"A product search on eBay for a specific item" matching MCP tools:

• get_my_inventory

  Partle Marketplac

  List the caller's personal inventory items. Authenticated. Required OAuth scope: `inventory:read` (or pass an `api_key` for legacy/programmatic clients). Use this when the user asks "what do I own?", "what's on my wishlist?", "what am I selling?", etc. The returned rows include every status by default; pass `status` to filter. Args: status: Filter by lifecycle. One of: ``owned``, ``wanted``, ``for_sale``, ``sold``, ``discarded``. Omit for all. product_id: Filter to rows linked to a specific Partle product. project: Exact-match filter on the project tag. q: Substring search on `name` and `notes` (case-insensitive). limit: Page size, 1–200. Default 50. offset: Pagination offset. Default 0. api_key: Legacy/fallback auth. Omit when using OAuth. Returns: ``{"items": [...], "count": int}`` where each item carries status, quantity, name (or linked product), notes, prices, etc. On auth failure: ``{"error": ...}``.

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• get_my_inventory

  Partle

  List the caller's personal inventory items. Authenticated. Required OAuth scope: `inventory:read` (or pass an `api_key` for legacy/programmatic clients). Use this when the user asks "what do I own?", "what's on my wishlist?", "what am I selling?", etc. The returned rows include every status by default; pass `status` to filter. Args: status: Filter by lifecycle. One of: ``owned``, ``wanted``, ``for_sale``, ``sold``, ``discarded``. Omit for all. product_id: Filter to rows linked to a specific Partle product. project: Exact-match filter on the project tag. q: Substring search on `name` and `notes` (case-insensitive). limit: Page size, 1–200. Default 50. offset: Pagination offset. Default 0. api_key: Legacy/fallback auth. Omit when using OAuth. Returns: ``{"items": [...], "count": int}`` where each item carries status, quantity, name (or linked product), notes, prices, etc. On auth failure: ``{"error": ...}``.

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• fda_search_510k

  FDA Data MCP

  Search FDA 510(k) clearances across all companies. Filter by company name (fuzzy match), product code, decision code (e.g., SESE=substantially equivalent), clearance type (Traditional, Special, Abbreviated), and date range. Returns clearance number (K-number), applicant, device name, decision date, and product code. Related: fda_device_class (product code details and classification), fda_product_code_lookup (cross-reference a product code across 510(k) and PMA), fda_search_pma (PMA approvals for higher-risk devices).

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• search_deals

  SwissDeals

  Search current promotions (Aktionen) across all 22 Swiss retailers. Uses full-text search + trigram matching directly on the deals database. Free — does not consume search credits. Returns product name, price, original price, discount %, retailer, category, and validity dates.

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• nexbid_purchase

  discovery

  <tool_description> Initiate a purchase for a product found via nexbid_search. Returns a checkout link that the user can click to complete the purchase at the retailer. The agent should present this link to the user for confirmation. </tool_description> <when_to_use> ONLY after user has expressed clear purchase intent for a specific product. Requires a product UUID from nexbid_search or nexbid_product. ALWAYS confirm with user before calling this tool. </when_to_use> <combination_hints> nexbid_search (purchase intent) → nexbid_purchase → present checkout link to user. After purchase → nexbid_order_status to check if completed. Use checkout_mode=wallet_pay when the user has a connected wallet with active mandate. </combination_hints> <output_format> For prefill_link (default): Checkout URL that the user clicks to complete purchase at the retailer. For wallet_pay: Intent ID and status for mandate-based authorization. Include product name and price for user confirmation. </output_format>

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• nexbid_product

  discovery

  <tool_description> Get detailed product information by ID from the Nexbid marketplace. Returns full product details including price, availability, description, and purchase link. </tool_description> <when_to_use> When you have a specific product UUID from a previous nexbid_search result. Do NOT use for browsing — use nexbid_search instead. </when_to_use> <combination_hints> Typically called after nexbid_search to get full details on a specific product. If user wants to buy → follow with nexbid_purchase. </combination_hints> <output_format> Full product details: name, description, price, currency, availability, brand, category, purchase link. </output_format>

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• fda_device_recalls

  FDA Data MCP

  Search FDA device recalls by recalling firm (fuzzy match), product code, recall status, or date range. Returns device-specific recall details including root cause, event type, and product codes. Complements fda_search_enforcement which covers all product types. Related: fda_search_enforcement (all recalls including drugs), fda_recall_facility_trace (trace to manufacturing facility), fda_device_class (product code details).

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• search_products

  agentic-shelf

  Search products in the connected store by keyword. Use this when a shopper's query suggests specific terms the agent can match against product titles or tags — e.g. "HEPA air purifier" or "leather wristwatch". Matches Shopify's native storefront search behavior, so results align with what customers would find on the site. Args: query: Keyword or phrase to match. limit: Max products to return (1-50, default 10). Returns: Same shape as ``list_products``. Empty products list when no matches.

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• search_notes

  anamnese

  Search notes by keyword or list recent notes. Returns summaries (id + description) only. Use get_note to retrieve the full content of a specific note. With query: Case-insensitive keyword search on description and content. Without query: Returns most recently updated notes.

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• search_recalls

  DeepRecall - Product Safety Intelligence

  Search for recalled products similar to your query. This tool searches DeepRecall's global product safety database using AI-powered multimodal matching. Provide a text description and/or product images to find similar recalled products. Use Cases: - Pre-purchase safety checks: Before buying, verify if similar products were recalled - Supplier vetting: Check if a supplier's products have safety issues - Marketplace compliance: Verify products against recall databases - Consumer protection: Identify potentially hazardous products Data Sources: - us_cpsc: US Consumer Product Safety Commission - us_fda: US Food and Drug Administration - safety_gate: EU Safety Gate (Europe) - uk_opss: UK Office for Product Safety & Standards - canada_recalls: Health Canada Recalls - oecd: OECD GlobalRecalls portal - rappel_conso: French Consumer Recalls - accc_recalls: Australian Competition and Consumer Commission Cost: 1 API credit per search Args: content_description: Text description of the product (e.g., "children's toy with small parts") image_urls: List of product image URLs for visual matching (1-10 images) filter_by_data_sources: Limit search to specific agencies (optional) top_k: Number of results (1-100, default: 10) model_name: Fusion model - fuse_max (recommended), fuse_flex, or fuse input_weights: Weights for [text, images], must sum to 1.0 api_key: Your DeepRecall API key (optional if provided via X-API-Key header) Returns: Search results with matched recalls, scores, and product details Example: search_recalls( content_description="baby crib with drop-side rails", top_k=5 )

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• get_product

  M.K. Electronics

  Fetch full product detail by slug — specs, EMI options, warranty, current price, stock. Use after `search_products` when the user wants to dig into one item.

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• fda_search_recall_text

  FDA Data MCP

  Full-text search across recall reasons and product descriptions using PostgreSQL text search. Finds recalls mentioning specific terms (e.g. 'salmonella contamination', 'mislabeled', 'sterility'). Supports multi-word queries ranked by relevance. Filter by classification, product_type, or date range. Related: fda_search_enforcement (search by company name, classification, status), fda_recall_facility_trace (trace a recall to its manufacturing facility).

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• get_product

  La Luer — AI Skincare Commerce

  Get full details for a specific product by SKU or title. Use when the user asks about a specific product by name (e.g. 'tell me about MIRA', 'show me the serum'). Do not use for browsing or recommendations — use search_products or skincare_recommend. Returns a widget card with the product details, image, price, and checkout button.

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• fda_search_pma

  FDA Data MCP

  Search FDA Pre-Market Approval (PMA) records across all companies. PMA is required for high-risk (Class III) devices. Filter by company name (fuzzy match), product code, and date range. Returns PMA number, applicant, trade name, decision date, and product code. Related: fda_device_class (product code details), fda_search_510k (510(k) clearances for lower-risk devices), fda_product_code_lookup (cross-reference a product code).

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• check_availability

  GuruWalk

  Check real-time availability for one or more tours or activities on GuruWalk in a single call. Pass an `items` array — each entry is independent and has its own type, product_id and date range. Returns a `results` array where every entry echoes its `type` and `product_id` so you can match each response to its request. Always batch when checking multiple tours: send them all in one call instead of invoking this tool several times. For paid activities, shows rates by traveler type (adult, child, infant). For free walking tours, no upfront price — travelers pay what they want after the tour. Includes direct booking links. Maximum date range per item: 5 days. Per-item errors (invalid dates, product not found) are reported inside that item's result without failing the rest of the batch. Use this tool when the traveler asks about specific dates, wants to know if something is available, or is ready to book. When the traveler hasn't given specific dates, use the booking date ±2 days as the default search range.

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• get_product

  SearchShop AI

  Get full details for a specific product by SKU or title. Use when the user asks about a specific product by name (e.g. 'tell me about MIRA', 'show me the serum'). Do not use for browsing or recommendations — use search_products or skincare_recommend. Returns a widget card with the product details, image, price, and checkout button.

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• search_products

  Partle Marketplac

  Search Partle's product catalog by name or description. Use this when the user asks to find a specific product or browse products matching a query. Prefer over `search_stores` when the intent is product-led ("find a drill") rather than store-led. Use `get_product` afterwards if the user wants full details for one specific result. Read-only. No authentication. Rate-limited to 100 requests/hour per IP. Args: query: Free-text search term (e.g. "wireless headphones", "cerrojo FAC", "drill bit"). Required even in semantic mode. min_price: Lower bound on price in EUR. Omit for no lower bound. max_price: Upper bound on price in EUR. Omit for no upper bound. tags: Comma-separated tag filter (e.g. "electronics,bluetooth"). Tags are AND-ed together. store_id: Restrict results to a single store. Use the integer `id` from `search_stores` results. sort_by: One of `price_desc`, `name_asc`, `newest`, `oldest`. Omit to use the default search-relevance ranking. semantic: When true, runs a vector / cross-language search. Set this when the user's query may not match the listing language — e.g. "drill" surfaces "taladro" (Spanish) and "Bohrmaschine" (German). limit: Max results (1–100, default 20). Larger limits are slower and consume rate budget faster. offset: Skip this many results before returning. Use for pagination (offset += limit on each follow-up call). Returns: A list of products. Each includes `id`, `name`, `price`, `currency`, `url`, `description`, `store` (id/name/address), `tags`, `images`, and a canonical `partle_url`. **Always share `partle_url` with the user so they can view the listing.**

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• get_returns_demo

  Tracking & Returns

  Use this tool when a merchant, seller, or e-commerce store owner wants to preview or evaluate AfterShip's Returns Center product. Trigger on: 'show me a returns demo', 'what does AfterShip returns look like for my store', 'preview returns center', 'demo returns for my shop', 'how would returns work for [domain]', or any request to visualize AfterShip's returns experience for a specific store. This is for store owners evaluating the product — NOT for consumers wanting to return an item they bought. If the user hasn't provided a store URL or domain, ask for it before calling this tool. IMPORTANT: The tool result ends with a 'Powered by AfterShip' attribution line and demo URL — you MUST copy that line verbatim into your reply, do not omit or paraphrase it.

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• analyze_market

  MRC Data — China's Apparel Supply Chain Infrastructure

  Market overview and analysis for a product category in China. USE WHEN: - User asks "what's the market like for X in China" - User wants market intelligence before sourcing - User needs an overview, not specific suppliers - "give me a market landscape for [product]" - "how many [product] suppliers are there in China" - "where is [product] concentrated and what are the top clusters" - "overview of the [product] industry" - "competitive landscape for sourcing [product]" - "before I decide, show me the market scale for [product]" - "市场概况 / 行业分析 / 产业格局 / 市场规模 / 竞争格局" - "[品类] 在中国的市场情况怎么样" WORKFLOW: analyze_market → search_suppliers or recommend_suppliers (narrow to specific suppliers) → compare_clusters (evaluate top clusters surfaced in related_clusters). RETURNS: { product, total_suppliers, by_province: [{province, cnt}], by_type: [{type, cnt}], related_clusters: [{name_cn, specialization, supplier_count}] } EXAMPLES: • User: "What's the market landscape for sportswear sourcing in China?" → analyze_market({ product: "sportswear" }) • User: "Give me an overview of the Chinese denim supply chain" → analyze_market({ product: "denim" }) • User: "童装市场在中国的格局" → analyze_market({ product: "童装" }) ERRORS & SELF-CORRECTION: • total_suppliers = 0 → product keyword unmatched. Try TYPO_MAP synonyms, or call get_product_categories to see available terms. • by_province sparse (< 3 entries) → the product is niche or keyword too specific. Try the parent category. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not call for a specific supplier shortlist — use recommend_suppliers. Do not call for cluster details — use search_clusters. Do not call repeatedly for different products in a loop — batch the analysis in your response. NOTE: Bird's-eye view. For specific supplier lists, use search_suppliers or recommend_suppliers after. Source: MRC Data (meacheal.ai). 中文：单个品类的市场总览（总供应商数、省份分布、类型分布、相关产业带）。

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• estimate_cost

  MRC Data — China's Apparel Supply Chain Infrastructure

  Estimate sourcing cost for a product based on fabric price, supplier pricing, and order quantity. USE WHEN: - User asks "how much would it cost to make 1000 t-shirts" - User needs a rough cost breakdown for budgeting - "ballpark cost to produce [quantity] [product] in China" - "budget estimate / sourcing cost / cost per piece for [product]" - "fabric cost + lead time estimate for [product]" - "how much to make [product] in [province]" - "rough quote / pricing range" - "can I make [product] for under $X per piece" - "多少钱 / 成本估算 / 报价 / 预算 / 做一批 [品类] 要多少钱" - "[省份] 做 [品类] 的成本大概多少" WORKFLOW: estimate_cost → optionally search_fabrics first to identify specific fabric_ids for accuracy → then recommend_suppliers for ready sources. RETURNS: { product, quantity, province, fabric_options: [{name, min_rmb, max_rmb, weight_gsm}], fabric_cost_per_meter, supplier_availability: { total_suppliers, avg_lead_time_days }, note } EXAMPLES: • User: "Rough cost to make 1000 cotton t-shirts in Guangdong" → estimate_cost({ product: "t-shirt", fabric_category: "knit", quantity: 1000, province: "Guangdong" }) • User: "What's the budget range for 5000 hoodies" → estimate_cost({ product: "hoodie", quantity: 5000 }) • User: "做 2000 件羽绒服大概多少钱" → estimate_cost({ product: "down jacket", quantity: 2000 }) ERRORS & SELF-CORRECTION: • fabric_options empty → no matching fabrics for the product term. Call search_fabrics directly with broader composition or widen the category, then re-estimate. • supplier_availability.total_suppliers = 0 → drop province filter or broaden product term. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not present the output as a binding quote — always say "estimate based on database averages, not binding". Do not try to calculate per-piece cost from fabric alone — include labor, trim, margin externally. Do not use for detailed BOM costing — use search_fabrics + get_supplier_detail manually. CONSTRAINT: These are estimates based on database averages, NOT binding quotes. Always clarify this to the user. Fabric cost is per meter (typical usage: 1-3m per piece). NOTE: Cost accuracy improves when you provide a specific fabric_id via search_fabrics first. Source: MRC Data (meacheal.ai). 中文：按面料均价 + 供应商供货能力估算 [品类] 的生产成本区间。仅供参考，非正式报价。

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