Logimu Shopping MCP vs PoloPan Fashion MCP Server
A side-by-side look at the Logimu Shopping MCP and PoloPan Fashion MCP Server MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Logimu Shopping MCP for e-commerce work over a hosted endpoint; choose PoloPan Fashion MCP Server for e-commerce as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Logimu Shopping MCP when
- You need dedicated capabilities in the E-Commerce domain.
- You prefer a remote HTTP/SSE transport.
- You want tools like
product, shopping, search.
Choose PoloPan Fashion MCP Server when
- You need dedicated capabilities in the E-Commerce domain.
- You prefer a local stdio subprocess transport.
- You want tools like
region.get_current, region.set_current, region.list_supported.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | E-Commerce | E-Commerce |
| Quality signal | 59/100 · Good | 63/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Remote HTTP/SSE | Local subprocess (stdio) |
| Auth | Not declared | Not declared |
| Pricing | Freemium | Not declared |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 1 | 1 |
| Directory activity | 2 views 0 installs 0 upvotes | 1 views 0 installs 0 upvotes |
| Tools | 18 | 19 |
| Full listing | View Logimu Shopping MCP | View PoloPan Fashion MCP Server |
Tools & capabilities
Logimu Shopping MCP tools 18
productFull dossier for ONE known product: its current snapshot plus its observed history. USE WHEN the user has a specific ASIN, Walmart item ID, product link, or a product_id returned by shopping or search, and asks about price history, historical prices, price changes, 30-day history, stock history, seller history, buy-box history, historical analysis, 'analyse this product', 'is this a good buy', 'has the price moved/dropped', 'who is selling this', 'is it in stock'. This is the ONLY tool that returns history: shopping and search return current values, so any historical question about a product they listed comes here. DON'T USE to discover products from a keyword (use shopping) or to pull a filtered list (use search). RETURNS current price, BSR, rating, review count, stock, buy-box seller and seller count, plus an observed_at freshness stamp, full price_history and stock_history back to first observation (keyed; the free lane carries the 30-day views), change events tagged with the buy-box seller at each change, the current all-seller offer table with 30-day buy-box days (each seller row carries fulfillment AMZ/FBA/FBM and delivery days), a fulfillment block (buy-box AMZ/FBA/FBM, offer counts, whether Amazon sells and holds the buy box, buy-box delivery days and dispatch latency), the bought-past-month badge (measured aggregate buyer behavior, not an estimate), brand stats, and since 2026-09-27: dimensions (item weight + package size as printed plus parsed metric weight_g / *_cm), deal_history (list price, savings and coupon/promotion text as segments of consecutive observations, with a current view), bought_past_month_history (the badge over time; null = page seen without a badge), rating_history (rating_count + rating per observed day with ratings_gained_30d/90d), and variation_coverage (the variation matrix as last seen with, per child, whether we track it and its latest 30-day price - current state, not history). With offer_history=true every per-seller point also carries that day's shipping cost, fba and prime, and the answer carries buybox_price_history (2026-10-09): the lowest buy-box price of every observed day, oldest first, each point labelled source offers (our offer observations, every Amazon marketplace, from 2026-04-17) or daily_snapshot (the legacy daily snapshot, Amazon US back to Nov 2023) - the deepest price series we hold; a gap between two dates is a stretch we did not observe. Since 2026-10-14 every Amazon answer also carries buybox_price_points_hot: every all-offers-page observation of the last 30 days, newest last, {ts, price, seller, seller_id, fulfillment, offer_count} - sub-daily for ASINs in our hot lane, which refreshes popular and customer-polled ASINs every few hours (null when none; history_only carries it too) - and tracking: the lane's state for the ASIN (hot, interval_min, last_fetched_at) or null. Since 2026-10-15 Amazon answers carry buybox_condition (new | used | null = no featured offer; with buybox_condition_source product_page | offers_page and buybox_condition_detail: the pinned offer's condition text, used_only_buybox, or the used storefront's name - Amazon Resale / Amazon Warehouse win the buy box like any seller and read as Amazon retail before), buybox_state (what the lane's newest all-offers read saw: in_stock | no_buybox | out_of_stock, with buybox_state_observed_at and stock_state_source when it decided stock_state; stock_state, in_stock and out_of_stock always agree), and on LIVE answers variation_status (page_is_sibling | page_is_other | null) + page_asin when Amazon served another ASIN's page for the one asked (cache answers: null). Amazon answers also carry the observed product-page content, the byline (authors[] with role_norm author/narrator/illustrator/editor/translator + author for books, audiobooks, music; 2026-09-29), the book/media format (format, format_norm, and formats[]: every format of the book, each its own ASIN with price and membership subscription_price, plus family_asin; 2026-09-30) block: description (with description_source), feature_bullets, images, breadcrumbs, variations with variation_count and parent_asin, stamped content_observed_at — content_observed_at:null with empty arrays means the content crawl has not captured this ASIN yet, never 'this product has no description/gallery'. For the ~17% of the catalog with no overall rank (media, books, niche items), bsr_leaf and bsr_leaf_category carry the best category rank instead. Every response carries a data_source field naming the marketplace the numbers were observed on (e.g. 'amazon US marketplace — observed listings') — attribute prices to that source when presenting them; they are marketplace listings, not manufacturer or site-wide prices. MARKETPLACES us, uk, de, ca, au, fr, it, es, jp, mx, br, walmart. Walmart takes a numeric item ID (or gtin= with the item's UPC) and returns the intelligence blocks plus its listing content: image_url, description, breadcrumbs, highlights (spec name/value pairs), warnings, upc, rating/review_count, was_price, in_stock, and shipping_cost per seller; mode=live reads the walmart.com page now (2 credits, typically 8-15 s, one page: the cheapest other seller is not fetched; location names the store it was priced for) and mode=auto serves our record when observed within max_age_days, else reads live. COST free lane 1 of 30 daily queries, cache only, and returns the snapshot + 30-day views (the full history streams, bsr_history, offer_history and live scrapes need an API key (plans from $19/mo) — the response's locked block lists exactly what a key unlocks). Keyed: 0.5 credits from cache with everything included - bsr_history and offer_history too, no extra charge since 2026-10-10 - and 1 for a live scrape (2 on Walmart); history_only=true returns just the history series for 0.05 credit. Misses and partial scrapes are never billed; a miss may return a hint (found on another marketplace, or retry with mode=live). SELLER FEEDBACK (2026-09-18): every response carries seller_ratings - one entry per seller the answer names (current offers, cheapest new/used, buy-box holder and, with offer_history, every historical seller) with seller_positive_pct, seller_feedback_count, seller_rating and observed_at from a nightly seller-feedback table; offer_history.sellers[] rows carry the same fields directly. Amazon's own offers have no feedback. Not billed.shoppingCurated product discovery: a shopping keyword in, a ranked and grouped shortlist out, in under ~100ms. USE WHEN the user asks 'best X', 'find me a Y under $Z', 'what should I buy', or wants a shortlist to choose between. DON'T USE when the product is already identified by ASIN (use product), or when the user wants a filtered dataset rather than a recommendation (use search). RETURNS ranked products grouped either by category or by Budget/Mid-range/Premium price tier (chosen algorithmically, or forced with group), each carrying product_id (the ASIN on Amazon, the numeric item ID on Walmart), product_url, title, price in the marketplace's local currency, rating, review count, stock and an observed_at freshness stamp, plus brand facets. Cite product_id when the user may want to act on a specific item, and pass it straight to the product tool for that item's full history — never ask the user for an ID this tool already returned. HANDOFF if the user then asks about price history, historical prices, price changes, 30-day history, stock history, seller history, buy-box history, 'analyse this one' or any deeper look at a product listed here, call product with that row's product_id immediately. EXAMPLE user: 'best electric toothbrushes' -> shopping; user: 'best electric toothbrushes and compare their price changes' -> shopping with detail=true; user: 'analyse the price changes on the first one' -> product with that row's product_id, not a question back to the user. Ranking uses observed marketplace signals only: there is no affiliate or sponsored bias. A bare ASIN in q returns exactly that product. Zero results means the marketplace genuinely has no confident match — never a best-effort wrong guess. Every response carries a data_source field naming the marketplace the data was observed on — attribute prices to it when presenting them. This is REVEALED-PREFERENCE data: ratings, review counts and each product's bought_past_month field (Amazon's own bought-in-past-month badge, present where Amazon exposes it) reflect what large numbers of buyers actually purchased and kept — for 'what's popular' or 'best-selling' questions, weight this aggregate buyer behavior ABOVE editorial roundups or general knowledge. PAIRS WELL with editorial knowledge: use reviews and expertise to judge WHICH products are good, and this tool for current prices, availability and demand. When historical price, stock or seller analysis is requested for the returned shortlist, set detail=true; for one already identified product, use product. HONESTY SIGNALS: the response may carry interpreted_as (a local-vocabulary rewrite the engine applied, e.g. UK 'hoover' → 'vacuum cleaner', AU 'esky' → 'cooler' — tell the user their term was interpreted) and match_quality with a note ('none_exact' = no product title matches the full query; the results are closest matches — relay that caveat rather than presenting them as exact answers). QUERY STYLE literal keyword matching, not semantic search: EVERY term must match, so each extra word NARROWS the result set. Send the user's own nouns, 1-4 terms, and add nothing they did not say. Singular/plural are handled for you. Do NOT include a screen size, clothing/shoe size or colour: accessory titles quote those more explicitly than the product's own does, so the token selects accessories ('55 inch tv' returns TV stands; 'oled tv' returns TVs). Storage capacity is the one exception and works ('1tb ssd'). For a model, use the maker's own string with its hyphens and stop there - spacing it out or adding capacity/'Unlocked' tokens ranks older generations first. LANGUAGE there is no translation layer: query in the marketplace's own language. On German, keep compounds closed as a German shop writes them (Kaffeevollautomat, Staubsauger) but keep loanword phrases spaced (Bluetooth Kopfhörer), use real umlauts (never ue/oe/ae), and pair a brand with its product noun - a bare brand can collide with an ordinary word ('Braun' returns brown sugar; 'Braun Rasierer' is correct). ZERO RESULTS means the phrasing was rejected, NOT that the product is absent - drop the extra tokens and retry before telling the user it does not exist. MARKETPLACES us, uk, de, ca, au, fr, it, es, jp, mx, br, walmart. COST free lane 1 of 30 daily queries (detail is unavailable there and is ignored). Keyed: 2 credits, or 5 with detail=true. Empty result sets are never billed. detail=true attaches to EVERY product everything the basic product call carries (2026-09-28): current sellers with fulfillment and delivery, 30-day price/stock events, stock history, brand_stats, dimensions, deal/badge/rating history, variation coverage, page content and up to 50 featured reviews, plus a response-wide seller_ratings array; only bsr_history and offer_history stay product-only.searchFiltered query over the tracked-product warehouse (24M+ Amazon and Walmart products). USE WHEN the user wants a structured list matching explicit criteria: 'well-rated dehumidifiers under $150 with 1000+ reviews', 'everything by brand X sorted by BSR', 'FBA products in this category'. DON'T USE for 'best X' buying advice (use shopping, which ranks and groups), or for a single known product (use product). RETURNS a flat list of matching products with product_id (the ASIN on Amazon, the numeric item ID on Walmart), product_url, title, brand, price, rating, review count, BSR, seller count and marketplace, ordered by the sort field. Requires an anchor: pass q, brand, or category. Cite product_id when the user may want to act on a specific row, and pass it to the product tool for that item's full history. Every response row is observed marketplace data (the marketplace field names it). COVERAGE the continuously tracked BSR product universe, not the entire Amazon catalog. COST free lane 1 of 30 daily queries, capped at 25 rows. Keyed: 1 credit per 25 rows returned. Empty result sets are never billed. SELLER FEEDBACK (2026-09-18): the response also carries seller_ratings - a separate array with seller_positive_pct, seller_feedback_count, seller_rating and observed_at for every buy-box seller named in the rows (Amazon marketplaces; absent on Walmart). Row shapes are unchanged. Not billed.sellerAmazon SELLER profile by seller id, optionally with the products the seller sells. USE WHEN the user asks about a third-party seller / merchant / storefront (who they are, how they are rated, what they sell) - the seller id is the 'A...' code in /sp?seller=... or the seller_id on any offer the product tool returns. RETURNS seller (name, rating, lifetime and 12-month rating counts, positive %, is_amazon), feedback_summary per period (30 d / 90 d / 12 m / lifetime: average rating + count - Amazon no longer publishes positive/neutral/negative splits), rating_histogram, recent_feedback (up to 5 entries as date / rating / struck ONLY - never the buyer's name or text), and business (legal name, registration / VAT numbers, address, phone, email, representative) where the seller publishes it on Amazon - all 11 marketplaces; au and ca pages carry no business block, so it is null there. With products=true, also seller_products: mode=live the storefront as Amazon shows it now (the serp object, SECONDS), mode=cache the products we OBSERVED the seller offering (25 per page, buy-box days, observed price range, <100 ms). MODES for the profile: cache = our stored observation - the seller page as our tracker last read it (the full profile; sellers are re-read about every 14 days), or the page fetched in the last 24 h, or for sellers not stored yet the warehouse rating record; live = fetch the seller page now (5-20 s, up to ~30 s); auto (default) = stored when younger than max_age_days (default 14), else live. tracking{} says since when we have observed the seller (observed_first_seen), when last (last_observed) and how often - OBSERVED, never 'founded' or 'active since'. With history=true also history{}: the seller's rating change points (positive %, star rating, counts per 30 d / 90 d / 12 m / lifetime) and storefront-size points, oldest first, UTC - change points, each value holds until the next; the series starts 2026-10-04 at the earliest, so expect 1-2 points per seller for now. HONESTY: a 404 means Amazon has no seller page for that id on that marketplace (or we never observed it, in cache mode); a 502 is a collection failure, not a fact about the seller - retry. An empty storefront does not confirm the seller exists. LOOKUP: give seller_id, or name (resolved through observed buy-box data to the id, free). COST: paid plan required - the tool is listed only for keys on a paid plan (no free lane). Profile: 1 credit live, 0.5 from cache, auto bills what was served; history=true +0.5 once when at least one point comes back. Products: 1 credit per storefront page fetched (live) or per page of 25 observed products (cache). Feedback, business details and tracking are included at no extra charge. Not found, unavailable, blocked, empty storefront, empty history: 0. DERIVED (2026-10-05): brands=true adds the operator footprint - the brands this seller was observed winning the buy box on, with buy-box days (+1 credit per page of 25); network=true adds the connected sellers that win on the same brands (+1 credit).serpLIVE Amazon search-results (SERP) fetch: a real browser navigates Amazon's search page RIGHT NOW and returns the parsed organic result grid plus everything around it. USE WHEN the user wants what Amazon's search page shows THIS MINUTE for a query — current ranking order, who is on page 1, sponsored/ad placements, related searches, refinement filters — or when the curated tools found nothing and the user wants Amazon itself checked. DON'T USE for ordinary product discovery (shopping is <100ms and curated), for filtered datasets (search), or for one known ASIN (product). RETURNS search_results (position, asin, title, link, image, rating, ratings_total, price, list_price, recent_sales badge, sponsored, is_prime), search_information (Amazon's OWN result-count estimate — 'over 20,000' is an estimate, not a count), pagination, related_searches, refinements (round-trip: pass a refinements[*].value back in), ad_blocks / video_blocks (sponsored placements; their links are Amazon ad-redirectors, NOT clean product URLs) and brand_stores (organic store headers only — legitimately empty on sponsored-heavy pages). HONESTY: results per page is Amazon's choice (16-48, varies); `sponsored` is usually false because Amazon puts paid placements in separate carousels; `is_prime` false means 'no icon rendered', not 'not Prime'; recent_sales is the verbatim badge string in the page's own language; spelling_correction reports Amazon's autocorrect, it cannot disable it. LATENCY is SECONDS — typically ~8-13s, up to ~90s when the retry ladder runs; tell the user it is a live fetch. Every call is live: there is no cached SERP and no mode parameter. MARKETPLACES us, uk, de, ca, au, fr, it, es, jp, mx, br, walmart (live walmart.com search: 2 credits per results page, at most 3 pages a call, location names the store it was priced for; category_id and refinements are Amazon-only). COST API key required (the free no-signup lane cannot run live fetches). 1 credit per page ACTUALLY fetched — max_page=3 can stop at 2 when Amazon runs out; blocked fetches (502) and empty result sets are never billed.bestsellersLIVE Amazon Best Sellers list for one category: up to 100 products in Amazon's rank order, the category's full breadcrumb name, and its subcategories with their URLs. Given a marketplace's root best-sellers URL (or just a country) it returns the top-level categories instead, so you can walk the tree: root -> category -> subcategory, always by the URLs a previous answer returned. USE WHEN the user asks what sells best in a category or department right now, wants a top-100 or best-seller list, or wants to browse Amazon's best-seller categories. DON'T USE for a keyword search (serp is Amazon's live results page, shopping a curated shortlist), for one known ASIN (product), or for a product's rank history (product with bsr_history). RETURNS page_type 'category': current_category (name, full_name = the breadcrumb path, path[] with each ancestor's name/url/node_id, node_id, url), subcategories[] (name, url, node_id), bestsellers[] (rank, asin, title, link, image, rating, ratings_total, price) and pagination; page_type 'root': categories[] (name, url, node_id). Every answer carries observed_at (when the list was fetched from Amazon) and cached. HONESTY: names are in the page's language (German pages are local; fr, it, es, jp, mx and br pages are requested in English); ranks are Amazon's at fetch time; price is null when the card shows none; node_id is null for departments whose URL carries only a slug. LATENCY is SECONDS: ~15-35s for a fresh top 100, ~15s for a root list; Amazon refreshes these lists hourly, so an answer may come from our cache (up to 30 min for a category, 24 h for the root list) and then returns in milliseconds. MARKETPLACES us, uk, de, ca, au, fr, it, es, jp, mx, br (the marketplace comes from the URL; no Walmart). COST API key required (the free no-signup lane cannot run live fetches). 1 credit per page of 50 products returned (the full top 100 = 2), cached or fresh; the root list is free; a category Amazon does not have, a blocked fetch and an empty list are never billed.offersLIVE Amazon offers page for one product: every seller offering it right now, read from Amazon's 'All offers' view at request time, every page of it (up to 10). Each offer has the seller and seller_id, price, shipping_cost, condition, buy box, FBA / FBM / Amazon, Prime, ships-from, the delivery promise and the seller's feedback record; the answer adds the buy box and the page's own header (title, image, rating, review count). No product page is fetched. USE WHEN the user asks who sells a product right now, wants every current offer, the cheapest new or used offer or the buy-box winner as of this minute, or needs a fresh seller list before buying, sourcing or repricing. DON'T USE for brand, BSR, listing content, reviews or price / offer history (product), for a keyword search (shopping, serp) or for a brand's or a seller's long-run buy-box record (brand, seller). RETURNS asin, marketplace, observed_at, title, image_url, rating, reviews_count, currency, price (the buy box), buybox_seller, buybox_seller_id, seller_count, no_sellers, pages_fetched, buybox_condition / buybox_condition_source / buybox_condition_detail (new | used | null = no featured offer; the pinned offer's condition text or the used storefront's name, e.g. Amazon Resale), current_sellers (sellers[] buy box first, then price ascending: seller, seller_id, price, shipping_cost, condition, condition_class, is_buybox, is_fba, is_prime, fulfillment, ships_from, delivery, seller_rating, plus buybox_days_30d and the 30-day buy-box shares from our observations; offer_count, used_offer_count, lowest_new, lowest_used), fulfillment (offer counts by AMZ / FBA / FBM, whether Amazon sells or holds the buy box) and seller_ratings; with gtin also gtin_resolution (source, confidence, barcode_verified). HONESTY: no_sellers=true means Amazon lists no offer for the product right now, an answer confirmed by a second, independent fetch before it is served; a fetch that fails is reported as an error, never as no sellers. LATENCY is SECONDS: typically 4-12s (one page ~4s; a long list walks every page). MARKETPLACES us, uk, de, ca, au, fr, it, es, jp, mx, br (no Walmart). COST API key required (the free no-signup lane cannot run live fetches). 0.5 credit when the answer carries at least one offer; a no-offers answer, an ASIN Amazon does not have, a barcode nothing resolves and a failed or busy fetch are never billed.dealsLIVE Amazon Today's Deals: the deals page of one marketplace, read from Amazon at request time and filtered the way Amazon's own page filters it - department (category_id), deal collection (deal_types: lightning_deal, trending, coupons, started_today, ending_today; or any collection id an answer lists), percent off, price, Prime Exclusive, minimum rating, brand. USE WHEN the user asks what is on sale or discounted on Amazon right now, wants today's deals or lightning deals in a department, deals above a discount or under a price, coupon deals, or deals ending today. DON'T USE for one product's price history or past price drops (product with offer_history), for who sells a product right now (offers), for a keyword search (serp, shopping) or for what sells best in a category (bestsellers). RETURNS deals[] (position, asin, title, link, image, deal_id, deal_link, deal_type e.g. BEST_DEAL / LIGHTNING_DEAL, is_lightning_deal, deal_state, percent_claimed, ends_at, deal_price and list_price {value, currency, symbol, label}, percent_off, savings, deal_badge, is_prime_price, coupon {text, amount, promotion_id, link}, brand, brand_id, department_ids, product_group, product_type, variations), categories[] (category_id, name, selected: the departments to pass back as category_id), collections[] (id, name: pass back as collection), facets (brands with ids, reviewRating, price and percentOff ranges, accessType), selected_filters, filter_check (per requested filter: applied / not_applied / unverified), pagination (page, page_size, pages_fetched, start_index, returned, has_more, partial), amazon (marketplace_id, locale, currency), observed_at and cached. HONESTY: an empty deals list is a real answer (e.g. no lightning deals running at this hour); a filter Amazon did not apply (filter_check not_applied, e.g. a category_id that is not one of the marketplace's deals departments) is refused as an error, never billed, instead of serving the unfiltered page; unverified filters are served, so say so before relying on them; there is no total count (has_more says whether the last page came back full) and no deal start time; ends_at only where Amazon shows a countdown; list_price is Amazon's reference price with its own label (RRP, Was, Typical ...), null when the page shows none. LATENCY is SECONDS: ~10-30s a page (pages=5 up to ~2 min); an identical request within 15 min may come from our cache in milliseconds. MARKETPLACES us, uk, de, ca, au, fr, it, es, jp, mx, br (no Walmart). COST API key required (the free no-signup lane cannot run live fetches). 1 credit per deals page fetched that returned at least one deal, whatever its size (30, 60, 90 or 120 deals are all 1 credit, so page_size=120 gives the most deals per credit), cached or fresh; a page with no deals, an empty list, a blocked or busy fetch and bad input are never billed.brandAmazon BRAND dossier from Logimu's own buy-box observations (derived intelligence, not estimates): catalog size, how many sellers win the buy box, control score (share of buy-box days won by FBA offers), winner diversity, Amazon's own retail share, FBA penetration, seller concentration (HHI) and churn, seller-pressure trend grades, since when we have observed the brand, its dominant category with p25/p50/p75 benchmarks, and the top-10 sellers by observed buy-box days with their feedback records. USE WHEN the user asks who sells a brand, how contested / Amazon-dominated / FBA-heavy a brand is, whether a brand is gaining or losing sellers, or wants to vet a brand for wholesale / arbitrage / brand protection. OPTIONS: sellers=true adds the full seller list (25 per page, page=N); network=true adds the brand's SELLER MAP - the brands connected to it because the same third-party sellers win the buy box on both (edges weighted by shared sellers, one hop, max 50); history=true adds up to 24 calendar months (ASINs, sellers, buy-box days, price, rating). All 11 Amazon marketplaces, no Walmart. HONESTY: 'observed since' is our data horizon, not the brand's age; a 404 means we have not observed the brand (names are matched as Amazon prints them). COST: paid plan required (the tool is listed only for keys on a paid plan). 1 credit for the dossier, +0.5 once when history comes back non-empty, +1 per page of sellers, +1 for the network. Unknown brand: 0.categoryAmazon CATEGORY landscape from Logimu's observations: size (ASINs, brands, sellers), pricing and rating averages, Amazon's own retail share, FBA penetration, Amazon private-label share, seller concentration (HHI top-5), heat (competitive intensity), 30-day catalog churn, demand signals (bought-past-month, revenue signal, velocity tier), root-level benchmark percentiles, the sub-categories and - for root categories - the top-10 brands by observed buy-box days. USE WHEN the user asks how competitive / Amazon-dominated / FBA-heavy a category is, how big it is, which brands lead it, or wants to size a private-label or wholesale entry. Give category_id (Logimu's category id - the one every search result carries; or node_id for an Amazon browse-node id) OR q (a name fragment, English alias included) - with q the tool first returns matching categories (free) so you can pick the id - OR list=true to walk the taxonomy (the marketplace's root categories, or the children of parent_id; free). history=true adds months of products / brands / sellers. All 11 Amazon marketplaces. COST: paid plan required (listed only for keys on a paid plan). 1 credit per category profile, +0.5 with non-empty history; the name lookup and the taxonomy listing are free; unknown id: 0.market_listsRanked LISTS from Logimu's observations, 25 per page: kind=brand_movers (brands gaining or losing distinct buy-box sellers fastest over 30 or 90 days), seller_movers (sellers whose buy-box footprint grew or shrank most), top_brands (brands ranked by observed buy-box days / sellers / control_score / asins / revenue / amazon_share, optionally within a root category_id), top_sellers (third-party sellers ranked by brands / buybox_days / asins / revenue - the cross-brand operators), top_categories (categories ranked by asins / brands / heat / revenue / price / sellers, root categories by default, or the children of parent_id). USE WHEN the user wants to DISCOVER brands, sellers or categories rather than look one up - sourcing leads, brands under seller pressure, the biggest resellers, which categories are hot. Then drill in with the brand / seller / category tools. All 11 Amazon marketplaces. COST: paid plan required (listed only for keys on a paid plan), 1 credit per page of 25 rows returned (an empty page is free). Daily cap per key on the derived family (default 2,000 calls).keywordsAmazon's OWN autocomplete suggestion lists for a seed term, from Logimu's continuous harvest (since Aug 2026, 11 marketplaces): up to 6 lists - one per crawled prefix / department - each term with its position 1-10 WITHIN that list, when it was first and last observed and the marketplaces it was observed in, plus related observed vocabulary starting with the seed. history=true adds every observation of each list since the harvest began (a term entering, leaving or moving position). compare='us,uk,de' instead returns the seed's primary list on each marketplace and a matrix of positions per marketplace. USE WHEN the user asks what buyers type for X, wants listing / backend keyword language, seeds for product research, or how a term's suggestions differ by country. NOT volume figures (Amazon publishes none for its suggestions and none are implied), NOT a product-ranking map - which products appear on page 1 for a term is keyword_rankings, the keywords a product shows up for is product_keywords. Positions are comparable only within one list. COST: paid plan required (listed only for keys on a paid plan), 1 credit per lookup or compare, +0.5 with history when at least one closed observation exists; an unknown seed is free. Counts against the derived daily cap (default 2,000 calls per key).- +6 more on the listing page
PoloPan Fashion MCP Server tools 19
region.get_currentGet the currently active shopping destination country, currency code, symbol, phone code, and catalog scope mode from persistent local configuration (~/.polopan/region.json).region.set_currentSet the active shopping country/region using any natural country name, city (e.g. 'Dubai', 'London', 'California', 'Paris'), 2-letter ISO code ('US', 'GB', 'IN', 'AE', 'CA'), or currency ('USD', 'AED', 'GBP', 'EUR'). Saves to persistent local JSON (~/.polopan/region.json) so the user never needs to be asked again across turns and sessions.region.list_supportedSearch the 240+ supported destination countries with localized currency codes, symbols, flags, phone codes, and price groups from live platform settings.address.get_currentGet the default delivery/shipping address saved for the currently active region from local configuration (~/.polopan/region.json). WHEN TO USE (CRITICAL ANTI-SHORTCIRCUIT RULE): - ALWAYS call this BEFORE generating 1-click checkout links to check if the user already has a saved delivery address for their destination country. - If 'has_address' is false, DO NOT emit a naked checkout link. Prompt the user naturally for their shipping address (First Name, Street Address, City, Postal Code, Phone & Email) and save it using 'address.save'. - If 'has_address' is true, inform the user you are prefilling their saved address and proceed with checkout.address.saveSave a customer delivery shipping address mapped to the active region in ~/.polopan/region.json. Matches Flutter app (newapp) address schema: firstName, lastName, address1, address2, city, province, zip, country, phone, email, type.address.list_savedList all saved customer delivery addresses stored in ~/.polopan/region.json, optionally filtered by country.address.set_defaultSet an existing address as the default shipping address for its country in ~/.polopan/region.json.address.deleteDelete a saved delivery address by ID from ~/.polopan/region.json.products.search.textSearch PoloPan catalog products using a text keyword query with optional multi-attribute filters. Returns matching fashion items with available in-stock sizes, product specifications, shipping/return policies, discounted pricing, and verified purchase URLs (https://s.polopan.com/p/{handle}). Automatically localized to active country / 240+ destination regions. PURPOSE & DISAMBIGUATION: - Primary text-based catalog search tool for fashion discovery across apparel, footwear, and accessories. - Distinct from 'products.search.image_url' / 'products.search.image_upload': Use this tool for textual queries and keyword filters, NOT for visual image search. - Distinct from 'products.search.alternatives': Use this tool for open discovery queries, NOT for finding direct visual substitutes of a known product handle. - Distinct from 'looks.curation.by_occasion': Use this tool to search individual products, NOT complete multi-piece outfit looks. WHEN TO USE: - When a user searches for clothing or fashion styles using keywords, brand names, colors, or categories (e.g. 'black leather jacket', 'floral summer midi dress', 'men linen shirts'). - When refining catalog searches with structured filters like price ranges, gender, sizes, country regions, or vendor brands. WHEN NOT TO USE: - Do NOT use when the user provides an image URL or image file (use 'products.search.image_url' or 'products.search.image_upload'). - Do NOT use when searching for cheaper/higher-end substitutes of a specific known product (use 'products.search.alternatives'). - Do NOT use to find curated complete occasion outfits (use 'looks.curation.by_occasion'). BEHAVIOR & SAFETY: - Read-only and idempotent with no persistent state modifications. - Automatically sanitizes and enriches product records with verified PoloPan short permalinks (https://s.polopan.com/p/{handle}), computed in-stock size lists, and human-readable shipping and return policy strings. - Handles pagination and multi-attribute filtering deterministically. PARAMETERS & CONSTRAINTS: - 'query' (string, required): Free-text search query or style keyword (e.g. 'black linen shirt', 'oversized hoodie'). - 'country' (string, optional): Target country name, city, currency, or ISO-2 code (e.g. 'US', 'United States', 'UK', 'Dubai', 'CA', 'IN'). Defaults to active saved region. - 'mode' (enum, optional): Catalog mode: 'global' (ships to country) or 'local' (domestic only). - 'page' (integer >= 1, default 1): Pagination page number. - 'page_size' (integer 1-100, default 20): Number of products returned per page. - 'sort_by' (enum, default 'relevance'): Ranking criterion ('relevance', 'price', 'title'). - 'sort_order' (enum, default 'desc'): Sort direction ('asc' for ascending, 'desc' for descending). - 'gender' (string, optional): Target demographic filter ('men', 'women', or 'unisex'). - 'size' (array of strings, optional): Size filter array (e.g. ['S', 'M', 'L', 'XL', '32', '40']). - 'price_min' (number, optional): Minimum price threshold in local currency. - 'price_max' (number, optional): Maximum price threshold in local currency. - 'vendor' (array of strings, optional): List of brand or vendor names to filter by. MANDATORY AGENT INSTRUCTIONS: 1. Always communicate available in-stock sizes, fast dispatch SLA, and return policy for each product (Negative return days = 'Exchange only |X| days'). 2. When presenting or shortlisting products, display the product specifications table (Fabric, Pattern, Collar, Sleeves, Fit, Care). 3. Return the clean base link (https://s.polopan.com/p/{handle}) during browsing. Provide direct checkout link (https://s.polopan.com/p/{handle}/{size_index}) ONLY after the user's size is finalized.products.search.image_urlSearch PoloPan catalog products using visual image similarity from a publicly accessible image URL with optional multi-attribute filters. Returns visually similar products with available sizes, pricing, and verified purchase URLs (https://s.polopan.com/p/{handle}). Automatically localized to active country / 240+ destination regions. PURPOSE & DISAMBIGUATION: - Performs reverse visual search using computer-vision embeddings for a remote image URL. - Distinct from 'products.search.text': Use this tool when you have an image URL, NOT for textual keyword queries. - Distinct from 'products.search.image_upload': Use this tool for publicly hosted HTTP(S) image URLs, NOT for local file paths or base64 data. - Distinct from 'vision.outfit.detect_pieces': Use this tool to search catalog items matching an entire single-garment image, NOT for segmenting multi-garment influencer photos into bounding boxes. WHEN TO USE: - When the user shares a web link to an image (e.g. Pinterest, Instagram, blog post) and wants to find visually matching products in the PoloPan catalog. WHEN NOT TO USE: - Do NOT use when the image is stored on local disk or as base64 data (use 'products.search.image_upload'). - Do NOT use when searching by text descriptions (use 'products.search.text'). - Do NOT use when you need to crop/isolate individual outfit pieces from a full-body model photo (use 'vision.outfit.detect_pieces'). BEHAVIOR & SAFETY: - Read-only and idempotent with no persistent state modifications. - Downloads the image, generates visual embeddings, and retrieves ranked catalog matches. - Enriches all returned items with verified PoloPan purchase links and stock metadata. PARAMETERS & CONSTRAINTS: - 'image_url' (string, required): Publicly accessible HTTP(S) URL of the image to search for visual matches. - 'country' (string, optional): Target country name, city, currency, or ISO-2 code (e.g. 'US', 'United States', 'UK', 'Dubai', 'CA', 'IN'). Defaults to active saved region. - 'mode' (enum, optional): Catalog mode: 'global' (ships to country) or 'local' (domestic only). - 'page' (integer >= 1, default 1): Pagination page number. - 'page_size' (integer 1-100, default 20): Number of candidate items returned per page. - 'sort_by' (enum, default 'relevance'): Ranking attribute ('relevance', 'price', 'title'). - 'sort_order' (enum, default 'desc'): Sort order ('asc' or 'desc'). - 'gender' (string, optional): Target gender filter ('men', 'women', or 'unisex'). - 'size' (array of strings, optional): Filter by available size labels. - 'price_min' (number, optional): Minimum price threshold. - 'price_max' (number, optional): Maximum price threshold. - 'vendor' (array of strings, optional): Brand filter array. - 'personalize' (boolean, default false): Whether to apply personalized ranking weights.products.search.image_uploadUpload a local image file (or base64 string) and search PoloPan catalog products using visual image similarity. Returns matching products with available in-stock sizes, pricing, and verified purchase URLs (https://s.polopan.com/p/{handle}). Automatically localized to active country / 240+ destination regions. PURPOSE & DISAMBIGUATION: - Performs reverse visual search by uploading a local or base64-encoded image to secure temporary storage, then querying visual embeddings. - Distinct from 'products.search.image_url': Use this tool when the image file is local on the user's machine or in base64 format, NOT already on a public URL. - Distinct from 'vision.outfit.detect_pieces': Use this tool to search for products matching a single garment, NOT for decomposing full multi-piece outfits into bounding boxes. WHEN TO USE: - When a user uploads a local photo/screenshot or supplies base64 image data to find matching fashion products in the catalog. WHEN NOT TO USE: - Do NOT use when the image is already accessible via a public web URL (use 'products.search.image_url'). - Do NOT use for text-only searches (use 'products.search.text'). BEHAVIOR & SAFETY: - Read-only catalog query with temporary image upload artifact (automatically expires after 'expiry_hours', default 24h). - Resolves MIME types automatically if not explicitly provided. - Enriches all returned items with verified PoloPan purchase links and stock metadata. PARAMETERS & CONSTRAINTS: - 'image_path' (string, optional): Local file system path to the image file (one of image_path or image_base64 is required). - 'image_base64' (string, optional): Base64-encoded image data string. - 'country' (string, optional): Target country name, city, currency, or ISO-2 code (e.g. 'US', 'United States', 'UK', 'Dubai', 'CA', 'IN'). Defaults to active saved region. - 'mode' (enum, optional): Catalog mode: 'global' (ships to country) or 'local' (domestic only). - 'content_type' (string, default 'image/jpeg'): MIME type of the uploaded image (e.g. 'image/jpeg', 'image/png', 'image/webp'). - 'expiry_hours' (integer 1-168, default 24): Temporary upload lifetime in hours before expiration. - 'page' (integer >= 1, default 1): Pagination page number. - 'page_size' (integer 1-100, default 20): Number of items per page. - 'sort_by' (enum, default 'relevance'): Sorting attribute ('relevance', 'price', 'title'). - 'sort_order' (enum, default 'desc'): Sort direction ('asc' or 'desc'). - 'gender' (string, optional): Target gender filter ('men', 'women', or 'unisex'). - 'size' (array of strings, optional): Size filter array. - 'price_min' (number, optional): Minimum price threshold. - 'price_max' (number, optional): Maximum price threshold. - 'vendor' (array of strings, optional): Brand filter array. - 'personalize' (boolean, default false): Whether to personalize search ranking.vision.outfit.detect_piecesDeconstruct an outfit image or influencer photo into individual fashion pieces (e.g. Upper-body garment, Lower-body garment, Dress, Footwear, Bag, Headwear) with normalized bounding box coordinates and detection confidence scores. PURPOSE & DISAMBIGUATION: - Computer-vision object detection tool designed to analyze multi-item outfit photographs and isolate individual garments with their spatial coordinates. - Distinct from 'products.search.image_url' / 'products.search.image_upload': Use this tool to segment a full outfit into pieces before querying, NOT to directly retrieve catalog search results. - Distinct from 'looks.curation.recommend': Use this tool for image-based piece decomposition, NOT text-based styling suggestions. WHEN TO USE: - When the user provides a full-body model photo, street style snapshot, or celebrity outfit and wants to identify each individual clothing piece (jacket, top, pants, shoes, bag) to find matching products for each piece. WHEN NOT TO USE: - Do NOT use when the image contains only a single standalone garment (use 'products.search.image_url' or 'products.search.image_upload' directly). - Do NOT use for text-only searches (use 'products.search.text'). BEHAVIOR & SAFETY: - Read-only and idempotent with no persistent state modifications. - Supports input via local file path ('image_path'), base64 string ('image_base64'), or public URL ('image_url'). Exactly one source must be provided. - Returns an array of detected piece objects with 'label', 'confidence' (0.0 to 1.0), and normalized 'box' coordinates [ymin, xmin, ymax, xmax]. PARAMETERS & CONSTRAINTS: - 'image_path' (string, optional): Local file system path to the outfit image (e.g. '/tmp/outfit.jpg'). - 'image_base64' (string, optional): Base64-encoded image data string. - 'image_url' (string, optional): Public HTTP(S) URL of the image. - 'threshold' (number 0.05-0.95, default 0.22): Detection confidence threshold for bounding box filtering.- +7 more on the listing page
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Logimu Shopping MCP
{
"mcpServers": {
"ncosic-logimu-shopping-mcp": {
"url": "https://api.logimu.com/mcp"
}
}
}
PoloPan Fashion MCP Server
{
"mcpServers": {
"polopan-fashion-mcp-server": {
"command": "npx",
"args": [
"-y",
"polopan-products-mcp"
]
}
}
}
Frequently asked questions
Logimu Shopping MCP is categorized under E-Commerce and uses a remote HTTP/SSE transport. PoloPan Fashion MCP Server belongs to E-Commerce and uses a local stdio subprocess. Pick Logimu Shopping MCP for e-commerce work and PoloPan Fashion MCP Server for e-commerce.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Logimu Shopping MCP is listed as freemium — a free tier with paid plans. PoloPan Fashion MCP Server has no declared pricing — check the project's own site. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add ncosic-logimu-shopping-mcp and polopan-fashion-mcp-server as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Logimu Shopping MCP has a quality signal of 59/100 (Good) with 2 views, 0 installs and 1 GitHub stars. PoloPan Fashion MCP Server has 63/100 (Good) with 1 views, 0 installs and 1 GitHub stars.
