DataDive vs Easyparser — Amazon Product & Seller Data
A side-by-side look at the DataDive and Easyparser — Amazon Product & Seller Data MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Easyparser — Amazon Product & Seller DataE-Commerce · Local subprocess (stdio)Real-time Amazon product, seller, and search data for AI agents across 21 marketplaces.60GoodAuth: not declaredVerdict: choose DataDive for e-commerce work as a local process; choose Easyparser — Amazon Product & Seller Data 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 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.
Choose Easyparser — Amazon Product & Seller Data when
- You need dedicated capabilities in the E-Commerce domain.
- You prefer a local stdio subprocess transport.
- You have the keys it needs:
EASYPARSER_API_KEY. - You want tools like
get_product_detail, get_product_offers, search_products.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | Easyparser — Amazon Product & Seller Data | |
|---|---|---|
| Category | E-Commerce | E-Commerce |
| Quality signal | 63/100 · Good | 60/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | Not declared |
| Pricing | Not declared | Not declared |
| Required env vars | DATADIVE_API_KEY | EASYPARSER_API_KEY |
| Verified owner | Community listing | Community listing |
| GitHub stars | 1 | 0 |
| Directory activity | 1 views 0 installs 0 upvotes | 3 views 0 installs 0 upvotes |
| Tools | 43 | 17 |
| Full listing | View DataDive | View Easyparser — Amazon Product & Seller Data |
Tools & capabilities
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
Easyparser — Amazon Product & Seller Data tools 17
get_product_detailFetch complete, structured information about a single Amazon product: title, brand, images, feature bullets, description, Buy Box price and availability, Prime eligibility, rating and review count, Best Sellers Rank, category path, variants, specifications, and top reviews. This is the primary tool for product analysis, listing enrichment, and price monitoring. Use this tool when you need a full product profile. If you only need competing seller prices, use get_product_offers instead (cheaper context). If you only need the sales rank, use get_bestseller_rank. If you only need shipping dimensions or the FBA fee category, use get_package_dimensions. Provide EITHER the identifier field OR url — never both. Costs 1 credit; set a_plus_content to true only when the user explicitly asks about enhanced brand content, as it costs 1 extra credit.get_product_offersFetch the list of seller offers for an Amazon product: every merchant selling the item with their price, shipping cost, condition (new/used), fulfillment type (FBA/FBM), and seller rating. Also returns the Buy Box winner. This is the tool for competitive price analysis, reseller monitoring, and availability tracking. Use this tool when the question is about WHO sells the product and at WHAT price. If you need the product's own details (title, images, rating), use get_product_detail instead. Each requested page costs 1 credit. Start with a single page (default) and only increase max_page if the user needs the full offer list. Use the condition and shipping filters to narrow results instead of paginating — it is cheaper.search_productsSearch Amazon by keyword or a full search/category URL and get structured product listings: ASIN, title, price, rating, review count, Prime status, and badges (Best Seller, Amazon's Choice) for each result. Also returns the available refinement filters (category, brand, price range) with their IDs. Use this tool for market research, keyword analysis, and product discovery. If the user already has an ASIN or product URL, do NOT search — use get_product_detail directly. Each page costs 1 credit. One page typically returns 20-40 products, which is enough for most questions. Use sort_by and exclude_sponsored to improve result quality instead of fetching more pages. To filter by category or brand, first run one search and read the refinements field in the response to discover valid filter IDs, then pass them in refinements.lookup_productConvert an external product identifier (UPC, EAN, GTIN, ISBN, JAN, MINSAN) into the corresponding Amazon ASIN, with an essential product snapshot. This is the bridge between manufacturer/retail barcodes and the Amazon ecosystem. Use this tool when the user has a barcode or standard product code and wants to find the matching Amazon listing. Typical workflows: catalog matching, inventory synchronization, converting supplier data into Amazon ASINs. Also accepts ASIN as input type if you need to validate one. You MUST set identifier_type to match the code you have — guessing the wrong type returns no results. UPC is 12 digits, EAN/GTIN is 13, ISBN is 10 or 13 (books). If the user gives a keyword instead of a code, use search_products instead. Costs 1 credit.get_sales_historyRetrieve a product's historical performance over up to 12 months: weekly aggregated trends for estimated views/traffic, sales, price, and Best Sellers Rank. This is Easyparser's 'time machine' — the deepest competitive-intelligence tool in this server. Use this tool for trend forecasting, seasonality analysis, conversion-rate estimation, and investment/sourcing due diligence. Do NOT use it for a simple current price check — get_product_detail is 5x cheaper for that. IMPORTANT cost rule: the base cost is 5 credits, and each month of history adds 1 credit (3 months = 8 credits, 12 months = 17 credits). Always ask the user how far back they need, or default to 3 months for a quick trend read. The history array in the response is aggregated by WEEK, not by day.get_bestseller_rankGet a product's current Best Sellers Rank (BSR): its numerical rank in main categories and sub-category node identifiers. A lightweight, single-purpose tool for popularity and demand tracking. Use this tool when you only need the rank — e.g. 'Is this product selling well?' or daily rank monitoring. Lower rank number means higher sales velocity. If you also need price, rating or content, use get_product_detail instead (it includes BSR along with everything else). For historical rank trends, use get_sales_history. Costs 1 credit.get_package_dimensionsGet a product's precise physical and logistical attributes: package height, width, length, weight, and its Amazon fee category (e.g. Computers, Apparel). This is the FBA fee-estimation and shipping-cost tool. Use this tool when the question is about shipping, storage, FBA fees, or warehouse planning. Do NOT use it for general product info — get_product_detail includes a human-readable dimensions string, but only THIS tool returns the structured fee category and exact measurements needed for fee calculation. Costs 1 credit.get_seller_profileUnmask the legal identity and reputation of any Amazon merchant: legal business name, registered business address, and granular feedback trends over 30/90/365 days and lifetime. This is the supplier-verification and brand-protection tool. Use this tool when the user asks 'who is this seller?', 'is this merchant legitimate?', or needs a supplier's legal entity for compliance or invoicing. To see WHAT the seller sells, use get_seller_products. To read individual buyer reviews about the seller, use get_seller_feedback. Provide EITHER seller_id OR url — never both. You can discover a product's seller by calling get_product_offers first. Costs 1 credit.get_seller_productsExtract a seller's full product catalog: every ASIN they sell with brand, category, price, Prime status, variant counts, and rating data. Also returns dynamic refinement filters (department, shipping, sustainability) for drilling into the catalog. Use this tool for competitor inventory analysis, stock monitoring, and pricing-strategy research. If you need the seller's legal identity or reputation instead of their products, use get_seller_profile. Each page costs 1 credit. Start with one page and use sort_by (e.g. 'exact-aware-popularity-rank' for best sellers first) to surface the most relevant products early. Provide EITHER seller_id OR url — never both.get_seller_feedbackRead individual buyer feedback entries for an Amazon seller: star ratings, comment text, and dates — filterable by rating range and time period. This is the reputation deep-dive tool. Use this tool when the user wants to read actual buyer comments about a seller, analyze complaint patterns, or audit service quality. For aggregate reputation scores (30/90/365-day percentages) without individual comments, get_seller_profile is sufficient and cheaper in context. Each page costs 1 credit. To find dissatisfied customers, set max_rating to 3 instead of paging through everything.list_operationsList all Easyparser operations available in this server, with their parameters, credit costs, and example use cases. Works WITHOUT an API key — call this first to understand what the server can do, or when you are unsure which tool fits the user's question. Also returns the list of supported Amazon marketplace domains.list_bulk_jobsList the user's bulk extraction jobs with their progress: operation, domain, status, total/completed/failed/invalid item counts, and timestamps. This reads the SAME data as the Bulk Requests page in the Easyparser web app — so it can see and track jobs the user started from the app UI, not just jobs submitted through this MCP server. Use this tool when the user asks about their bulk jobs: "is my job done?", "what bulk jobs are running?", "did the DETAIL batch finish?". Each row includes a group_id — pass it to get_bulk_job_items to drill into individual items, or to get_bulk_webhook_logs to check webhook deliveries. Statuses: pending (queued), processing (running), completed (all items succeeded), partial_success (some items failed or were dropped — check failed_items/invalid_items counts), failed. This tool is free of per-call credits (monitoring endpoints do not consume credits).- +5 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.
DataDive
{
"mcpServers": {
"datadive": {
"command": "npx",
"args": [
"-y",
"@datadive-tools/mcp"
],
"env": {
"DATADIVE_API_KEY": "YOUR_DATADIVE_API_KEY_HERE"
}
}
}
}
Easyparser — Amazon Product & Seller Data
{
"mcpServers": {
"easyparser-amazon-product-seller-data": {
"command": "npx",
"args": [
"-y",
"easyparser-mcp"
],
"env": {
"EASYPARSER_API_KEY": "YOUR_EASYPARSER_API_KEY_HERE"
}
}
}
}
Frequently asked questions
DataDive is categorized under E-Commerce and uses a local stdio subprocess. Easyparser — Amazon Product & Seller Data belongs to E-Commerce and uses a local stdio subprocess. Pick DataDive for e-commerce work and Easyparser — Amazon Product & Seller Data 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.
DataDive has no declared pricing — check the project's own site. Easyparser — Amazon Product & Seller Data 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 datadive and easyparser-amazon-product-seller-data as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, DataDive has a quality signal of 63/100 (Good) with 1 views, 0 installs and 1 GitHub stars. Easyparser — Amazon Product & Seller Data has 60/100 (Good) with 3 views, 0 installs and 0 GitHub stars.
