Retail pricing and availability monitoring
Adidasエンドポイントを使えば、「Retail pricing and availability monitoring」をドキュメント化された入力とJSONレスポンスによる再現可能なAPIリクエストに変えられます。
Turn Adidas.com's public storefront into structured retail data — keyword product search or category browse with real pagination and sort options, full product detail per SKU (pricing, description, images, every purchasable size), and a store locator with per-store detail (hours, phone, in-store services), all as normalized JSON. Credential-free.
Search and browse Adidas products, get full product detail, and find nearby Adidas stores as structured JSON.
エンドポイントファミリー
5
ドキュメント化されたパラメータ
24
サンプル数
7
ライブカタログスナップショット
稼働エンドポイント
7
メソッド
GET
必須パラメータ
14
スキーマ参照
7
{
"platform": "Adidas",
"endpoint": "adidas-search",
"method": "GET",
"path": "/adidas/search",
"auth": "apiKey"
}ユースケース
Search and browse Adidas products, get full product detail, and find nearby Adidas stores as structured JSON.
Adidasエンドポイントを使えば、「Retail pricing and availability monitoring」をドキュメント化された入力とJSONレスポンスによる再現可能なAPIリクエストに変えられます。
Adidasエンドポイントを使えば、「Product catalog research」をドキュメント化された入力とJSONレスポンスによる再現可能なAPIリクエストに変えられます。
Adidasエンドポイントを使えば、「Store locator and footprint mapping」をドキュメント化された入力とJSONレスポンスによる再現可能なAPIリクエストに変えられます。
マネージド実行
以下の数値はすべて、稼働中のAdidasエンドポイントカタログから取得しています（エンドポイント7件、ドキュメント化されたリクエストパラメータ24個、公開レスポンススキーマ7件）。DocsとPlaygroundが参照しているカタログと同じものです。
Adidasのエンドポイントは7件、5つのリクエストファミリーに分類されています（Product、Search、Storeほか2件）。
これらのAdidasエンドポイントには24個のリクエストパラメータがドキュメント化されており、うち14個が必須です。統合コードを書く前に入力仕様をすべて確認できます。
7件のAdidasエンドポイントのうち7件が実際のサンプルレスポンスを、7件がドキュメント化されたレスポンススキーマを備えています。最初のリクエストの前に実データの形に合わせて実装できます。
Adidasのエンドポイントは成功時のスキーマに加えてエラーレスポンス（400、404、429、503）もドキュメント化しています。ブロック、レート制限、レコード欠損は空データではなく型付きのエラーとして返ります。
Adidasのエンドポイントは7個のホスト型MCPツールとして提供されており、エージェントは同じパラメータと同じJSON契約のまま、追加の実装なしに同じルートを呼び出せます。
カバレッジマップ
これらのカードは稼働中のエンドポイントカタログから生成されるため、このページはDocsとPlaygroundが使うAPIサーフェスをそのまま反映します。
/adidas/product
/adidas/search
/adidas/store
/adidas/stores
/adidas/suggest
エンドポイントカタログ
/adidas/searchSearches Adidas.com product listings by keyword, or browses a category listing by taxonomy slug, with real pagination and sort options. Exactly one of query or category is required. Returns normalized product summaries (title, price, rating, images, color variants) plus facet filter groups, sort options, and (for category browse) a breadcrumb trail. Keyword search is best-effort relevance, not a guaranteed match: an obscure keyword returns whatever Adidas's own search index surfaces. A genuinely empty keyword search returns an empty product list, and requesting a page beyond the available result pages (or an unknown category) returns a not-found error. Category values are the path segment after /us/ in an Adidas category URL (e.g. women-athletic_sneakers); they can also be read from the url fields of a search/category response's own filters and breadcrumbs. Facets are applied by composing them into the category slug rather than by a separate parameter: each filters[].values[].slug is a token you splice into the category value (e.g. category=women-black-athletic_sneakers applies the Color=Black facet, and category=women-athletic_sneakers-prime applies Shipping=PRIME). Use filters[].key (the facet's stable name, e.g. searchcolor) rather than filters[].id, which is an opaque per-deployment UUID that cannot be used to build a request. Note the token's position within the slug varies by facet, so compose from a slug you have seen rather than assuming a fixed order.
レスポンスに関する注記
- Keyword search is best-effort relevance, not a guaranteed keyword match: an obscure `query` returns whatever Adidas's own search index surfaces (there is no reliable signal to distinguish a true match from Adidas's fallback results). - Requesting a `page` beyond the available result pages — or an unknown `category` — returns a not-found error rather than an empty page. - `products[].id` is the Adidas SKU to pass as `product_id` to `adidas-product` for full product-detail data. - `products[]` carries rich per-SKU metadata: `division` (brand line, e.g. Originals/Sportswear/Performance), `category` (e.g. shoes/clothing), `sport[]`, `generic_product_type[]`, `surface[]`, `available_sizes[]`, `color_variations[]`, and `orderable`/`preorderable`/`personalizable` availability flags. - `filters[]` are the facet groups Adidas's own search exposes (e.g. Shipping, Width, Brand, Price), each with `values[]` carrying a live `count`. `sort_options[]` lists the four sort orders with the currently selected one flagged. `breadcrumbs[]` is populated only for category browse (empty for keyword search). - **Applying a facet.** There is no separate filter parameter: facets are applied by composing them into the `category` value. Each `filters[].values[].slug` is the token to splice in — `category=women-black-athletic_sneakers` applies Color=Black to `women-athletic_sneakers`, and `category=women-athletic_sneakers-prime` applies Shipping=PRIME. The resulting `total_products` matches the facet value's advertised `count`. The token's **position within the slug varies by facet** (Color sits in the middle, Shipping at the end), so compose from a slug you have actually seen rather than assuming a fixed order, and read `filters[].values[].selected` to see what is already applied. - Use `filters[].key` (the facet's stable name, e.g. `searchcolor`, `multi_age_gender_en_us`) when reasoning about facets. `filters[].id` is Adidas's opaque per-deployment UUID — it is not stable across categories and cannot be used to build a request. - The facet set is **category-dependent**: `women-athletic_sneakers` exposes 10 groups, `men-clothing` 15, and some categories add a Size group that others omit. Always read the facets from the response rather than assuming a fixed list. - `total_pages` is derived from `total_products` and the 48-per-page size.
MCPツール adidas_search
/adidas/productReturns normalized product-detail data for one Adidas SKU: name, brand, category, description, pricing (current/standard/sale), images, and every purchasable size variant. product_id is the Adidas SKU (e.g. JI0397), taken from a search result's products[].id field or the trailing segment of an Adidas product page URL. An unknown product_id returns a not-found error.
レスポンスに関する注記
- An unknown `product_id` returns a not-found error. - `price` fields are in the listing's currency (US dollars for the US storefront); `current_price` is the effective price (sale price when on sale, otherwise standard), `standard_price` is the list price, `sale_price` is the sale price, and `discount_text` is the site's own discount label (e.g. `-40%`). - `images[]` is the product's detail view list (each with a `url`, `view`, and `sort_order`); `variations[]` lists every purchasable size (`sku`, `size`, `split_size`, `gtin`). - `breadcrumbs[]` is the category trail (e.g. Originals > Shoes); `sport[]` lists the sport/activity tags.
MCPツール adidas_product
/adidas/product/reviewsReturns one page of customer reviews for an Adidas product model, plus the model's rating summary: overall rating, star histogram, percentage of reviewers who recommend it, per-attribute averages (Size, Width, Comfort, Quality with their own scale labels), and Adidas's AI-generated review digest when one exists. Each review carries the rating, headline, body, author nickname, purchased colorway, helpful/not-helpful vote counts, badges, customer photos, and submission time. model_number is the Adidas model number (e.g. SAMBAU2312) -- NOT the SKU: take it from an adidas_search result's products[].model_number field, which is a different value from products[].id. Reviews are returned 10 per page. Reviews are scoped to review text written in the requested locale's language, and most of the US catalog's reviews are English, so a non-English locale commonly returns rating statistics and a localized summary with an empty reviews list. A model with no reviews -- including a well-formed but unrecognized model_number -- returns an empty reviews list rather than an error, because Adidas answers 200 with a zero count rather than 404.
レスポンスに関する注記
- `total_reviews` counts the reviews matching this request's filters, so it shrinks when `rating` is set. `rating_count` is the model's unfiltered review count from the rating summary and stays constant. - `rating_histogram` is ascending: index 0 is the 1-star count, index 4 the 5-star count. - `secondary_ratings[]` are the per-attribute scales Adidas collects (`Size`, `Width`, `Comfort`, `Quality`), each with its own `min_label`/`mid_label`/`max_label` wording in the requested locale. A scale with no midpoint wording returns an empty `mid_label`. - `summary` is Adidas's AI-generated digest of the model's reviews. It is present only when the upstream has generated one, is localized by `locale`, and carries the upstream's own `disclaimer`, which should be shown alongside the text. - Reviews are scoped to review text written in the requested locale's language. Most of the US catalog's reviews are English, so a non-English `locale` commonly returns the rating statistics and a localized `summary` with an empty `reviews` list. - A model with no reviews — including a well-formed but unrecognized `model_number` — returns an empty `reviews` list with `total_reviews: 0` rather than an error, because Adidas answers `200` with a zero count rather than `404`. - `recommended` is omitted when the upstream did not record a recommendation for that review. `color` (the purchased colorway), `badges`, `photos`, and `responses` (brand replies) are omitted when empty. - `source_url` is the reviews request; `ratings_source_url` is the sibling rating-summary request the same call makes.
MCPツール adidas_product_reviews
/adidas/product/review-topicsReturns the topics an Adidas product model's customer reviews can be filtered by -- the "filter by topic" chips the product page shows, commonly satisfaction, comfort, color, purchase, fit, appearance, quality and style. Feed a topics[].topic value back to /adidas/product/reviews as its topic parameter to return only reviews about that aspect. The topic vocabulary is per model, not a fixed list: a shoe exposes fit and comfort topics that an accessory does not, so read it per model rather than hard-coding it. model_number is the Adidas model number (e.g. SAMBAU2312) -- NOT the SKU: take it from an adidas_search result's products[].model_number field. A model with no reviews, including a well-formed but unrecognized model_number, returns an empty topics list rather than an error. Note the label field is a display form of topic, not translated text: Adidas returns the same English values for every locale on this route.
レスポンスに関する注記
- The topic vocabulary is **per model**, not a fixed list — a shoe exposes fit and comfort topics an accessory does not. Read it per model rather than hard-coding it. Commonly seen values are `satisfaction`, `comfort`, `color`, `purchase`, `fit`, `appearance`, `quality` and `style`. - `label` is a **display form of `topic`, not translated text**. Adidas returns the same English values for every `locale` on this route, unlike `/adidas/product/reviews`, where the locale genuinely translates the rating-scale labels. - A model with no reviews — including a well-formed but unrecognized `model_number` — returns an empty `topics` list rather than an error, because the upstream answers `200` with an empty list rather than `404`. - Passing a `topic` that is not in this list to `/adidas/product/reviews` returns `400` naming the model's valid topics, rather than silently returning zero reviews.
MCPツール adidas_product_review_topics
/adidas/suggestReturns the top matching products for a partial query, the same search-as-you-type preview Adidas's own search box shows. Adidas has no separate term-autocomplete index, so each suggestion is a matching product (id, title, url, image, price) rather than a completed search phrase. Best-effort relevance: an obscure query returns whatever Adidas's own search surfaces.
レスポンスに関する注記
- Adidas has no separate term-autocomplete index, so each `suggestions[]` entry is a matching product (`id`, `title`, `url`, `image_url`, `price`) rather than a completed search phrase. - Best-effort relevance: an obscure `query` returns whatever Adidas's own search surfaces, not a guaranteed prefix match. - Each suggestion's `id` is the SKU to pass as `product_id` to `adidas-product` for full detail.
MCPツール adidas_suggest
/adidas/storesReturns Adidas physical retail stores nearest to a coordinate, sourced from Adidas's own store-finder API: name, address, phone, coordinates, distance in miles, opening hours, and in-store feature flags. lat and lng are both required. Adidas's upstream ignores a caller-supplied radius and returns the nearest ~20 stores ordered by distance. A location with no stores returns an empty list rather than an error.
レスポンスに関する注記
- Adidas's upstream ignores a caller-supplied radius and returns at most 20 stores, ordered nearest-first. Each store carries its own `distance_miles`. `total` is the number of stores found near the coordinate, and every one of them is in this single response. - A location with no stores returns an empty `stores` list rather than an error. - `opening_hours[]` lists all seven days (monday..sunday) with `start`/`end` "HH:MM" times, or `closed: true` for a day the store is not open. - `features[]` are Adidas's in-store service flags (e.g. `clickCollect`, `wifi`, `runGenie`).
MCPツール adidas_stores
/adidas/storeReturns normalized detail for one Adidas retail store: name, status, phone, description, full address, coordinates, opening hours, and in-store services (e.g. Click and Collect, Free Wi-Fi). store_id is the numeric Adidas store id, taken from an adidas-stores response's stores[].id field. An unknown store_id returns a not-found error.
レスポンスに関する注記
- An unknown `store_id` returns a not-found error. - `status` is the store's current state (e.g. `OPEN`); `opening_hours[]` lists each day with `status` and `from`/`to` "HH:MM" times. - `services[]` lists in-store services (e.g. Click and Collect, Free Wi-Fi) with Adidas's own `type` and display `name`.
MCPツール adidas_store
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Adidasのスクレイピング方法
Crawlora's Adidas endpoints return product search/browse, product detail, and store locator data as normalized JSON with one API key — no Adidas account required.
Send a keyword to /adidas/search, or a category taxonomy slug to browse a listing instead, both with real pagination and sort options — normalized product summaries, facet filters, and sort options.
Pass a product_id (SKU) from a search result's products[].id field to /adidas/product for pricing, description, images, and every purchasable size variant.
Pass lat/lng to /adidas/stores for the nearest Adidas retail stores, nearest-first, with hours, phone, and in-store service flags.
Pass a store_id from a /adidas/stores result to /adidas/store for that store's full detail — status, description, opening hours, and services.
Send a partial query to /adidas/suggest for the same search-as-you-type product preview Adidas's own search box shows.
Pass a model_number — the products[].model_number field on a search result, which is not the same value as products[].id — to /adidas/product/reviews for a page of that model's customer reviews, its rating summary, and Adidas's own AI-generated review digest. Call /adidas/product/review-topics first for the "filter by topic" chips the product page offers, then feed a topic value back to the reviews endpoint to read only the reviews about that aspect.
FAQ
Send a keyword to Crawlora's /adidas/search endpoint and get normalized product summaries — pricing, sizes, color variants — as structured JSON, no Adidas account required.
Yes — pass a category taxonomy slug to /adidas/search instead of a query for the same paginated listing shape, including a breadcrumb trail.
Pass latitude and longitude to /adidas/stores for the nearest stores, each with its own distance, hours, phone, and in-store service flags.
Yes — /adidas/product/review-topics returns the same "filter by topic" chips the product page shows, and passing one of those topic values to /adidas/product/reviews narrows the page to reviews about that aspect. Both take the model_number, not the SKU: take it from a search result's products[].model_number field, which is a different value from products[].id.
No — an obscure query returns whatever Adidas's own search index surfaces as a best-effort relevance match, not a guaranteed match, the same caveat as other major retail search APIs.