Apiguru Amazon Data vs DataDive
A side-by-side look at the Apiguru Amazon Data and DataDive MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Apiguru Amazon DataE-Commerce · Local subprocess (stdio)Live Amazon product, review, search, deal, offer/stock and seller data across 20 marketplaces.64GoodAuth: no auth requiredVerdict: choose Apiguru Amazon Data for e-commerce work as a local process; choose DataDive 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 Apiguru Amazon Data when
- You need dedicated capabilities in the E-Commerce domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: No auth required.
- You want tools like
product_details, product_reviews, search.
Choose DataDive when
- You need dedicated capabilities in the E-Commerce domain.
- You prefer a local stdio subprocess transport.
- You have the keys it needs:
DATADIVE_API_KEY. - You want tools like
list_niches, get_niche_keywords, list_keyword_exclusions.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | Apiguru Amazon Data | |
|---|---|---|
| Category | E-Commerce | E-Commerce |
| Quality signal | 64/100 · Good | 63/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | No auth required | Not declared |
| Pricing | Freemium | Not declared |
| Required env vars | None listed | DATADIVE_API_KEY |
| Verified owner | Community listing | Community listing |
| GitHub stars | 0 | 1 |
| Directory activity | 2 views 0 installs 0 upvotes | 1 views 0 installs 0 upvotes |
| Tools | 12 | 43 |
| Full listing | View Apiguru Amazon Data | View DataDive |
Tools & capabilities
Apiguru Amazon Data tools 12
product_detailsUse for one product's current listing. Not for several ASINs (product_details_batch), seller offers or stock (offers_stock), or review text (product_reviews). Fetches the complete product record for one ASIN on one marketplace: title, price, star rating, rating count, images, description, feature bullets, variations and category. Price: $0.003 per call. 404 means the ASIN is absent from that marketplace and IS billed. 503 is a temporary failure on our side and is NOT billed - retry. Bullet points and specs are what Amazon shows for the listing; on multi-variant listings they can describe the product family rather than the exact variant. A null field means Amazon did not show it. Keys of `product_information`, `tech_specs` and the other specification tables are trimmed: Amazon prints some rows twice, once with a trailing space ("Package Dimensions "), and the copies are merged under the clean name; when they differ the listing's own value wins (the second copy of "ASIN" on a variant often names the parent).product_reviewsUse for what customers say about one product. Not for its full review history (Amazon serves full and star-filtered review lists only to signed-in accounts) or for seller feedback (seller_reviews). Returns the overall rating, rating count, review_histogram (percent of all ratings per star), Amazon's 'customers say' AI summary where that marketplace shows one, and the reviews on the product page (typically up to 8 from this marketplace and 5 from other countries), each with rating, review_date, review_country, from_this_marketplace, verified flag and helpful_votes; from_rating/to_rating keep a star window. Price: $0.003 per call. Same 404-billed / 503-not-billed semantics as product_details. No paging or sort: the reviews are those on the product page; review_coverage says how many and from where. from_rating/to_rating filter them. review_histogram (here and on product_details) gives the share of every rating. customers_say is null where Amazon shows no summary (several marketplaces never do); customers_say_note then says so.searchUse to find products by keyword and filters. Rows are result snippets; for the authoritative record of one ASIN call product_details. Search Amazon products by keyword. Filters: page, sort_by, category_id (browse node), min_price / max_price (decimals), product_condition (NEW / USED / RENEWED), brand, seller_id, today_deals and deal_type (coupons, all_discounts, buy_more_save_more). Every answer carries filters_applied, filters_ignored (with the reason) and available_filters for that marketplace. Price: $0.003 per call. Blank values and the literal string 'null' are treated as unset. Invalid page, sort_by, price, product_condition or deal_type is a free 400 naming the parameter and the allowed values. Condition and deal refinements are per marketplace; a marketplace that lacks one gets the unfiltered feed plus an entry under filters_ignored, never a silent empty page. `product_num_ratings` is an integer; `offers_count` is the "N offers" count a result card shows, an integer, and null when the card shows none (most cards; it used to default to 1); `product_brand` is the brand line Amazon prints above some titles (fashion and many branded listings) and null on cards without one -- product_details has `brand_name` for every ASIN; `product_star_rating`, `product_price` and `product_original_price` are decimal strings; a null field means Amazon did not show it. `is_prime` is true when the result carries a Prime badge or its delivery line offers Prime delivery. `metadata.reachable_pages` says how far `page` can go: Amazon serves at most 7 pages of a query even when `metadata.total_pages` counts more (then `pagination_note` says how to split the query). `_links.next` is the next page while there is one; a page past the end is a free 400 `page_out_of_range`. A full page is up to 48 results and about 54 KB; the tool returns the first 10 as light rows by default and the answer carries `_truncated`, `_omitted_fields`, `_projection` and `_notes`. filters_applied echoes the effective sort_by (RELEVANCE when none was sent). A BEST_SELLERS ordering is Amazon's query-scoped popularity, not a category rank: a row's `badges` / `is_best_seller` are what the result card showed for this query, and an ASIN that is #1 in its subcategory can carry no badge here while product_details reports best_seller=true with the rank. For a rank claim, use product_details or best_sellers. An empty `products` list is served as success only when Amazon itself reports 0 results (metadata.total is 0 and `hint` says so). Anything else that is not a usable result is an unbilled, retryable 503 with code upstream_unavailable.product_details_batchUse to enrich a list of ASINs in one call; compare the ASINs in `results` with the ones you sent. Batch variant of product_details. Accepts a comma-separated ASIN list, deduplicates it, and fetches all of them concurrently. Far cheaper and faster than N single calls. Price: $0.0024 per item (max 20). Billed per ASIN processed, including ones that come back not-found. More than 20 ASINs returns 413. Bullet points and specs are what Amazon shows for the listing; on multi-variant listings they can describe the product family rather than the exact variant. A null field means Amazon did not show it. ASINs Amazon has no record for on that marketplace are listed in not_returned (billed, like a not-found product_details call; retrying will not help). The batch record comes from Amazon's app, not the product page: price, rating, availability, images, bullets and variants, but no best-seller ranks, no category path and no merchant (seller) id. For those call product_details per ASIN. On an API key both cost one request per ASIN, so the batch saves calls and time, not money; keyless (x402) it is also cheaper per item.offers_stockUse to compare sellers' current offers and see who holds the buy box, and, with check_inventory, how many units a buyer can add to the cart now. The stock number is not a sales estimate. Not for the listing's own details (product_details). Returns the current offer list per ASIN (seller, price, condition, buy-box winner) and, optionally, the actual purchasable stock quantity. Price: $0.0045 per item (max 10). Billed per lookup, which is more than one per ASIN when check_inventory is true. offers_count=winner returns only the offer flagged is_buybox_winner (offers_total keeps the full count); it used to scope only the inventory check and return every offer. /scrape is a legacy alias for the same handler. When Amazon will not return the offer list for an ASIN, the result carries source=product_page: the featured (buy-box) offer read from the product page, offers_on_amazon (how many offers Amazon says exist) and a note; other sellers' offers and stock are then not included. With check_inventory, a cart check that cannot read a quantity is retried once on another session; one that still fails says so on the offer, inventory_response {"error": "cart check failed", "code": "cart_check_failed", "retryable": true}, is not billed, and never reads as a quantity of 0. Each offer carries `ships_from` (the "Ships from" name: Amazon for FBA, else the seller), `delivery_message` (the offer's own delivery promise, e.g. "FREE delivery Friday, October 16 on orders shipped by Amazon over $35") and `delivery_message_secondary` (the "Or fastest delivery ..." line), null when the offer shows none. `delivery` is the same value as `ships_from`, kept for existing integrations; it never held delivery information. A row for an ASIN that could not be answered carries `error`, `code`, `http_status`, `billed`, `retryable` and `next_step`, like a top-level error: `upstream_unavailable` (not billed, retry that ASIN), `no_offers` ("No offers found": the offer list holds no offer that can be bought as is -- customizable listings have none; billed like a 404) or `asin_redirected` (Amazon answers the ASIN with another listing's offers; billed like a 404). On agent.apiguru.app the answer also carries `success` and `_links` (self, and `details_url` per row); `billable_requests_count` is there on your own key only.best_sellersUse for what sells best in a department right now. The rank is Amazon's, not a sales figure. Best-seller rankings for a department of one marketplace, 50 per page. Every answer carries the department it resolved to and how (category_resolution: by slug, name or a fragment of a name, with a hint when a fragment such as 'shoes' landed on the whole 'Clothing, Shoes & Jewelry' department, or 'subcategory' when a word such as 'camera' named a subcategory), available_categories (that marketplace's departments with slugs; MCP: with compact=false) and available_subcategories (the children of the node shown, with the ids subcategory_code takes). On amazon.com subcategory_code also takes any browse node id at any depth, or a name resolved under the department ("women's shoes", "mules & clogs"); category.subcategory_path gives the node's full path and category.heading the page's own title line. Price: $0.003 per call. No required parameters - calling it bare returns US appliances page 1. `category` accepts a slug, a display name, one of the older US department names, or a unique fragment; category_resolution.via says which, and a fragment match adds a hint naming the subcategories that carry the word, with ids. On amazon.com the whole browse tree is known: subcategory_code takes any node id or a name at any depth, subcategory_name and subcategory_path are filled without a fetch, and available_subcategories lists the node's real children (empty on a leaf). Other marketplaces name only what their navigation showed. page is capped at 5 (a 400 beyond, not a 500). `rank` is the position within the requested list on this page. Rows are the same for every marketplace; only the department vocabulary differs, and the answer carries it.dealsUse for items currently promoted in Amazon's deals feed. Not for one product's price (product_details); for a brand by name use search with brand= and today_deals=true. Returns the current Amazon deals feed: ASIN, title, deal price, list price, discount, deal badge, start/end time and product links. Filter by department (categories), brand id (brands), rating cut-off, price bounds, minimum discount and Prime program. Every answer carries available_filters (the category and brand ids this marketplace accepts, with names), filters_applied / filters_ignored (what took effect) and next_offset (the next page, null when the feed ends). Price: $0.003 per call. Filters are by id: categories takes a department id or name, brands takes brand ids only; available_filters in every answer lists both with names, and filters_applied / filters_ignored report what Amazon honoured. A page is 30 rows; page with offset=next_offset (null when exhausted); total_count caps at 500. min_price, max_price, min_discount and max_discount are applied to the rows after retrieval, scanning up to 3 feed pages per call, so a page can hold fewer than 30 rows and total_count does not reflect them. An empty answer carries a hint saying why. Deal prices expire: check deal_ends_at. The older price_range and discount_range parameters are still accepted, as buckets (1-5 = under 25 / 25-50 / 50-100 / 100-200 / 200 and up; 1-4 = 10 / 25 / 50 / 70 percent off or more) or as bands such as 25-50 and 70+.seller_profile_batchUse to vet sellers: name, rating and feedback counts as Amazon displays them. Returns the storefront profile for each seller id: business name, rating, feedback counts, address and marketplace presence. Price: $0.0036 per item (max 10). Seller ID validation is all-or-nothing: one malformed id rejects the entire request with 400. Every row in results is an object with `status`: `ok` (the profile), `not_found` (Amazon has no page for that id on this marketplace; billed, like a 404) or `unavailable` (could not be retrieved right now; NOT billed on the keyed path, `retryable: true`). A row is never null. billable_requests_count counts ok + not_found rows; on the pay-per-call rail the per-item quote is settled up front, so retry `unavailable` ids in a separate call rather than expecting a partial refund. On agent.apiguru.app the answer also carries `success` and `_links.self`; `billable_requests_count` is there on your own key only.seller_productsUse to list one seller's storefront. Products listed by a seller: a storefront search. Takes the same filters as search -- query, page, sort_by, category_id, min_price / max_price, product_condition, brand, today_deals, deal_type -- and answers with filters_applied, filters_ignored and available_filters like search does. Price: $0.003 per call. A malformed seller_id is a free 400, as on seller_profile_batch. metadata.total_pages counts every page of the result (48 rows a page), but Amazon serves at most 7 pages of one query: metadata.reachable_pages is how far page can go, and when it is less, pagination_note says how to see the rest (min_price/max_price bands or category_id). _links.next is the next page while there is one; a page past the end is a free 400 page_out_of_range. Invalid sort_by, price, product_condition or deal_type is a free 400 that lists the allowed values. No single sort order lists a whole storefront: Amazon's relevance order (the default) can count far fewer products than the store holds and repeats products across pages, and other orders include products it leaves out (measured on one store: relevance 321, NEWEST 1,898; on another, relevance and NEWEST shared 90 of 161 products). Page 1 of a storefront without `query` carries `coverage_note` with the recipe: page through sort_by=RELEVANCE and sort_by=NEWEST, split a store larger than 336 products into min_price/max_price bands, dedupe by asin. An empty storefront carries `note`: Amazon reports 0 products for a seller with no listings on that marketplace and for an id that is not a seller alike, so check it with seller_profile_batch (`_links.seller_profile`). Rows have the search row shape: `offers_count` is null unless the card shows a count, and `product_brand` is null on most storefront cards (Amazon prints no brand line there; product_details has `brand_name`).seller_reviewsUse for a seller's customer feedback (service, shipping). Not for product reviews (product_reviews). Returns paginated seller feedback, optionally filtered to a star-rating window. Price: $0.003 per call. from_rating and to_rating are optional; omit both for unfiltered feedback. A page holds 5 reviews and the answer carries current_page and has_next_page; Amazon exposes no total, so page until has_next_page is false (up to page 100).list_capabilitiesList every Apiguru endpoint with its price, required parameters and supported marketplaces, plus your current access mode, cache and session budget. Free: answers locally with no network request and no charge. Call this before paid tools if you need to plan.send_feedbackReport a bug, ask for a field, or say what would make this API more useful. Free: never billed, no API key needed. Prefer GitHub if you have an account -- an issue at https://github.com/apiguru-app/agent-kit/issues gets a reply on the thread, this wall does not. Use this tool when you have no GitHub account or nothing to attach one to. message: what happened or what you want. Be specific: the tool, the parameters, the field, what you expected, what you got, and the request_id from the answer. One issue per entry -- a five-point list cannot be closed point by point. category: bug | wish | praise | question | other endpoint: which tool or path it is about, e.g. "search". agent: what you are, e.g. "acme-pricing-bot/2.1". Optional. contact: a GitHub handle or email if you want a reply. Shown publicly on the wall. Optional.
DataDive tools 43
list_nichesUse this first when the user asks about their niches, or to find a `nicheId` for use with `get_niche_keywords`, `get_niche_competitors`, or `get_ranking_juice`. Retrieves a paginated list of Niches, newest dive first. Each Niche represents a market segment or product category being tracked. Returns nicheId, heroKeyword, nicheLabel, marketplace (com/uk/de/...), and latestResearchDate per niche, plus pagination metadata (currentPage, pageSize, total, lastPage, hasNext, hasPrev). Narrow with `searchText` (label/keyword) or `searchAsin` (a competitor ASIN) instead of scanning pages; a page holds at most 50 niches, so the full list of a large account needs several calls.get_niche_keywordsUse this when the user asks about keywords, search terms, or search volume for a niche. Retrieves the master keyword list for the specified Niche — the relevant search terms monitored for ranking and performance metrics. For each keyword, returns search volume, relevancy (numeric score or "Outlier"), and competitor ASIN organic ranks (asinRanks: { ASIN -> rank | null }), plus `origin`: `mkl` (found by the dive), `manual` (added by hand with `add_niche_keywords`), `outlier` / `residue` (moved in from those lists). Only `manual`, `outlier` and `residue` keywords can be taken out with `remove_niche_keywords`. Keywords hidden by the niche's keyword exclusions are not listed; see `list_keyword_exclusions`. Also returns the latestResearchDate.list_keyword_exclusionsUse this to see which keywords are hidden from a niche's Master Keyword List, e.g. when a keyword the user expects is missing from `get_niche_keywords`. Returns the same lists the DataDive web app shows, oldest first: `exact` — each entry hides the one keyword that matches it exactly, with that keyword's `searchVolume` (0 when the niche has no such keyword); `phrase` — each entry hides every keyword that contains the phrase, with `excludedKeywordsCount` and the total `searchVolume` of the keywords it hides (not counting ones already excluded exactly). Exclusions hide keywords wherever they come from: the dive, the Outliers and Residue lists, and hand-added ones. Change them with `add_keyword_exclusions` and `remove_keyword_exclusions`.get_niche_rootsUse this when the user wants to find the highest-impact words across a niche's keywords, asks about 'keyword roots', or wants to prioritize terms for a listing. Retrieves the keyword lexical roots for the Niche — individual words or word-combinations extracted from the Master Keyword List (e.g. 'bluetooth headphones' yields roots 'bluetooth', 'headphones', 'bluetooth headphones'). Returns `roots` and `normalizedRoots` tables, each item with root, frequency (how many keywords contain it), broadSearchVolume (summed search volume of those keywords), and broadSearchVolumeRatio (0-1, relative to the top root), plus the per-keyword breakdowns and latestResearchDate.get_niche_competitorsUse this for competitor ASINs, product titles, BSR, sales/revenue benchmarks, or niche opportunity scoring. Retrieves the list of Competitors within the specified Niche along with Niche statistics. For the Niche: keyword statistics, opportunity evaluation, benchmark median values, and overall competitor strength assessment. For each Competitor: ASIN, product title, BSR, category and full category tree, sales, revenue, ratings, reviews, price, number of variations, image URL, total Ranking Juice, and ranking data. BSR supports ranking Competitors by sales position within mentioned category. For the Ranking Juice breakdown by title, bullets and description, use `get_ranking_juice`.get_ranking_juiceUse this when the user asks 'how do I rank higher', 'what's my ranking juice', or wants listing-optimization guidance. Retrieves the Ranking Juices for each Competitor within the specified Niche. Ranking Juice is DataDive's proprietary metric identifying the key factors driving search-ranking success for each Competitor's listing. Returns currentListing and optimizedListing breakdowns (rankingJuice + per-property contributions: title, bullets, description) plus per-competitor listing scores and the latestResearchDate.list_rank_radarsUse this to find a `rankRadarId` before calling `get_rank_radar_data`. Filter by `nicheId` if the user has already identified a niche. Retrieves a paginated list of Rank Radars — keyword-rank trackers monitoring organic and sponsored positions for specific ASINs over time. Each item includes id, status (ACTIVE, PAUSED or ARCHIVED), asin, marketplace, keywordCount, title, imageUrl, and summary metrics: top10KW, top10SV, top50KW, top50SV. Supports pagination metadata (currentPage, pageSize, total, lastPage, hasNext, hasPrev); a page holds at most 50 Rank Radars, so a large account needs several calls.get_rank_radar_dataUse this to analyze keyword ranking trends over time. Requires startDate and endDate (yyyy-mm-dd), at most 90 days apart. Retrieves historical keyword ranking data for the specified Rank Radar within the date range. Without `currentPage` and `pageSize` it returns every active keyword in one result. To read one page instead — faster for large Rank Radars — pass `currentPage` and/or `pageSize` (max 100) and continue with `currentPage` + 1 while `hasNext` is true. The response carries currentPage, pageSize, total, lastPage, hasNext and hasPrev; `total` is the Rank Radar's active keyword count. Paused keywords are not included. Each keyword has id, keyword, searchVolume, relevancy, ranks (per-day { date, organicRank, sponsoredRank, impressionRank }), and `highlights` — the user tags on this keyword. Each `highlights` entry is the same object `list_rank_radar_tags` returns (id, title, color, description, startAt, endAt, type, krtId, krtKeywordId); its `id` is the `tagId` the tag-management tools take. A rank of 101 means the product was not in the top 100 results that day. For PPC metrics use `get_rank_radar_ppc_data`; for Search Query Performance use `get_rank_radar_sqp_data`. Use after `list_rank_radars` to discover a `rankRadarId`.get_rank_radar_sqp_dataUse this when the user asks how shoppers search, click, add to cart and buy for the keywords of a Rank Radar — Amazon's Search Query Performance (SQP) data, from Brand Analytics. Requires startDate and endDate (yyyy-mm-dd), at most 90 days apart; 30 days or less keeps it fast. Each keyword's metrics are aggregated over the range: searchQueryVolume and searchQueryScore; impressions, clicks, cart adds and purchases, each as the total across all sellers (`*TotalCount`), this ASIN family's count (`*AsinCount`) and its share (`*AsinShare`); click, cart-add and purchase rates; and ctr/cvr for the market (`*Total`) and for this ASIN family (`*Asin`). `numberOfDaysWithData` says how many days of the range had SQP data; metrics are null when there is none, which is normal for low-volume keywords, recent dates (Amazon publishes SQP with a delay) and sellers without Brand Analytics. Results are paged by keyword: `data` holds one page, and the response carries currentPage, pageSize, total, lastPage, hasNext and hasPrev. `total` is the Rank Radar's active keyword count; to read every keyword, call again with `currentPage` + 1 while `hasNext` is true, using `pageSize` 100 for large Rank Radars. Paused keywords are not included. Each call counts toward API usage and is rate limited (about 60 requests/minute). Use after `list_rank_radars` to discover a `rankRadarId`.get_rank_radar_ppc_dataUse this when the user asks about advertising (Sponsored Products) performance for the keywords of a Rank Radar. Requires startDate and endDate (yyyy-mm-dd), at most 90 days apart; 30 days or less keeps it fast. Each keyword's metrics are aggregated over the range: sponsoredRank (median; 101 means not in the top 100 sponsored results), impressionRank and impressionRankShare; how many exact, phrase, broad and auto campaigns target it; organicSales, ppcSales, ppcSpend, costPerClicks, clickThroughRate, conversionRate, totalClicks, totalImpressions, totalOrders and acos. Metrics are null or 0 when the seller runs no ads on a keyword. Set `includeCampaigns` for the per-campaign breakdown. Results are paged by keyword: `data` holds one page, and the response carries currentPage, pageSize, total, lastPage, hasNext and hasPrev. `total` is the Rank Radar's active keyword count; to read every keyword, call again with `currentPage` + 1 while `hasNext` is true, using `pageSize` 100 for large Rank Radars. Paused keywords are not included. Each call counts toward API usage and is rate limited (about 60 requests/minute). For the seller's campaigns as a whole, use `list_ppc_campaigns`. Use after `list_rank_radars` to discover a `rankRadarId`.list_rank_radar_tagsUse this to find a `tagId` before calling `update_rank_radar_tag`, `delete_rank_radar_tag`, `add_rank_radar_tag_search_terms` or `remove_rank_radar_tag_search_terms`. Returns the user tags ("highlights") on the specified Rank Radar, ordered by title (case-insensitive, ascending). Each row carries `id`, `title`, `color`, `description`, `startAt`, `endAt`, `createdAt`, and `keywordCount` — the number of Rank Radar keywords this tag is applied to. The system "paused" tag (the one the quota pauser writes with a reserved colour) is not listed and cannot be addressed from these tools. Supports pagination metadata (currentPage, pageSize, total, lastPage, hasNext, hasPrev); a page holds at most 50 tags.create_rank_radarUse this to start tracking organic and sponsored keyword rankings for an ASIN within a niche. ⚠️ Spends Search Term tokens (cost scales with `numberOfKeywords`) and cannot be undone — set `confirm: true` only after the user approves the cost. Not safe to retry: each call spends tokens again and creates a separate Rank Radar — if a call errors or times out, check `list_rank_radars` instead of re-calling. The first call previews the creation and may return `warnings` alongside the cost note — for example that this product family is already tracked by another Rank Radar. Show every warning `message` to the user verbatim, before asking them to confirm; do not summarise it or decide on their behalf. A warning never blocks creation. Returns the new `rankRadarId`; read its data later with `get_rank_radar_data`. Requires `asin`, `numberOfKeywords` (min 1), and a `nicheId` from `list_niches`.- +31 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.
Apiguru Amazon Data
{
"mcpServers": {
"apiguru-amazon-data": {
"command": "npx",
"args": [
"-y",
"apiguru-mcp"
]
}
}
}
DataDive
{
"mcpServers": {
"datadive": {
"command": "npx",
"args": [
"-y",
"@datadive-tools/mcp"
],
"env": {
"DATADIVE_API_KEY": "YOUR_DATADIVE_API_KEY_HERE"
}
}
}
}
Frequently asked questions
Apiguru Amazon Data is categorized under E-Commerce and uses a local stdio subprocess. DataDive belongs to E-Commerce and uses a local stdio subprocess. Pick Apiguru Amazon Data for e-commerce work and DataDive 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.
Apiguru Amazon Data is listed as freemium — a free tier with paid plans. Auth: no auth required. DataDive 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 apiguru-amazon-data and datadive as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Apiguru Amazon Data has a quality signal of 64/100 (Good) with 2 views, 0 installs and 0 GitHub stars. DataDive has 63/100 (Good) with 1 views, 0 installs and 1 GitHub stars.