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750,166 tools. Updated 2026-11-06 08:54

"Amazon Prime" matching MCP tools:

  • Get detailed KDP niche intelligence for a specific keyword. Returns demand score, competition score, Amazon BSR range, estimated monthly revenue, review threshold, average book pricing, and data freshness for the given Kindle publishing niche. Pricing tiers (x402 USDC on Base network): - $0.03 per query for cached/pre-seeded keywords - $0.10 per query for live on-demand research (new keywords) Use the free `list_niches` tool first to see available keywords. Payment options: 1. Set the KDP_X_PAYMENT environment variable on the server for auto-pay. 2. Pass a valid x402 payment header via the x_payment argument. 3. If neither is set, the tool returns structured 402 payment instructions that an x402-capable agent can use to construct and retry payment. Args: keyword: The KDP niche keyword to research (e.g. "romance novels", "keto cookbook") x_payment: Optional base64-encoded x402 payment header. Takes precedence over the KDP_X_PAYMENT environment variable.
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  • Search Amazon products 1. Function Discover candidate Amazon products by keyword, category and business metrics, returning a paginated list of the shared Product model (max 100 per page). Reads the catalog snapshot: omit dateRange or pass 'Nd' for the latest daily snapshot, or 'YYYY-MM' for that month's month-end snapshot (earliest 2026-02). Filters include price, monthly sales and revenue floors, growth rates, BSR and its 7-day change, rating, seller count, brand and seller lists, badges, fulfillment, package weight in ounces, package size tier, Buy Box seller country, gross margin rate and category scope. Setting any range or list filter excludes products whose value is unknown, so a narrower result set is a coverage effect, not a market finding. categoryScope=includeChildren (default) matches the given category levels and every subcategory; exactOnly requires the product's path to end at the last given level. monthlySalesFloor is the page's 'N+ bought in past month' figure carried forward by the catalog snapshot, a lower bound rather than an estimate. Billing: 1 credit per request. 2. Use cases Use search to find products you do not know yet — niche discovery, competitor screening, filtering by margin or growth. Use /products/detail to read ASINs or a parent's variants you already know; it takes no filters. Use /products/competitors for competitor lookup around one ASIN, and /markets/search for category-level demand and competition. ### Responses: **200**: Successful Response (Success Response) Content-Type: application/json **Example Response:** ```json { "success": true, "meta": { "requestId": "Requestid", "timestamp": "Timestamp" } } ``` **Output Schema:** ```json { "properties": { "success": { "type": "boolean", "title": "Success", "description": "Whether the request was successful", "default": true }, "data": { "title": "Data", "description": "Response data payload" }, "error": { "description": "Error details if request failed" }, "meta": { "description": "Metadata for API responses.\n\nCredit fields follow the ADR-0003 parallel-fields strategy (Option 3):\n- `credits_remaining` / `credits_consumed` (int): legacy fields, rounded\n to whole credits, kept for zero-breaking-change to existing SDK clients.\n- `credits_remaining_exact` / `credits_consumed_exact` (float): new\n precision-aware fields for clients that opt in to decimal credits.\n\nSee ADR-0003 decision 5 and the \u00a78 deprecation timeline.\n\nTODO(2026-11, ADR-0003 \u00a78 +6mo): mark `credits_remaining` /\n`credits_consumed` as `deprecated=True` in their Field() definitions\nand announce in customer changelog.\nTODO(2027-05, ADR-0003 \u00a78 +12mo): remove the legacy int fields via a\nmajor-version bump of the OpenAPI surface.", "properties": { "requestId": { "type": "string", "title": "Requestid", "description": "Unique request identifier" }, "timestamp": { "type": "string", "title": "Timestamp", "description": "Response timestamp in ISO 8601 format" }, "total": { "title": "Total", "description": "Total number of records" }, "page": { "title": "Page", "description": "Current page number" }, "pageSize": { "title": "Pagesize", "description": "Number of records per page" }, "totalPages": { "title": "Totalpages", "description": "Total number of pages" }, "creditsRemaining": { "title": "Creditsremaining", "description": "Remaining API credits (rounded to whole credits; see creditsRemainingExact for precise value)" }, "creditsConsumed": { "title": "Creditsconsumed", "description": "Credits consumed by this request (rounded; see creditsConsumedExact for precise value)" }, "creditsRemainingExact": { "title": "Creditsremainingexact", "description": "Remaining API credits, precise to 1 decimal place" }, "creditsConsumedExact": { "title": "Creditsconsumedexact", "description": "Credits consumed by this request, precise to 1 decimal place" }, "tokensUsage": { "description": "Provider token-usage block \u2014 populated on terminal video polls only, null on every non-video endpoint. See TokensUsage for its fields." } }, "type": "object", "required": [ "requestId", "timestamp" ], "title": "ResponseMeta" } }, "type": "object", "required": [ "meta" ], "title": "OpenApiResponse[list[Product]]", "examples": [] } ``` **422**: Validation Error Content-Type: application/json **Example Response:** ```json { "detail": [ { "loc": [], "msg": "Message", "type": "Error Type", "ctx": {} } ] } ``` **Output Schema:** ```json { "properties": { "detail": { "items": { "properties": { "loc": { "items": {}, "type": "array", "title": "Location" }, "msg": { "type": "string", "title": "Message" }, "type": { "type": "string", "title": "Error Type" }, "input": { "title": "Input" }, "ctx": { "type": "object", "title": "Context" } }, "type": "object", "required": [ "loc", "msg", "type" ], "title": "ValidationError" }, "type": "array", "title": "Detail" } }, "type": "object", "title": "HTTPValidationError" } ```
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  • Confirms whether an SSP/exchange is authorized to sell a publisher's inventory according to that publisher's ads.txt. This is a cache lookup against ads.txt files crawled daily across the top 10,000 publisher domains — it does NOT fetch the publisher's ads.txt live, so it is fast and adds no latency to a real-time bidding decision. Use this tool when: - You are an ad-buying agent and want to confirm, pre-bid, that a supply path (publisher → exchange → seller_id) is legitimate. - You are detecting domain spoofing or unauthorized resale in a bid stream. - You want to check whether a seller is listed DIRECT or RESELLER. Do NOT use this tool when: - You want a full supply-path trust score — that endpoint is Sigil P31. - You want surveillance tracker data for the domain — use `get_domain`. Inputs: - `publisher_domain` (body, required): Publisher domain, e.g. `nytimes.com`. A `www.` prefix and scheme/path are stripped automatically. - `exchange_domain` (body, required): The exchange/SSP domain as it appears in ads.txt, e.g. `google.com`, `amazon-adsystem.com`. - `seller_id` (body, required): The publisher's seller/account ID at that exchange, e.g. `pub-4177862836555934`. Matched exactly. - `seller_type` (body, optional): `DIRECT` or `RESELLER`. When supplied it is checked against the ads.txt entry; a mismatch is reported as a warning. - `resolve_chain` (body, optional): When true, a matched RESELLER entry is cross-checked against the exchange's sellers.json (one authoritative hop). Returns: - `verified`: true (entry found), false (confidently not listed), or null (ads.txt could not be retrieved — indeterminate). - `confidence`: `high` | `degraded` | `low` | `unknown`. - `seller_entry`: the matched ads.txt line (line number, raw text, parsed fields) when `verified` is true; otherwise null. - `ads_txt_parse_status`, `ads_txt_last_parsed`, `stale`: provenance of the cached crawl this answer is derived from. - `reseller_chain`: empty unless `resolve_chain: true` and the matched entry is RESELLER — then it carries the sellers.json cross-check for the seller. - `warnings`: actionable flags, e.g. `publisher_not_in_corpus`, `publisher_has_no_ads_txt`, `seller_type_mismatch`, `ads_txt_cache_stale`. Cost: - Counts as one request against the daily rate limit. Latency: - Typical: <50ms (single cache lookup, no outbound fetch). p99: <120ms.
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  • Pass exactly ONE of {query} or {category_slug}. Searches Amazon (com|co.uk|de|fr|es|it) and returns ranked hits with buybox price (gross + VAT-excluded net), ratings, review counts, and ASINs. Drill down with glim_amazon_get(ref). Set sort_by='most_reviewed' (with min_reviews to filter junk) for a trust-weighted re-rank within the current page. Compact text by default; pass format='json' for full structured data.
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  • Analyze a negative Amazon review for root cause and a suggested response (async; 2 credits). Extracts the underlying issue from a critical review and drafts a brand-appropriate response angle. Use this after a negative review appears, to decide how to reply. Do NOT use it to generate a listing or an appeal - use generate_listing or generate_poa for those. Read-only; deducts 2 credits; runs asynchronously, poll for the result. Args: text: the negative review text (required). marketplace: marketplace code (default US). lang: zh or en (default en).
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  • Keyword Research (marketplace term universe): search the FULL Top Search Terms store by phrase - not anchored to any ASIN. Filters: contains (up to 5 substrings, ANDed), department, rank_max, min_volume; period WEEK or MONTH (MONTH = monthly volumes); sort rank | volume | trend. Each term: search_frequency_rank, volume (amazon_sqp = TRUE Amazon volume where any account's SQP covers the term; estimated_from_rank with volume_band otherwise), rank_change vs ~4 periods back, and the top-3 clicked ASINs with click/conversion shares (the term's competitive landscape). The estimator block reports calibration health (pairs, typical error factor, band coverage). Use keyword_finder for ASIN-anchored lookups; this tool for term-first research. For the guided process start with keyword_opportunities.
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  • Returns Amazon's console change-history events for Sponsored Products and Sponsored Brands (the Seller Central History tab, last 90 days). IMPORTANT: changes made through the API, including every change agentcentral makes, do NOT appear here, so this is typically empty for agent-managed accounts. To see changes agentcentral made use get_action_history; to detect any Sponsored Products campaign-setting change regardless of source use get_campaign_snapshots with view=changes. Amazon does not include who made the change and does not return Sponsored Display changes.
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  • Query verified U.S. generator-level operating, planned, retired, or canceled power capacity from EIA-860M. Use this for capacity questions by state/jurisdiction, county FIPS, source-reported balancing authority code, fuel, prime mover, technology, lifecycle, or year. Pass filters inside the `params` object. The operating/planned/retired/canceled selector is `lifecycle` (e.g. `lifecycle: "operating"`, the default) — there is no `status` or `status_group` parameter. Returns JSON aggregates with citations and optional generator-level records when `include_records` is true. Does not determine electricity supplied, generation MWh, real-time dispatch, capacity factor, battery storage throughput/duration, demand/load, prices, data-center load, or transmission deliverability. For capacity REQUESTED in an ISO interconnection queue (projects pending interconnection, not yet built), use the relevant ISO's queue tool: query_power_interconnection_queue_v1 (MISO), query_power_interconnection_queue_pjm_v1 (PJM — or query_power_interconnection_queue_pjm_cycle_v1 for PJM's cluster/cycle grid), or query_power_interconnection_queue_caiso_v1 (CAISO).
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  • Query verified U.S. monthly net electricity generation (MWh) from EIA-923. Use this for "how much was generated" questions by state, source-reported balancing authority code, fuel, prime mover, sector, plant, or generator, for a given month. For a fuel total or a fuel mix (e.g. "coal generation", "top fuels"), filter or group by `fuel_group` — it sums the several energy_source_code values a fuel spans (coal alone is 6 codes), so a total is correct-by-construction; use the raw `energy_source_code` only when you want one exact as-reported code, since it splits coal/biomass across sub-codes. Select one `atom`: `by_fuel` (default — the complete plant total) or `by_generator` (generator-level, joinable to EIA-860M); never sum across atoms. History runs monthly from 2014-01 onward and is served by default: a bare `data_month` anywhere in that window answers from the newest promoted vintage covering it, and the response `as_of` is that knowledge cut (pin `as_of` to any date to reproduce what was served then — it resolves to the newest vintage at or before it; an empty result names the served window in an `empty_scope` note). `balancing_authority_code` is reported by EIA only from 2018 onward — a BA-filtered query cannot see earlier months. Pass filters inside the `params` object. Returns JSON aggregates with citations down to the exact source month-cell. Does not determine installed capacity (MW — use power.capacity), demand/load, wholesale prices, fuel cost, heat rate, capacity factor, or real-time/hourly dispatch.
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  • Search affiliate shopping products and get ranked recommendations. Only `query` is required — a natural-language description of what the shopper wants (e.g. "a warm waterproof jacket for winter hiking"). Add fields to narrow results: - keywords: exact product terms. Use a SPECIFIC product type ("women running shoes", "stainless steel knife set"), not a bare generic noun ("shoes", "knife") — a generic keyword can surface the wrong audience. Audience/occasion go in `query`. - commerce_l2s: category ids to restrict to (read the `commerce://categories` resource for valid values — that taxonomy is for shopping only). - max_price / min_price: price band (USD). platforms: ["amazon"|"walmart"] (empty=all). - intent: ranking preset — "cheapest" | "best_discount" | "top_rated" | "best_value". - require_commission: only return products that pay a commission (default: server setting, normally on). Pass false to include zero/unknown-commission products. - limit: max results (default 10). `agent_id` is REQUIRED: your registered, active agent id. It is the attribution key and the access key — a missing/blank or unregistered agent_id is rejected (no anonymous use). Returns {count, products:[{offer_id,title,brand,price,rating,...,buy_url,product_url}]}. Each product's `buy_url` is ALREADY the trackable affiliate buy link — hand it to the shopper directly. (`product_url` is the plain product page.) There is no separate resolve/click step.
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  • Check which of the 11 Amazon marketplaces a keyword was observed in Amazon's own autocomplete suggestions, with first/last observed dates and an example current suggestion-list position per marketplace. Use when a seller asks 'does anyone type X on Amazon Germany/Japan/…', compares keyword presence across countries, or plans a marketplace expansion (pair with brand_xmarket / operator_xmarket_presence). Exact-keyword match — not volumes, not rankings. Amazon marketplaces only.
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  • Fetch a LIVE Amazon search-results page (SERP) right now — the ranked organic + sponsored listings a shopper would see this minute for a query, with position, ASIN, title, price, rating and badges. Use when the user wants CURRENT ranking/visibility: 'who ranks for "dog bed" on Amazon right now', 'is my ASIN on page 1 for this keyword', 'what's sponsored vs organic for this search'. This is a real-time fetch (takes ~10-40 seconds) — for warehouse keyword search use search_products; for buying advice use shopping_search. Amazon marketplaces only; up to 3 pages per call.
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  • Use 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).
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  • Fetch 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.
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  • Search 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.
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  • Aggiorna una o PIÙ campagne Sponsored Brands (SB) ESISTENTI: budget giornaliero, stato (ENABLED|PAUSED) e OTTIMIZZAZIONE DELL'OFFERTA. Per ottimizzare senza tagliare volume NON strozzare il budget: usa i moltiplicatori per posizionamento con patches.bid_adjustments_by_placement=[{placement, percentage}] — placement (enum verificato): TOP_OF_SEARCH | DETAIL_PAGE | HOME | OTHER (alias comodi: top, detail_page, home, other); percentage = aumento % dell'offerta su quella posizione, SOLO valori positivi (0 o più): dal 15/6/2026 Amazon NON accetta più moltiplicatori SB negativi e il tool li rifiuta prima di chiamare Amazon. Per spendere meno su una posizione abbassa i bid di keyword/target (ads_update_sb_keyword_bid/ads_update_sb_target_bid) e alza le altre posizioni. Passare i moltiplicatori implica bid_optimization=false (ottimizzazione Amazon spenta — richiesto da Amazon); con bid_optimization=true/AUTO ottimizza Amazon e i moltiplicatori non servono. SINGOLA: campaign_id + patches. BATCH: items=[{campaign_id, daily_budget?, state?, bid_adjustments_by_placement?}], MAX 25. Esito verificato per-id e PER-PLACEMENT (read-after-write). NON crea campagne nuove.
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  • Create an idempotent, auditable write intent that builds a complete brand-new campaign structure (one or more campaigns, each with ad groups, keywords, and product ads) for the given ASINs in a single call -- this is not for editing anything that already exists. Preview only: nothing is built on Amazon until the returned request is confirmed via approve_write_v1 and then execute_write_v1, at which point the durable pipeline fans the build out into a trackable campaign setup job (poll its progress with get_campaign_setup_job_v1). The preview states `will_dispatch`: true means execute_write_v1 sends it to Amazon, false (execution_mode PREVIEW_ONLY) means it is recorded and never sent. Keywords and targets are researched while the structure is built and are not part of this preview; the campaigns are created ENABLED and start serving once built (get_campaign_setup_job_v1 lists them). A product that already has one of the requested campaign types (live, or built by a setup in the last 30 days) is refused unless allow_existing_structure is true. At most one campaign structure setup can be running per profile at a time. To create a single ad group, keyword, product target, or product ad in an EXISTING campaign instead of building a whole new structure, use prepare_adgroup_create_v1 / prepare_keyword_create_v1 / prepare_product_target_create_v1 / prepare_product_ad_create_v1 instead. To create ONE single custom campaign with a name you choose (for example a Phrase or Broad keyword campaign), use prepare_campaign_create_v1 and then fill it with prepare_adgroup_create_v1 / prepare_product_ad_create_v1 / prepare_keyword_create_v1.
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  • Create an idempotent, auditable write intent to edit one or more EXISTING ad groups' name, state (ENABLED/PAUSED/ARCHIVED), and/or default bid -- the default bid an individual target in that ad group falls back to when it has none of its own, not any one target's own bid. At least one field must be supplied per ad group, and only the fields supplied are changed. campaign_id must come from get_campaigns and adgroup_id from get_ad_groups. Nothing changes on Amazon or in Sellantica until the returned request is confirmed via approve_write_v1 and then execute_write_v1. The preview states `will_dispatch`: true means execute_write_v1 sends it to Amazon, false (execution_mode PREVIEW_ONLY) means it is recorded and never sent. To change one target's own bid or state instead of the ad group's default, use prepare_target_bid_v1; to create a new ad group instead of editing one, use prepare_adgroup_create_v1.
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  • Create an idempotent, auditable write intent to change the state of one or more EXISTING product ads: enable, pause, or archive (archiving is dispatched as an Amazon delete, not a state PUT). ad_id must be the Amazon product ad's own id (not its ASIN or its ad group id); a request for a state the ad is already in is rejected at prepare time. Nothing changes on Amazon or in Sellantica until the returned request is confirmed via approve_write_v1 and then execute_write_v1. The preview states `will_dispatch`: true means execute_write_v1 sends it to Amazon, false (execution_mode PREVIEW_ONLY) means it is recorded and never sent. To create a brand-new product ad instead of changing an existing one's state, use prepare_product_ad_create_v1.
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  • Amazon products that compete with one product for the same searches, with how many they share and how each ranks. Use when: "which products compete with ours on Amazon", "who shows up beside this ASIN", building a competitor set for a listing. Not for: the results for one search, use amazon_search_products. Web competitors, use seo_get_serp_competitors. Returns: List. rows[]: asin, shared_keywords, avg_position, organic_keywords, organic_volume, sponsored_keywords. ASINs only: call amazon_get_product for a title. The product itself is left out. Cost: 5 + 5 per 100 rows. Default 100 rows = 10 credits. Cached 7 days. Example: {"asin": "B0B41YH9B6", "limit": 20}
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  • Monthly Amazon search volume for up to 1,000 keywords you already have, in the order given. Use when: "how many people search Amazon for X", comparing Amazon demand with Google demand, sizing a product idea on Amazon. Not for: Google volume, use seo_get_keyword_metrics. Finding keywords, use amazon_search_keywords. Returns: List. rows[]: keyword, volume (monthly Amazon searches; null = no data). Cost: 5 + 5 per 100 keywords. 10 keywords = 6 credits. Cached 7 days. Example: {"keywords": ["standing desk", "desk converter"]}
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