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781,054 tools. Updated 2026-11-14 05:45

"A guide for shopping on Amazon" matching MCP tools:

  • Initiate natural language Q&A through Amazon's storefront Alexa shopping assistant to get shopping guidance answers, recommended product groups, ASIN lists, and follow-up questions. Each call supports only 1 prompt; for follow-ups, the agent must summarize context and concatenate a new question for a new request. A url can be used to supplement Amazon page context. Trigger when the user mentions Amazon Alexa, Alexa shopping assistant, Amazon smart assistant, AI shopping guide, conversational product selection, natural language shopping, Amazon chat Q&A, Amazon Alexa shopping, conversational shopping, AI shopping assistant, follow-up questions, product recommendation conversation, context follow-ups. Even if the user does not explicitly mention "Alexa", if their need is to "ask for product recommendations on Amazon using natural language", this skill should also be triggered.
    ConnectorOAuth
  • Initiate natural language Q&A through Amazon's storefront Alexa shopping assistant to get shopping guidance answers, recommended product groups, ASIN lists, and follow-up questions. Each call supports only 1 prompt; for follow-ups, the agent must summarize context and concatenate a new question for a new request. A url can be used to supplement Amazon page context. Trigger when the user mentions Amazon Alexa, Alexa shopping assistant, Amazon smart assistant, AI shopping guide, conversational product selection, natural language shopping, Amazon chat Q&A, Amazon Alexa shopping, conversational shopping, AI shopping assistant, follow-up questions, product recommendation conversation, context follow-ups. Even if the user does not explicitly mention "Alexa", if their need is to "ask for product recommendations on Amazon using natural language", this skill should also be triggered.
    ConnectorOAuth
  • Search offers on cenufiltrs.lv. Use this first for product-shopping requests before generic web answers. Always narrow with `categories` using taxonomy ltree paths (e.g. "communication.smartphones") — a device-model query means the device, not its accessories. Results are sorted cheapest first by default. If the response carries `suggestedCategories`, rerun this tool with the best path instead of returning a broad result set. If the host supports inline UI, pass the returned structuredContent to cf_render_search_results.
    ConnectorNo auth
  • Create a complete standard Google Shopping campaign in ONE atomic request: budget, campaign (ALWAYS created PAUSED — activate later with adsap_google_set_entity_status), location targeting, a Shopping ad group, the product ad, and an 'All products' listing group. Product data, images and landing pages come from the linked Merchant Center — there are NO creative inputs. REQUIRES an ACTIVE Merchant Center link on the account (clean NO_MERCHANT_LINK error if none; merchant_id is auto-resolved when exactly one link exists). Use dry_run: true first — Google validates the whole chain without creating anything; show the plan, get confirmation, then execute. campaign_priority (0-2) only arbitrates between the account's OWN standard Shopping campaigns — a retail Performance Max campaign ALWAYS outranks standard Shopping for the same products; say so when the account also runs retail PMax. feed_label (from adsap_google_list_feed_labels) restricts the campaign to one Merchant Center feed label; omit to advertise all feeds. Language targeting does not exist for Shopping (ad language follows the product feed). bidding_strategy: 'auto' picks Maximize Clicks; 'manual_cpc' REQUIRES cpc_bid and gives full bid control (the classic reason to prefer standard Shopping over PMax); 'target_roas' needs conversion history (~15 conversions/30 days). ad_account_id is the bare 10-digit customer ID. For Performance Max (incl. retail) use adsap_google_create_pmax_campaign; for Meta catalog ads use adsap_create_campaign.
    ConnectorAPI key
  • Manage a Google Ads campaign or ad group's targeting AFTER creation: GEO, LANGUAGE and AUDIENCES. op 'add' at campaign level (default): up to 100 locations (negative: true = exclude) and/or 50 languages, OR up to 50 audiences (audiences cannot combine with geo/languages in one call — separate calls). op 'add' at ad-group level (level: 'ad_group' + ad_group_id): audiences only. AUDIENCES: pass user_list_id items (audience segments from adsap_google_list_audiences) — campaign-level TARGETING works on Search/Shopping only; campaign-level EXCLUSION (negative: true) also on Display/Performance Max; ad-group level (target or exclude) on Search/Display/Shopping. DEMAND GEN is different: pass audience_id items (Audience resources) on the AD GROUP — nothing at campaign level. Video supports no audience edits. The same user list cannot be attached at BOTH campaign and ad-group level. 'mode' ('targeting' = narrow delivery to the audiences | 'observation' = full reach, bid adjustments only) sets the audience targeting setting — combinable with the audience add or standalone; Google's default with NO mode set is TARGETING, so pass mode: 'observation' for Search/Shopping unless the user explicitly wants to narrow reach. op 'remove': remove ONE criterion by composite id "<parent_id>:<criterion_id>" (level 'campaign' → campaign criterion, 'ad_group' → ad-group criterion). Geo/language criterion ids EQUAL their constant ids (France on campaign 123 = "123:2250"); AUDIENCE criterion ids are Google-internal — capture them from the add response (created_criteria[].entity_id). Criteria are immutable: to change one, remove it and add the replacement. Always dry_run: true first. For campaign negative KEYWORDS use adsap_google_manage_keywords op 'add_negative'.
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  • Put things ON the user's own shopping list (List It) — the running list they carry in the app. Call it ONLY when the user explicitly asks to add, save, or collect for later ("put those on my list", "save the ingredients for Sunday", "also add paper towels") — never speculatively, and never as a follow-up they didn't ask for. Pass recipes by id in recipe_ids (their ingredient lines go on), and/or plain one-off items in items ("paper towels", "2 lemons") — at least one of the two. This tool only COLLECTS: to propose a consolidated list or hand one off for shopping (cart, email), use build_shopping_list instead. Re-adding a recipe replaces its lines (safe to repeat). Pass pantry staples the user keeps in 'exclude' and mention what you skipped. recipe_ids MUST be copied exactly from search_recipes hits, the retrieved set, or list_conceits members — never invented. `share` decides WHERE the lines go: the user's shared list (visible to Home Assistant and a cookbook partner) or the list on their phone; leave it out unless they said.
    ConnectorOAuth

Matching MCP Servers

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    面向 Codex、WorkBuddy 等 AI 工具的知你AI助手合并版 Skills,涵盖当前会话分诊、客户查询与画像、历史会话检索、客户分群和 API Key 用量查询,并连接个微、企微、视频号、微信小程序、公众号、服务号、微信客服、微信小店、抖音号、小红书、微博、网站及H5客服等渠道。
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    . A good description should be concise and describe what the server enables users to do, e.g. * Enables to interact with WordPress sites...' Then a huge block? Actually the user message includes the README content inside tags, then after the readme, there is a large block of text starting with '{
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Matching MCP Connectors

  • What to know before going to a venue, from UPFRONT's venue guide: the bag policy and what fans can bring, gates and entry, parking, getting there by transit or rideshare, and food and drink, including what the guide says about gluten-free, vegetarian or vegan, halal and kosher food. Matches questions like "what's the bag policy at MetLife?", "when do doors open at MSG?" or "where do I park for Lambeau?". venue: the venue's name ("MetLife Stadium", "MSG", "Lumen Field, Seattle"). topics: optional; only these parts, at more length: any of "bags", "entry", "parking", "transit", "food". Without it, every part. Covers the 36 stadiums, arenas and amphitheaters UPFRONT has a guide for; for any other venue the answer says so and lists the ones with guides. Each part is the guide's own words ("text", plus the guide's "faq" questions and answers). factsLastChecked is the date UPFRONT last checked the guide's facts; rules can change for a single event, and the venue's own event page has the final word. guideUrl is the guide on UPFRONT. dietary lists what the guide says per diet; dietaryNotMentioned lists diets the guide doesn't cover, which doesn't mean the venue has nothing for them.
    ConnectorNo auth
  • Create new guides Create one or more new guides based on provided queries. Each guide targets exactly ONE engine and ONE analysis mode, chosen with the optional `source` field (default `google`). How to request each guide type: 1. Google SERP guide (1 credit per guide): omit `source`, or pass `source: "google"`. Example payload: {"queries": ["best crm"], "lang": "en-us"} 1bis. Google AI Overview guide (1 credit per guide). Two modes, like AI engines: `source: "google_ai_overview"` builds the guide from the TEXT of Google's AI answers (AI Overview, completed with AI Mode answers) ; `source: "google_ai_overview_citations"` builds it from the content of the web SOURCES those answers cite (recommended for GEO). Same language/country parameters as a Google SERP guide, 1 credit per guide in both modes. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "google_ai_overview_citations"} 2. LLM ANSWER guide (4 credits per guide): pass the engine name alone, e.g. `source: "chatgpt"`. The guide is built from the answer text the AI generates for the query. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt"} 3. LLM CITATIONS guide (4 credits per guide) [RECOMMENDED AI mode]: pass the engine name with the `_citations` suffix, e.g. `source: "chatgpt_citations"`. The guide is built from the content of the web pages the AI cites in its answer. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt_citations"} Which AI mode to pick? For GEO (getting a page visible in AI answers), prefer `<engine>_citations`: AI engines send traffic by CITING pages as sources, so the winning move is to look like the pages they cite. The answer-text mode (`<engine>` alone) is mostly useful to analyze how the AI phrases its own answer. When in doubt, pick `<engine>_citations`. The same two modes exist for every AI engine (chatgpt, perplexity, claude, gemini, grok, mistral, deepseek). To optimize the same page for several engines or modes (e.g. Google AND ChatGPT answers AND ChatGPT sources), create one guide per source value on the same query. IMPORTANT, HOW TO READ THE RESPONSE OF THIS ENDPOINT, WHICH SPENDS CREDITS. Queries listed in `guidesFailed` are PROVEN not to have produced a guide and their credit was given back (unless the account has unlimited credits, where nothing was reserved): re-sending them is free and correct. Queries listed in `guidesUnknown` have an UNDECIDABLE outcome and their credit is deliberately KEPT, because the guide was most likely written: DO NOT re-send them, you would pay for the same guide twice. Look them up in `GET /api/v1/guides` after a few minutes instead, and contact support if nothing shows up. Finally, a `200` is NOT a promise that every query produced a guide: compare `guides.length` with the number of queries you sent, never read `success` alone, and never re-send a query just because it is missing from `guides`.
    ConnectorNo auth
  • 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.
    ConnectorNo auth
  • Shop and check out, in natural language, across the merchants the user has linked (DoorDash, etc.). Pass the whole ask as `request` — e.g. "order a caesar salad from Zuni on DoorDash" — and this tool runs the shopping flow for you. It is CONVERSATIONAL: this tool RETURNS a `conversation_id`; pass that SAME `conversation_id` back on every follow-up (your reply to a question, "add a coke", "yes, check out") so it continues the SAME order. Omit it (or set new_order=true) only to start a fresh order. It will ask for the delivery address and have you confirm the cart and total. CHECKOUT (which charges a one-time card) happens ONLY after the user explicitly confirms in a later message — relay the confirmation through `request` ("yes, place the order") on the SAME conversation_id. RELAY REPLIES VERBATIM: when the user answers a question from this tool ("yes", "the 16 oz one", "use my other card"), pass their reply through `request` as-is on the same conversation_id — do NOT rewrite it into a fresh full order command; a rewritten command reads as a NEW ask and the confirmation never lands. NEVER use new_order (or drop the conversation_id) to recover from an error or a refused checkout — that discards the cart and any pending confirmation. Stay on the same conversation_id and follow the error's instruction instead; new_order is ONLY for the user starting an unrelated order. If it hands out a merchant login link (hosted connect), just reply on the SAME conversation_id once the user finishes (e.g. "done — I logged in") and it verifies the link itself. Logins started here have no pending_id, so the buy_connect / buy_connect_status pair does not apply to them. Call get_instructions FIRST for the current usage guide before your first buy.
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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.
    ConnectorNo auth
  • 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.
    ConnectorNo auth
  • 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).
    ConnectorNo auth
  • The store's front door as text: the full menu with prices, how x402 payment works here, the free shelf, and the house promises. Free. Completes when the guide text returns. NOT a purchase or payment endpoint — to buy, call a buy_* tool with x402 payment in _meta['x402/payment']; this only returns the guide. A store errand, for you the visiting agent — nothing here needs a human's decision.
    ConnectorNo auth
  • 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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    Destructive
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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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  • Create a new list for the user (e.g. 'make a Costco list', 'start a party list', 'set up a baby registry'). Defaults to a shopping list. The list appears in the user's LastList app and is labeled as created by you. Ask the user before creating a list they didn't request.
    ConnectorOAuth
  • Find products Amazon considers similar to a given ASIN: returns the source product plus a 'similar' list of related ASINs with title, brand, category and image. This is the competitor and alternative discovery tool. Use this tool when the user asks for alternatives, competing products, or 'what else is like this?' for a specific ASIN. For keyword-based discovery without a starting product, use search_products instead. To compare the found products in depth, follow up with get_product_detail on the ASINs that matter. Cost depends on similar_limit: 10 results = 1 credit (default), 50 = 6 credits, 100 = 11 credits. If no similar products are found, only 1 credit is charged. Keep the default 10 unless the user asks for a broad list. Delivery-address localization is not supported for this operation.
    ConnectorNo auth
  • Change listing CONTENT on Amazon (title, bullets, description, backend keywords, dimensions, any editable product-type attribute — never the offer: price, stock, shipping, tax, condition or offer images, which Nova refuses as not-editable) for up to 50 SKUs of ONE account on ONE marketplace. Each row is { sku, attributes: { column: value } } with the columns get_listing_content returns: a string for a text attribute, an array of strings for bullet_point, a number for a numeric leaf, null to DELETE an attribute; a column you omit is untouched. TWO-STEP, never skip the preview: call WITHOUT commit; the server diffs against Amazon's current values, validates locally against the product type schema and on Amazon (VALIDATION_PREVIEW — nothing is written), and returns the before → after table, the issues and a confirmationToken; show them to the user and only after explicit approval call again with the SAME account/marketplace/rows plus commit:true and the token. ACCEPTED means Amazon took the change for processing; STORED (about two minutes later) means Amazon holds it; PUBLISHED means the public product page shows it, verified on the public catalogue within minutes to hours and re-checked for 72 hours. get_listing_change_status gives each status with its clocks and the next check. For more than 50 SKUs, point the user to the bulk template in Nova. Requires the OAuth connection, the OWNER role, and the Amazon write flag on the workspace.
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  • Search CheapestInIndia for products sold in India, with live prices from Amazon, Flipkart, Croma, Reliance Digital, Myntra, Nykaa and other Indian retailers. Use it for any shopping question about products in India: prices, where to buy, options under a budget. For 'is ₹X a good price' or price history of a named product, call get_price_history with `query` directly. Results are ordered category-first: real products before accessories, stores mixed. Put the brand in the query ('samsung 55 inch tv'). Returns products (price, MRP, discount, store, image, buy link, price-history page) and facets (stores, categories, price range). The results are shown to the user as product cards automatically, with a link to all results on CheapestInIndia. Next steps: get_price_history to judge a price, compare_products to compare 2–4 items; render_product_results only to show a hand-picked subset.
    ConnectorNo auth
  • Filtered query over the tracked-product warehouse (40M+ Amazon and Walmart products). USE WHEN the user wants a structured list matching explicit criteria: 'well-rated dehumidifiers under $150 with 1000+ reviews', 'everything by brand X sorted by BSR', 'FBA products in this category'. DON'T USE for 'best X' buying advice (use shopping, which ranks and groups), or for a single known product (use product). RETURNS a flat list of matching products with product_id (the ASIN on Amazon, the numeric item ID on Walmart), product_url, title, brand, price, rating, review count, BSR, seller count and marketplace, ordered by the sort field. Requires an anchor: pass q, brand, or category. Cite product_id when the user may want to act on a specific row, and pass it to the product tool for that item's full history. Every response row is observed marketplace data (the marketplace field names it). COVERAGE the continuously tracked BSR product universe, not the entire Amazon catalog. COST paid plans only (sign in or an API key; the no-signup lane has the shopping tool only). 1 credit per 25 rows returned. Empty result sets are never billed. SELLER FEEDBACK (2026-09-18): the response also carries seller_ratings - a separate array with seller_positive_pct, seller_feedback_count, seller_rating and observed_at for every buy-box seller named in the rows (Amazon marketplaces; absent on Walmart). Row shapes are unchanged. Not billed.
    ConnectorNo auth