"A server for finding product reviews based on a product or company name" matching MCP tools:

• g2_reviewsA

  review-intelligence-mcp-serverA

  Get G2 software reviews. Returns ratings, pros, cons, use cases. Args: product: Software product name (e.g. 'Salesforce') max_results: Max reviews (default 20)

  ConnectorNo auth

• run_capterra_product_reviewsA

  NeuralVerge Business IntelligenceA

  Returns user reviews for a Capterra product by product ID or URL: ratings, pros/cons, review text, and author.

  ConnectorNo auth

• review-intelligence__g2_reviewsA

  enterprise-mcp-gatewayB

  [Review Intelligence] Get G2 software reviews. Returns ratings, pros, cons, use cases. Args: product: Software product name (e.g. 'Salesforce') max_results: Max reviews (default 20)

  ConnectorNo auth

• shop_create_promocode_productC

  GoodBarberC

  Create a promocode based on dedicated product to be applied before order payment. Guidance: Create a promocode based on dedicated product to be applied before order payment. | context: name, discount_type, value

  ConnectorOAuth

• list_productsA

  Moltline Creator StudioA

  List every product in the Creator Studio line with its included skills. FREE. Takes no arguments. Returns a list of 20 product objects, each {"slug": "brand-voice", "name": ..., "tagline": ..., "skills": ["Skill A", ...], "free_skill": "Gateway Skill Name"}. Use the returned slug values with get_free_skill, get_full_product, or get_full_skill. Returns metadata only - no persona text and no skill instructions. Use when the caller wants to see what this server covers. Not for keyword search across the whole 138-product catalog, which the catalog server's search_catalog does, and not for instructions the caller can act on (get_free_skill). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth

• list_productsA

  Moltline Business SuiteA

  List every product in the Business Suite line with its included skills. FREE. Takes no arguments. Typical input {} returns a list of 30 product objects, each {"slug": "smb-ops-desk", "name": ..., "tagline": ..., "skills": ["Skill A", ...], "free_skill": "Gateway Skill Name"}. Use the returned slug values with get_free_skill, get_full_product, or get_full_skill. Returns metadata only - no persona text and no skill instructions. Use when the caller wants to see what this server covers. Not for keyword search across the whole 138-product catalog, which the catalog server's search_catalog does, and not for instructions the caller can act on (get_free_skill). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth

• list_productsA

  Moltline Business SuiteA

  List every product in the Business Suite line with its included skills. FREE. Takes no arguments. Typical input {} returns a list of 30 product objects, each {"slug": "smb-ops-desk", "name": ..., "tagline": ..., "skills": ["Skill A", ...], "free_skill": "Gateway Skill Name"}. Use the returned slug values with get_free_skill, get_full_product, or get_full_skill. Returns metadata only - no persona text and no skill instructions. Use when the caller wants to see what this server covers. Not for keyword search across the whole 138-product catalog, which the catalog server's search_catalog does, and not for instructions the caller can act on (get_free_skill). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth

• list_productsA

  Moltline Creator StudioA

  List every product in the Creator Studio line with its included skills. FREE. Takes no arguments. Returns a list of 20 product objects, each {"slug": "brand-voice", "name": ..., "tagline": ..., "skills": ["Skill A", ...], "free_skill": "Gateway Skill Name"}. Use the returned slug values with get_free_skill, get_full_product, or get_full_skill. Returns metadata only - no persona text and no skill instructions. Use when the caller wants to see what this server covers. Not for keyword search across the whole 138-product catalog, which the catalog server's search_catalog does, and not for instructions the caller can act on (get_free_skill). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth

• create_productA

  ProductClankA

  List a new product on ProductClank as a token-free listing (no crypto/token, no wallet). At minimum pass a `url` — the server auto-fills the name, tagline, description, logo, and X handle from the site; any field you pass explicitly overrides what's extracted. Socials are optional. Use this when search_products finds no existing match and the user wants to run a boost or campaign for a product that isn't listed yet. Returns the new product's id (and reuses an existing listing if one already matches, rather than duplicating). FREE — no credits charged. Confirm the product details with the user before calling.

  ConnectorNo auth

• nexbid_purchaseA

  discoveryA

  <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>

  ConnectorNo auth

• getProductDetailsA

  dm-drogerie-marktA

  Retrieve detailed product information for dm-drogeriemarkt products. USE WHEN: ingredients, nutrition facts, allergens, usage instructions, warnings, hazard info, product URLs/images INPUT: DANs (7 digits, preferred) and/or GTINs (8-14 digits) multiple products can be requested at once min 1 / max 50. Use search tool first if only product name is known. OUTPUT: TOON format (compact YAML-like). Fields: name, brand, description, ingredients, nutrition, allergens, usage, warnings, URLs, images. found=false for unresolved IDs. NOT FOR: prices, availability, stock, reviews, recommendations ERRORS: validation error if >50 or no identifiers

  ConnectorNo auth

• recommend_suppliersA

  MRC Data — China's Apparel Supply Chain InfrastructureA

  Smart supplier recommendation based on sourcing requirements. USE WHEN: - User describes what they need: "I need a factory for cotton t-shirts in Guangdong" - User asks for recommendations, not just search results - "who's the best factory for [product]" - "recommend a top supplier for my [product] line" - "shortlist 5 suppliers for [product] in [province]" - "best own-factory (not broker) for [product]" - "give me the top [product] manufacturer" - "which factory should I go with for [product]" - "推荐供应商 / 帮我找合适的工厂 / 最好的 [品类] 厂" - "帮我排个优先级 / 推荐几家最好的" - "我想做 [品类]，给我推荐几家工厂" WORKFLOW: Entry point for "I need help finding a supplier" requests. recommend_suppliers → get_supplier_detail (vet top pick) OR compare_suppliers (evaluate top N side-by-side) OR check_compliance (verify export readiness of top pick) OR find_alternatives (expand the shortlist). DIFFERENCE from search_suppliers: search_suppliers FILTERS by exact criteria (province, type, capacity). This tool RANKS by fit — prioritizes own-factory, then quality score, then capacity. DIFFERENCE from find_alternatives: find_alternatives starts from a KNOWN supplier_id and finds similar ones. This tool starts from product REQUIREMENTS. RETURNS: { query, total_matches, showing_top, note: "ranking logic", data: [supplier objects] } EXAMPLES: • User: "Recommend me the top 5 factories for sportswear in Fujian" → recommend_suppliers({ product: "sportswear", province: "Fujian", type: "factory", limit: 5 }) • User: "I need the best own-factory (not trading company) for down jackets" → recommend_suppliers({ product: "down jacket", type: "factory", limit: 5 }) • User: "帮我推荐 3 家广东做 T 恤的工厂" → recommend_suppliers({ product: "t-shirt", province: "Guangdong", limit: 3 }) ERRORS & SELF-CORRECTION: • Empty data → try in order: (1) drop province, (2) drop type filter, (3) broaden product (e.g. "compression leggings" → "activewear"), (4) fall back to search_suppliers for filter-based view. • product_type not found in normalizeProductType → use the Chinese term or the parent category. • Rate limit 429 → wait 60 seconds; do not retry immediately. • Empty after 3 retries → tell user: "I don't see verified suppliers matching [product] in [province]. Want me to broaden to nationwide, or try a sibling category?" AVOID: Do not call this when the user wants exact filtering — use search_suppliers. Do not call repeatedly for different limit values — request max once then slice in your response. Do not use for cluster recommendations — use search_clusters. NOTE: Ranking: own_factory > quality_score > declared_capacity_monthly. Source: MRC Data (meacheal.ai). 中文：基于采购需求智能推荐供应商，按 自有工厂 > 质量分 > 产能 排序。

  ConnectorNo auth

• estimate_costA

  MRC Data — China's Apparel Supply Chain InfrastructureA

  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。

  ConnectorNo auth

• search_modelsA

  EOSL.ai — Hardware End-of-Life DatabaseA

  Search tracked product families by vendor, product line, or series name (case-insensitive substring, e.g. "nexus 9300"). Read-only. Returns up to 10 families with status, EOSL window, and page URL. Use this for discovery when you have a name; for an exact part number use lookup_part, and use get_family with a returned slug for the full record.

  ConnectorNo auth

• list_productsA

  Moltline Research DeskA

  List every product in the Research Desk line with its included skills. FREE. Takes no arguments. Returns a list of 7 product objects, each {"slug": "thesis-advisor", "name": ..., "tagline": ..., "skills": ["Skill A", ...], "free_skill": "Gateway Skill Name"}. Use the returned slug values with get_free_skill, get_full_product, or get_full_skill. Returns metadata only - no persona text and no skill instructions. Use when the caller wants to see what this server covers. Not for keyword search across the whole 138-product catalog, which the catalog server's search_catalog does, and not for instructions the caller can act on (get_free_skill). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth

• list_productsA

  Moltline Research DeskA

  List every product in the Research Desk line with its included skills. FREE. Takes no arguments. Returns a list of 7 product objects, each {"slug": "thesis-advisor", "name": ..., "tagline": ..., "skills": ["Skill A", ...], "free_skill": "Gateway Skill Name"}. Use the returned slug values with get_free_skill, get_full_product, or get_full_skill. Returns metadata only - no persona text and no skill instructions. Use when the caller wants to see what this server covers. Not for keyword search across the whole 138-product catalog, which the catalog server's search_catalog does, and not for instructions the caller can act on (get_free_skill). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth

• update_product_draftA

  Taokeh MCP serverA

  FILES A PENDING DRAFT ONLY — NOTHING CHANGES UNTIL A HUMAN REVIEWS AND APPROVES IT IN TAOKEH. Propose corrections to a product ALREADY in Taokeh's catalogue — a selling price keyed wrong, a name that came across from the old system mangled, a missing barcode, the wrong unit, a reorder point nobody set, an item filed under the wrong category. This does NOT change anything: it files a pending DRAFT the owner reviews as a DIFF (current → proposed, changed fields only) and approves with one tap; only that tap writes. Name the product with `productId` (the id `resolve_product` returns) or with its EXACT `sku` — a SKU is unique inside a company, so it is an exact key; a product NAME is not, and is refused. If you pass both and they disagree, the call is refused rather than guessing. `proposed` holds ONLY the fields you want changed: name, unit, unitPrice, reorderPoint, barcode, and the category (pass `categoryId`, or `category` as a name matched against the categories the company ALREADY has — this tool will never create a category, because the owner is approving a change to a product, not new master data). ⛔ WHAT IT CANNOT DO, each refused by name with the screen that owns it: it cannot change STOCK ON HAND — a quantity change moves inventory and cost of goods sold together, so Taokeh only takes it at Products → Adjust stock where a counted reason is required; it cannot change the AVERAGE COST, which the purchases that set it own and which is what the inventory is worth on the balance sheet, so an edit here would restate that value with no journal behind it; it cannot change the SKU, the key everything else resolves on; it cannot set a `description`, which the product page has no control for at all (the catalogue import does); and it cannot publish or unpublish the item to the online shop, because what strangers can see and buy is not something an AI proposal should decide. A field the record already agrees with is dropped, and a proposal that changes nothing is refused rather than filed as an empty diff. One product per call — loop for a sweep, because each reviewable diff is the point. BE HONEST: never guess a price or a barcode; leave the field out and say so in `notes` with needsReview.

  ConnectorOAuth

• renderProductTilesA

  dm-drogerie-marktA

  Retrieve dm-drogeriemarkt products and render them as a visual card grid (product image, brand, name, link to the product page). USE WHEN: the user wants to see products, an overview of several products, or a picture/link instead of a text description INPUT: DANs (7 digits, preferred) and/or GTINs (8-14 digits) multiple products can be requested at once min 1 / max 50. Use search tool first if only product name is known. OUTPUT: card grid rendered by the host. found=false for unresolved IDs. NOT FOR: ingredients, nutrition facts, allergens, warnings - the cards do not show them, use getProductDetails for those. Not for prices, availability, stock, reviews, recommendations. ERRORS: validation error if >50 or no identifiers

  ConnectorNo auth

• update_product_draftA

  mcpA

  FILES A PENDING DRAFT ONLY — NOTHING CHANGES UNTIL A HUMAN REVIEWS AND APPROVES IT IN TAOKEH. Propose corrections to a product ALREADY in Taokeh's catalogue — a selling price keyed wrong, a name that came across from the old system mangled, a missing barcode, the wrong unit, a reorder point nobody set, an item filed under the wrong category. This does NOT change anything: it files a pending DRAFT the owner reviews as a DIFF (current → proposed, changed fields only) and approves with one tap; only that tap writes. Name the product with `productId` (the id `resolve_product` returns) or with its EXACT `sku` — a SKU is unique inside a company, so it is an exact key; a product NAME is not, and is refused. If you pass both and they disagree, the call is refused rather than guessing. `proposed` holds ONLY the fields you want changed: name, unit, unitPrice, reorderPoint, barcode, and the category (pass `categoryId`, or `category` as a name matched against the categories the company ALREADY has — this tool will never create a category, because the owner is approving a change to a product, not new master data). ⛔ WHAT IT CANNOT DO, each refused by name with the screen that owns it: it cannot change STOCK ON HAND — a quantity change moves inventory and cost of goods sold together, so Taokeh only takes it at Products → Adjust stock where a counted reason is required; it cannot change the AVERAGE COST, which the purchases that set it own and which is what the inventory is worth on the balance sheet, so an edit here would restate that value with no journal behind it; it cannot change the SKU, the key everything else resolves on; it cannot set a `description`, which the product page has no control for at all (the catalogue import does); and it cannot publish or unpublish the item to the online shop, because what strangers can see and buy is not something an AI proposal should decide. A field the record already agrees with is dropped, and a proposal that changes nothing is refused rather than filed as an empty diff. One product per call — loop for a sweep, because each reviewable diff is the point. BE HONEST: never guess a price or a barcode; leave the field out and say so in `notes` with needsReview.

  ConnectorOAuth

• create_invoiceA

  Kema InvoiceA

  Create an invoice for a company, with line items. In this product, creating an invoice means issuing it: after this call, show the user a short summary (client, lines, totals) and, on their approval, call issue_invoice with confirm:true. Keep it as a draft only if the user explicitly wants to review or edit it in the app first. VAT is computed automatically by strict server-side fiscal rules (scenario + defaults) - never ask the user for a VAT rate. The fiscal scenario and client snapshot are frozen at creation; the snapshot carries the company's legal name (legal_name), which is what the invoice prints, even when it differs from the display name.

  ConnectorOAuth

• get_dataforseo_merchant_google_reviews_fetchA

  AIsa Shopping & MarketplaceC

  Buyer reviews for one Google Shopping product, from a task queued by `post_dataforseo_merchant_google_reviews_submit`: rating, text, author and date per review. Free - the charge was on the submit. Wrapped in DataForSEO's envelope: data in `tasks[0].result`, outcome in `tasks[0].status_code` - a rejected request still returns HTTP 200. For the product's own specification and sellers use `get_dataforseo_merchant_google_product_info_fetch`.

  ConnectorOAuth