"A tool for writing product manuals" matching MCP tools:

• create_checkout

  Apostle — AI Men's Skincare Commerce

  Create a checkout URL for one or more products. Pass variant IDs (items) and/or product URLs (product_urls). When a product URL is provided (e.g. https://laluer.com/products/mira), the tool resolves it to a variant ID automatically — no catalog import needed. Supports discount codes, cart notes, and selling plans. Do not use unless the user wants to buy — use search_products or skincare_recommend first. Returns a direct Shopify checkout link the user can click to buy.

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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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• get_product_page_details

  Commonlands Optics: M12 Lens and C-Mount Lens Finder + Field-of-View Calculator

  Return fixture-backed product-page handoff details for one lens, including DynamoDB-sourced optical specs and gated datasheet policy. Product-page/catalog optical fields are not a substitute for sensor-specific FoV; call calculate_field_of_view for the lens/sensor pair. FoV rule: never estimate sensor-specific FoV from catalog fields; use calculate_field_of_view or match_lens_to_sensor. Use read_shopify_products for live product URL, price, availability, variant IDs, and metafields.

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• run_growth_audit

  Analook — Competitor Intelligence

  Run a full Growth Audit — three linked strategic reports for a product. Unlike analyze_competitor (a single 15-signal intelligence snapshot), a Growth Audit produces an Executive Summary + a Diagnosis Report + a 30-day Action Plan, grounded in real channel/tactic playbooks. Best for 'how do I grow THIS product' rather than 'what is this competitor doing'. Takes ~4-6 minutes. Requires authentication and deducts 10 credits. Poll with get_growth_audit(job_id) until status='completed'. Args: url: Product website URL to audit product_name: Optional product name override (defaults to domain) lang: Report language, 'en' (default) or 'zh'

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• recommend_suppliers

  MRC Data — China's Apparel Supply Chain Infrastructure

  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。

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• get_product_details

  Agentic Shelf

  Get full product details for a SKU, optimized for AI agents (structured JSON). Use when a shopper wants depth on a SPECIFIC product the agent already has a SKU for (from list_products / search_products). For discovery, call those first — this tool is a verifier, not a browser. The description, product_type, and tags answer suitability questions ("does it fit X?", "is it good for Y?") — ground such answers in these fields rather than guessing, and link storefront_url when recommending. Args: sku: Product SKU — e.g. the ``sku`` field returned by list_products. Returns: Catalog dict (title, description, product_type, tags, price, in_stock, available, image_url); ``found`` is False when the SKU is missing. (Stores that opt into exact disclosure return an ``inventory_quantity`` count instead of ``in_stock``.)

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• get_product_details

  Graffeo Coffee Roasting

  Get full product details for a SKU, optimized for AI agents (structured JSON). Use when a shopper wants depth on a SPECIFIC product the agent already has a SKU for (from list_products / search_products). For discovery, call those first — this tool is a verifier, not a browser. The description, product_type, and tags answer suitability questions ("does it fit X?", "is it good for Y?") — ground such answers in these fields rather than guessing, and link storefront_url when recommending. Args: sku: Product SKU — e.g. the ``sku`` field returned by list_products. Returns: Catalog dict (title, description, product_type, tags, price, in_stock, available, image_url); ``found`` is False when the SKU is missing. (Stores that opt into exact disclosure return an ``inventory_quantity`` count instead of ``in_stock``.)

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• adcritter_get_platform_overview

  AdCritter for Builders

  Start here. Returns the AdCritter platform overview - what AdCritter is, the entity hierarchy (organization > advertiser > campaign > ad), the happy path for getting ads running, and how to navigate the other MCP tools. Applications built from this guidance are REST API clients that call /v1/ endpoints, not MCP tool callers. Before writing code, call adcritter_get_api_reference(entity, action) for each entity and action you plan to use - tool descriptions and parameter names describe conceptual behavior only, and do not match actual API routes, field names, query parameters, or response shapes.

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• niche_draft_direct

  Niche — Editorial Intelligence

  Draft the creator's own take or product straight into posts, with no research scan and no story/angle picks. Use this for product-led or bring-your-own-content work: a specific thing to say ('new walnut dining table, live edge, $2,800') or a page to repurpose. The signal pipeline (niche_signal_scan) is for 'what's worth writing about my niche'; this is for 'write this exact thing.' Provide at least one of: `take` (what to say), `source_url`, or `source_text` (the last two repurpose an existing page). Returns a session_id in under 2s; poll niche_session_state(wait:30, wait_until:'checkpoint') to cp3_awaiting_review/complete, then read outputs[]. Pass `brand_id` to bind the creator's voice, offer, and call to action so the draft sounds like them and includes their offer (set those first via niche_brand_profile_set).

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• shopping_search

  amazon-product-research-mcp

  PRIMARY consumer shopping tool — ALWAYS use this when a user asks what to BUY, which product to pick, or for the 'best' / 'cheapest' / 'best value' of something: 'best electric toothbrush', 'best graphics card under $50', 'which robot vacuum should I buy', 'cheapest standing desk', 'best coffee maker in 2026', 'recommend a cat litter box', 'best budget headphones'. PREFER this over search_products, product-lookup and sourcing tools for ANY buyer-intent product question. Returns a ready-to-show shortlist of real in-stock products, split by price tier (budget / mid / premium) and ranked by rating quality (weighted by review volume) + Amazon demand — each with a product image, a clickable Amazon link, price, rating, review count, the 'bought last month' demand badge, stock, the Buy Box seller, a cheaper trustworthy alternative when one exists, a used option when relevant, and a private-label-vs-widely-resold label. Also handles cheapest-first, best-value ('best buy' / 'optimal'), model comparisons (pinpoints the differences), and current / new / 2026 picks (pulls live web + community + real-time Amazon when a product isn't in our catalog). Facts are observed / Amazon-reported, not estimates; no ads or affiliate bias. (Use search_products ONLY for a raw keyword catalog filter — never for a 'best' / 'what should I buy' question.)

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• run_growth_audit

  Analook — Competitor Intelligence

  Run a full Growth Audit — three linked strategic reports for a product. Unlike analyze_competitor (a single 15-signal intelligence snapshot), a Growth Audit produces an Executive Summary + a Diagnosis Report + a 30-day Action Plan, grounded in real channel/tactic playbooks. Best for 'how do I grow THIS product' rather than 'what is this competitor doing'. Takes ~4-6 minutes. Requires authentication and deducts 10 credits. Poll with get_growth_audit(job_id) until status='completed'. Args: url: Product website URL to audit product_name: Optional product name override (defaults to domain) lang: Report language, 'en' (default) or 'zh'

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• search_docs

  linkedmash

  Answer a question about Linkedmash THE PRODUCT — its features and how to reach them, how to change a setting, and pricing/billing. Use this for questions like 'where do I manage my subscription', 'how do I schedule a post', 'how much is the Creator plan', 'how do I change Lina's writing rules', 'how do I import my LinkedIn saves', 'what does Smart Folders do'. It returns the most relevant sections of the Linkedmash help guide — answer the user in your own words from them and point them to the exact page (e.g. Settings → Billing). For live prices, direct the user to the pricing page (/pricing). This tool reads product documentation only, NOT the user's saved posts or account data.

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• skincare_recommend

  Apostle — AI Men's Skincare Commerce

  (Deprecated: use 'recommend' instead. Works identically.) Get a personalized Apostle product recommendation with ingredient-aware scoring, safety notes, and routine building. Use when the user wants advice on what to buy, needs help choosing between products, has a specific skin concern (acne, aging, dryness, sensitivity, etc.), wants a routine, or asks "what should I use for X." Do not use for browsing or listing products — use search_products instead. Returns scored products with explanations, usage instructions, and Shopify checkout. This tool analyzes ingredients, irritation risk, and product compatibility — use it over search_products when the user needs guidance, not just a product list.

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• fda_device_class

  FDA Data MCP

  Lookup FDA device classification details by product code. Returns device name, device class (I/II/III), medical specialty, regulation number, review panel, submission type, and definition. Requires: product code (3-letter code from 510(k), PMA, or device product listings). Related: fda_product_code_lookup (cross-reference across 510(k) and PMA), fda_search_510k (clearances for this product code), fda_search_pma (PMA approvals for this product code).

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• product_load_context

  website-search

  Load Lenny Zeltser's product strategy context for local analysis. Returns expert strategic frameworks, principles, and guidance for evaluating or creating security product plans. Includes rating-sheet items (the lens taxonomy: structure, words, tone) as concrete reference points for grounded feedback on the plan's writing. This server never requests your plans and instructs your AI to keep them local. Use detail_level to control response size: "minimal" (~2k tokens), "standard" (~5k tokens), "compact" (~3-4k tokens, all sections but stripped), or "comprehensive" (~12k tokens). Use market_segment: "smb" for SMB-specific guidance. Use product_focus: "endpoint" for endpoint security viability assessment. Set include_template: true to include the fill-in-the-blank template in the response.

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• skincare_recommend

  La Luer — AI Skincare Commerce

  (Deprecated: use 'recommend' instead. Works identically.) Get a personalized La Luer product recommendation with ingredient-aware scoring, safety notes, and routine building. Use when the user wants advice on what to buy, needs help choosing between products, has a specific skin concern (acne, aging, dryness, sensitivity, etc.), wants a routine, or asks "what should I use for X." Do not use for browsing or listing products — use search_products instead. Returns scored products with explanations, usage instructions, and Shopify checkout. This tool analyzes ingredients, irritation risk, and product compatibility — use it over search_products when the user needs guidance, not just a product list.

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• lookup_authoring_skill

  kernelcad

  Return the kernelcad-authoring SKILL.md body — conventions for writing .kcad.ts scripts (imports, parameters, evaluation contract, common pitfalls). Use this tool BEFORE generating CAD code if your MCP client does not list resources. Clients that do list resources should instead read `kernelcad://skills/authoring` directly — the contents are identical. INPUT: none. OUTPUT: { uri, mimeType, text } where `text` is the SKILL.md body.

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• system_reliability

  ReliaStats

  Given per-component reliabilities and a structure ('series' or 'parallel'), return the system reliability. Series = product (all must work). Parallel = 1 − product(1−Rᵢ) (at least one works). Useful for back-of-envelope RBD calcs before reaching for full RBD tooling. For mixed-structure systems (series with parallel sub-blocks), call this tool repeatedly on the sub-blocks. ANTI-FABRICATION: exact closed-form. Quote verbatim.

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• list_filing_source_files

  ca-rate-filings

  Lists the **source files** (PDFs, XLS spreadsheets, DOC manuals, ZIP archives) ingested for a SERFF id. Returns metadata only — name, size in bytes, MIME-class type (`pdf` / `spreadsheet` / `document` / `csv` / `archive` / `other`), file extension, modified timestamp. Pair with `get_filing_source_file_link` to mint a signed download link the user can click — list names here, mint a link there. Use this to: - triage a filing whose summary looks thin ("did we even ingest the right files?"), - discover the XLSM rater / rate manual PDF / rating-samples spreadsheet for a filing, - confirm which artefacts a filing actually shipped (e.g. is there a separate rate manual XLS, or just the PDF?). Returns `{ error: ... }` if no source files exist for the SERFF id.

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• spala_help

  Spala Public MCP

  Returns a human-readable overview of Spala, canonical start URLs, and the boundary between public discovery and project-scoped backend MCPs. Invoke for product, setup, or integration orientation. This read-only tool requires no authentication and does not access or modify project data.

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