entrants. Don't use: for evergreen winners (use list_bestsellers); for full category listings (use list_category_products); when you only have a keyword (use search_categories first). Returns: data.json[0].data.{ reftag='zg_bsnr_g_', recsList } — recsList is a JSON-string array (parse twice); each row { id, metadataMap.{ render.zg.rank, ... } }. Pair with: ↑ categorySlug as in list_bestsellers; ↓ feed id (ASIN) into get_amazon_product to see why it climbed (pitch, pricing, variant strategy). Cost: ~1 point/call, ~5s.

[Amazon seller storefront] List all listings under a merchant ID, paginated (24 rows/page). Use when: user says "show me this seller's products" / "how many SKUs does store X carry" / "competitor storefront category breadth" / "what is this seller pushing" / "research a seller's catalog strategy". Don't use: without a merchant ID (find 'sold by' link on any product PDP first); for a single product (use get_amazon_product). Returns: data.json[0].data.{ pageIndex, maxPage, nextPage, results[{ asin, title, price, star, rating, rank, img }] } — 24 rows/page. Two pagination modes: ① page locates a specific page (default 1); ② pageCount accumulates the first N pages in one call (N≤3, flat-merged into the same results). When pageCount>1, pageIndex/nextPage are blanked (pages already merged). Category filter: categoryId filters the seller's products by category. Pair with: ↑ sellerId usually from get_amazon_product's seller.id field, or from amazon.com/sp?seller=... URL; categoryId extractable from the storefront URL's rh=n:; ↓ feed asin into get_amazon_product to deep-dive hero products. Cost: ~1 point/page, ~5s; pageCount=N billed by pages actually crawled (failed pages refunded). Tips: use pageCount to grab the full multi-page SKU set in one shot (max 3 pages); use page to view one specific page; the first page is enough to glance at what the store sells. Amazon first-party sellerId = 'ATVPDKIKX0DER'.

[Amazon category listing] List concrete on-sale products under a Browse Node ID (paginated, 24 rows/page). Use when: user says "what's selling in category X" / "list products in node 12345" / "show me what's in this category"; after picking a categoryId during scouting, you want to see real listings; competitor-research on category density. Don't use: when only the top-50 winners matter (use list_bestsellers — cheaper and more signal); for category-level aggregate metrics (use filter_categories — sales/search volume/competitor density); for niche rather than full category (use filter_niches). Returns: data.json[0].data.{ pageIndex, maxPage, nextPage, categoryName, pagination, results[{ asin, title, price, star, rating, rank, img }] } — 24 rows/page. Pagination: use the 'page' param (default 1, 1-based); 'nextPage' holds the next page number, 'nextPage=null' or 'page>=maxPage' means last page reached. Pair with: ↑ nodeId from search_categories (keyword→category) or get_category_children (tree drilldown); ↓ asin into get_amazon_product; same categoryId can also feed filter_categories for aggregate metrics. Cost: ~1 point/page, ~5s. Only paginate when the user explicitly asks for more / all results — otherwise the first page is enough.

[Amazon category search] Match Amazon's category tree by keyword (Chinese or English) and return candidate nodes. Use when: user gave a keyword/concept rather than a category id, and a downstream tool needs categoryId / browseNodeId (e.g. filter_niches / filter_categories / list_category_products / inferring list_bestsellers slug); when you need to know where a product concept lives in Amazon's taxonomy. Don't use: when you already have categoryId/nodeId (use get_category_paths for breadcrumbs or a downstream filter directly); when you want to drill the subtree (use get_category_children). Returns: data.items.data[{ browseNodeId, browseNodeIdPath, browseNodeName, browseNodeNameCn, browseNodeNamePath, browseNodeNamePathCn, parentBrowseNodeIdPath, productType, sellable, hasChild }] + pagination. Pair with: ↓ feed browseNodeId into list_category_products / list_bestsellers (derive slug from path) / filter_niches / filter_categories; ↓ feed into get_category_children to drill further; ↓ feed into get_category_paths for breadcrumbs. Cost: ~1 point/call, ~3s.

[Amazon category tree drilldown] List direct children from any node (or omit parent to start at the roots). Use when: user says "show me Amazon's category tree" / "subcategories under X" / "list top-level departments" / "drill to level 3"; building a category map; deciding which level is right after search_categories returned candidates. Don't use: when a keyword jump is faster (use search_categories); when you want products in the category, not its subcategories (use list_category_products). Returns: data.items.data[{ browseNodeId, browseNodeIdPath, browseNodeName, browseNodeNameCn, parentBrowseNodeIdPath, productType, sellable, hasChild }] + data.items.pagination.{ total, page, size, hasNext }; omit parentBrowseNodeIdPath to fetch top-level roots; hasChild=1 means the node has further children. Pagination: use the 'page' param (default 1, size default 10 / max 50); 'pagination.hasNext=true' means the node has more children not yet listed. Pair with: ↑ parentBrowseNodeIdPath either omitted (roots) or from search_categories; ↓ feed each result's browseNodeIdPath back in to drill another level, or into list_category_products / filter_categories. Cost: ~1 point/page, ~3s. Only paginate when a node has unusually many children (>size) and the user explicitly wants all subcategories.

[Amazon category commercial-metrics filter] Filter categories by dozens of metrics (sales, GMS, search volume, conversion, return rate, price tier, competitor density, …) — or use as a "category detail" endpoint by passing a single categoryId. Use when: user says "find categories worth entering" / "high-sales categories" / "low return-rate categories" / "high search-volume but low competition categories" / "show me all metrics for category X"; category-level blue-ocean hunt; getting the 30+ metric snapshot of one category. Don't use: for niche-level (use filter_niches — finer granularity); for actual products in a category (use list_category_products); for just the readable name (use get_category_paths). Returns: data.items.data[{ id, categoryId, marketplaceId, timeRange, sampleScope, snapshotDate, unitSoldSum, glanceViewsSum, searchVolumeSum, netShippedGmsSum, buyBoxPriceAvg, buyBoxPriceTier, searchToPurchaseRatio, returnRatio, asinCount, offersPerAsin, newAsinCount, newBrandCount, avgAdSpendPerClick, unitSoldTrendDirection, unitSoldChangeRateBucket, ... trend + quantile-bucket fields }] + data.items.pagination.{ total, page, size, hasNext }. Pagination: use the 'page' param (default 1, 1-based, size capped at 10); 'pagination.hasNext=true' means more pages exist, 'hasNext=false' means last page. Pair with: ↑ required timeRange ('l7d' common) + sampleScope ('all_asin') + marketplaceId (defaults US); categoryId from search_categories / get_category_children; ↓ feed high-potential categories into list_category_products / list_bestsellers for real listings. Cost: ~1 point/page, ~5s. Tips: size capped at 10 (backend hard limit); only paginate when the user explicitly asks for more candidate categories — single-detail or quick-filter calls are fine on page 1; long-tail filter fields (unitSoldTrendDirections / metricChangeRateBuckets / dozens more) pass through via extraFilters.

[Amazon niche filter] Filter Amazon Niches (a finer-grained "demand cluster" than categories) by 50+ commercial metrics, or use as a "niche detail" endpoint for one niche. Use when: user says "find blue ocean" / "high search volume + low competition niches" / "fast-growing small markets" / "niche scouting" / "give me the deep report on this niche" / "low return-rate niches" / "niches with return rate under 10%"; the core filter step of GTM scouting SOPs; getting fee structure / brand age / new-launch trends for one niche. Don't use: for full categories (use filter_categories); for actual products in a niche (the niche record only carries 1 referenceAsin; combine with categoryId + list_category_products); for plain keyword search (use search_amazon). Returns: data.items.data[{ nicheId, nicheTitle, referenceAsinImageUrl, currency, searchVolumeT90, searchVolumeT360, searchVolumeGrowthT90, minimumPrice, maximumPrice, avgPrice, productCount, sponsoredProductsPercentage, primeProductsPercentage, top5ProductsClickShare, top20BrandsClickShare, brandCount, sellingPartnerCount, avgBrandAge, avgBestSellerRank, avgProductPrice, avgReviewCount, avgReviewRating, avgDetailPageQuality, newProductsLaunchedT180/T360, successfulLaunchesT90/T180/T360, returnRateT360, fee fields T365 … 100+ fields }] + data.items.pagination.{ total, page, size, hasNext }. Pagination: use the 'page' param (default 1, 1-based, size capped at 10 (default 3)); 'pagination.hasNext=true' means more pages exist, 'hasNext=false' means last page. Pair with: ↑ marketplaceId required (defaults US); nicheTitle for keyword filter, nicheId for single-niche detail; ↓ feed referenceAsin into get_amazon_product to see the representative product; niche doesn't carry a categoryId directly — derive separately if needed. Cost: ~1 point/call, ~5s. Tips: size capped at 10 (default 3); pass long-tail filters (50+ fields) via extraFilters; classic blue-ocean combo = high searchVolumeT90Min + low top5ProductsClickShareT360Max + moderate productCountMax + positive searchVolumeGrowthT90Min + returnRateT360Max ≤ 0.10 (low-return). For return-rate filtering use returnRateT360Max (upper bound, 0-1 decimal); the response includes returnRateT360 with the actual return rate.

[Amazon category breadcrumb resolver] Batch-resolve categoryId list to full paths (e.g. 'Electronics > Headphones > Over-Ear Headphones'). Use when: a report needs readable category context (not bare IDs); user has a list of numeric IDs and wants the names; multiple categories need labels for comparison. Don't use: for a single ID — most other tools already return browseNodeNamePath in their responses; for tree structure (use get_category_children). Returns: data.items[{ categoryId, categoryName, categoryNameCn, browseNodeNamePaths[], browseNodeNamePathCns[] }] — one row per input ID. Pair with: ↑ categoryIds from any prior step (filter_niches/filter_categories output, user-pasted ID list); ↓ usually presentation-only, downstream rarely depends on it. Cost: ~1 point/call, ~2s (cheaper than N single resolutions).

[Local Maps via Google Maps] Local-business search (data source: Google Maps; use must comply with Google Terms of Service). Search local businesses at a given lat/lng — returns name, address, rating, review count, etc. Use when: user says "Y businesses in city X" / "local retail research" / "offline channel distribution" / "coffee shops/supermarkets/wholesalers in area" / "physical-store coverage density"; offline competitor/channel research; gauging physical-supply density of a category in a region. Don't use: for e-commerce listings (Amazon series); for global trends (use keyword_trends); for Google search results (use ai_search). Returns: data.organicResults[{ place_id, name, about, rating, number_of_reviews, borough, street_addr, city, postal_code, ... }]. Pair with: ↑ query (business keyword) + latitude/longitude/zoom (zoom 1=world, 13=city, 21=single building); ↓ presentation-focused, downstream rarely consumes. Cost: ~1.5 points/call, ~5s. Tips: zoom 13 (city, default) gives you a whole neighborhood; zoom 17+ narrows to one street.

[Design Patent TRO risk control · WIPO global design / IP search] Query the WIPO design database across 12 sources (USPTO US designs, CNID China, HAGUE international registrations, …), with one-click chaining to US design-patent TRO (temporary restraining order) / litigation risk control. Use when: user says "check trademark" / "design patent search" / "any IP risk for new product" / "X company's patent portfolio" / "WIPO search" / "USPTO query" / "what is registration DM/XXX"; pre-launch IP clearance during scouting/GTM SOPs; competitor IP-portfolio research. Don't use: for keyword ranks / product reviews / product detail (this is an IP database, not a commerce database); for US text-trademark search (this DB focuses on design patents — text trademark coverage is limited). Returns: data.data.{ total, hits[{ IRN, HOL[], DETAIL_DATA.structured.{indication_of_products, statement_of_novelty, ...}, IMG[], IMG_DATA[{filename,url}], DC, RD, STATUS, LCS[], DS[], PROD[], SOURCE, DETAIL_URL }] }. With enableLitigation=true each matched patent additionally carries litigationStatus(success/skipped/failed) + caseTotal + cases[{ caseId, docketNumber, caseName, court, status, dateFiled, parties[], patentNumbers[], entries[] }] (backed by US PACER litigation data — one call returns patents + lawsuits). Pair with: ↑ source required; hol=holder name / prod=product name / irn=international registration / lcs=design classification; enableLitigation=true chains US litigation lookup (IP-risk loop, no separate tool needed); ↓ DETAIL_URL lets the user jump to WIPO's official page to verify. Cost: ~2 points/call, ~5s; with enableLitigation=true add +12 points only when a patent is found (free if none). ⚠️ Perf contract: CNID + hol/prod MUST be paired with id/idSearch/rd/status/lcs (otherwise the backend rejects to avoid a 17M-row full scan); JPID has no HOL/PROD; USID has no STATUS; ed (expiration date) is silently ignored on all sources — filter dates via rd instead. With enableLitigation on, each page re-triggers the litigation query and billing.

[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.

[Keyword Trends via Google Trends] Keyword popularity (data source: Google Trends; use must comply with Google Terms of Service). Time series + per-region heatmap + rising related queries (with 'Breakout' tags). Compare up to 5 keywords on one chart. Use when: user says "how hot is keyword X" / "A vs B popularity" / "any seasonality" / "which states love X" / "find breakout terms" / "new-product direction" / "trend comparison" / "is X past its peak yet". Don't use: for absolute search volume (Trends is 0-100 relative); for products/links (use search_amazon / ai_search); for a single keyword's snapshot (need ≥ 2 for meaningful comparison). Returns: data.json.{ keywordsGeoData[{ keyword, geoMapData[{ geoCode, geoName, value[], formattedValue[], hasData[] }] }], keywordsRankData[{ keyword, rankList[{ rankedKeyword[{ query, value, formattedValue, link, hasData }] }] }], timelineData[{ time, formattedTime, value[], formattedValue[] }], geoMapData[] }, taskId, url. Pair with: ↑ keywords from user or core terms found via search_amazon; ↓ feed Breakout/rising terms back into search_amazon to explore new opportunities, or filter_niches to see if they've crystallized into a niche. Cost: ~1.5 points/call, ~5s. Tips: timeRange = today 12-m (default) | today 3-m | today 5-y | all ; region = ISO country code or 'WORLD'; language affects related-query language.

[Generic Amazon scrape — power-user escape hatch] Scrape pages the 5 purpose-built tools don't cover. Two input modes (pick one): ① content=bare fragment (keyword / nodeId / sellerId / ASIN) + site — backend builds a basic URL per parserName. content mode carries NO filter/sort/pagination — it's just the bare fragment. Best for simple pages when you only have the fragment. ② url=full Amazon link — put ANY filter/sort/pagination into this url (the only way, since content mode can't). Filter syntax examples: price $25-50 → '/s?k=earbuds&low-price=25&high-price=50'; sort by reviews → '&s=review-rank'; paginate → '&page=2'; category+price → '/s?i=aps&rh=n%3A172282&fs=true&low-price=25'. Use when: a standard tool can't build the target URL — "search X but only $25-50" / "results sorted by reviews" / "category filtered by price"; or the user already has a specific Amazon link. For any filtering, use url mode. Don't use: when a purpose-built tool fits — plain keyword search → search_amazon, single ASIN → get_amazon_product, seller → list_seller_products, category ranks → list_bestsellers/list_new_releases. Returns (format='json'): data.json[0].data.{ ... results[] ... }, shape depends on parserName. ⚠️ If content/url doesn't match parserName, the backend returns data.{ status_code, rawHtml, url } (unparsed). Pair with: ↓ feed asin into get_amazon_product / get_amazon_reviews. Cost: ~1 point/call, ~5s. ⚠️ Pass exactly one of content / url (both or neither errors); filtering/pagination requires url mode; parserName must match the page type.

[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.