"Exploring the term 'make' and related resources" matching MCP tools:

• smithsonian_browse_category

  smithsonian-mcp-server

  Browse Smithsonian objects within one exact category — a single museum (mode "museum"), culture, indexed date term (mode "period"), object type (mode "medium"), or subject term (mode "topic"). The value must be an exact indexed category term, not free text: resolve museum, culture, period, and topic vocabulary with smithsonian_list_terms first (object_type is not enumerable there — harvest it from smithsonian_search_objects results, and treat each casing as its own category, since a harvested object_type covers only the casing it was written in). Returns the category total count, a page of matching objects, and a museum breakdown of that page; page the full category with start and rows. For open-ended or topic discovery, start with smithsonian_search_objects instead.

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• find_cocktails_by_ingredient

  Cocktail Glass

  Find every cocktail in the catalogue that uses one specific ingredient. Matching is a case- and diacritic-insensitive substring match against each cocktail's ingredient names, so "gin" will also match "sloe gin" and "ginger beer" — use a more specific term if that matters. Returns up to 60 summary results (name, URL, family, glassware) in catalogue order. Takes one ingredient only; for "what can I make from X, Y, and Z?" use find_makeable_cocktails instead, which handles multiple ingredients and reports near-misses.

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• get_vehicle_details

  FINN Auto - Car Subscription

  Get full specifications, equipment, all images, and pricing per term for a specific vehicle. Use a vehicle_id from search_vehicles results. IMPORTANT: Always show `detail_url` as a clickable link — it points to the FINN configurator where the user picks term and km. To produce a direct checkout link for a specific term + km combination (and optionally a one-time Fahrzeugbereitstellung), call `get_subscription_pricing` and use the `checkout_url` it returns. Never construct checkout URLs yourself. The `vehicle_id` field is an internal API identifier — never display it to users.

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• well_get_entity

  Well

  Read ONE entity with its sub-resources nested in a single call. Convenience over well_get_schema + well_query_records: resolves the field paths for you and returns the single record with its related data expanded. depth (relation-nesting BOUNDARY, 1-3, default 1): 1 = the entity + its direct sub-resources (emails, phones, locations, …) 2 = + the sub-resources' related scalars 3 = the full level-3 graph (LARGER payload — use when you need the whole picture) Stops at depth 3. Aggregates are excluded. Each child collection is capped at 50 rows; for a full list or to page a large child collection, use well_query_records on that child root instead.

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• find_makeable_cocktails

  Cocktail Glass

  Given the ingredients you have on hand, find every cocktail you can make completely — one where you already have all of its ingredients. Garnishes are treated as optional and plain water is assumed available; soda and tonic water are not. Matching is word-based, not substring: "gin" matches "London dry gin" but not "ginger beer", and generic terms do not match product-class extras ("gin" will not cover "sloe gin" or "orange bitters"). Returns two lists: "makeable" (drinks you can make now, up to 60) and "almostMakeable" (drinks exactly one ingredient short, up to 25, each naming the missing ingredient). Drinks needing two or more extra ingredients are omitted entirely. Both lists are ordered simplest first — fewest distinct ingredients in the full recipe, then alphabetical by name. Use this for multi-ingredient "what can I make?" questions; for a single ingredient use find_cocktails_by_ingredient.

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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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• string_proteins_for_term

  STRING Database MCP Server

  Retrieve proteins annotated with a functional term or descriptive text in a single species. You can query for tissues, compartments, diseases, processes, pathways, and domains. IMPORTANT: For cross-species comparisons, run this tool separately for each species. Select relevant model organisms to search or ask user to provide the selection. The results reflect annotation depth within each category; use caution when interpreting. If no results are found, try simplifying the query. For tissue queries, follow BRENDA tissue nomenclature and omit the word "tissue" (e.g. use "skin" instead of "skin tissue"). Output fields: - category: Source database of the matched functional term (e.g. GO, KEGG, Reactome, Pfam, InterPro). - term: Exact identifier for the functional term. - description: The free text description of the term. - proteinCount: Number of proteins annotated with that term - preferredNames: Full protein-name list when `detail_for_term` is set - stringIds: STRING protein identifiers when returned - preferredNames_omitted: True when a row omits the protein-name list - stringIds_omitted: True when STRING identifiers are omitted

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• keyword_demand

  amazon-product-research-mcp

  Keywords observed in Amazon's own autocomplete suggestions for a seed term, per marketplace: the current suggestion list(s) for the seed's prefix (each term with its position 1-10 within that list) plus related observed vocabulary starting with the seed, with the marketplaces each term was observed in. Use for listing/backend keyword language, 'what do buyers type for X', or seeding niche/product research with real buyer phrases. No volume figures and no organic-ranking data — observed suggestion vocabulary only. Amazon marketplaces only.

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• paywall__customer_deduct_from_fup

  Netfluid

  Deducts a unit from the customer's available workflow allowance. Do not call this tool when the customer is requesting assistance related to the pay wall and its subscriptions. This tool should be called after each AI response that is not pay wall related. @param customer_id: The customer's database id @return: a json object, containing the customer_id and remaining fup token balance in the "values" object

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• lookup_vehicle

  Chaiz MCP

  Fuzzy-match a vehicle by make (and optional model/year) to confirm it exists and correct spelling before a plan search. Use this when the user's make/model looks misspelled or uncertain.

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• collection.create

  DocImprint

  Create a named document collection for cross-document semantic search and RAG-based Q&A. Free — no credits consumed. Use when you want to group related evidence bundles for unified search (collection.search) or question answering (collection.ask). NOTE: Collections start empty. Add evidence bundles with collection.add_document. Indexing is async — once complete, use collection.search or collection.ask. Returns: { collection_id: string (col_...), name: string } Example prompts: - "Create a collection called Q4 Contracts for my quarterly reports." - "Set up a new document group named Due Diligence Docs." - "Make a collection to organize my vendor agreements."

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• get_glossary

  VouchedPros

  Plain-language definitions of industry terms in a service category (e.g. SEER2, AFUE, AHRI match). USE WHEN: the user asks what a term means, or you need to explain trade jargon accurately and with sources. ARGS: `category`; optionally `term` (a slug) for one definition — omit to list. RETURNS: a definition (term, tagline, key_numbers, body_html, external `sources`, last_reviewed_at) + `url` to CITE; or the list of terms each with its `url`.

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• search_terminology

  NewTqnia MCP – Technology News & Research

  Searches terminology by English term, Arabic term, abbreviation, or slug using normalized, case-insensitive matching. Administrators see draft and published terms with both languages, and should call this before creating a new term to avoid duplicates. Other accounts see published terms only, in a single locale (pass the caller's language in locale), each with a canonical URL to the full definition.

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• get_timeline_by_id

  newtqnia

  Returns one published timeline. Administrators get the complete bilingual record with every event, source, and related link, plus access to draft content. Other accounts get a single locale (pass the caller's language in locale): each event's title, summary, media, sources, and related links, plus a canonical URL to the full timeline - never event bodies or the timeline introduction/conclusion.

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• ics2_check

  FreightUtils MCP Server

  Check a goods description against the official EU ICS2 stop-words list — terms the European Commission deems too vague or generic for an entry summary declaration (ENS) goods-description field (data element 18 05 000 000). Pass description=<goods description>. Behavior: deterministic term matching against the in-force EU list; each flagged term carries a note (a standalone stop-word means automatic rejection, an embedded one means make the description more specific); clean=true means no listed term matched — it does NOT guarantee acceptance, and no binary accepted/rejected verdict is given. Rate-limited (anonymous use: 25 requests/day per IP): a 429 error body carries retry_after_seconds and a Retry-After header — back off and retry, or call get_subscribe_link for higher limits. Returns: the description echo, flagged[] (term + note), clean, caveat and disclaimer under result, plus a _source citing the EU list and legal basis, plus confidence, _source and citation (the FreightUtils v1 response envelope). Limitations: STRICTLY a reference check — not an ENS filing, not a customs-compliance determination, not legal advice; the EU list is non-exhaustive and updated periodically. Related: hs_code_lookup (commodity codes — a different field of the ENS), uk_duty_calculator (duty/VAT, unrelated to ENS screening). Use BEFORE filing an ENS — for customs/documentation teams, brokers and agents building filing pipelines.

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• autonomous_varrd_ai

  VARRD — Statistically Validated Trading Edges + AI Research Engine

  Point VARRD's autonomous AI in a direction and let it discover edges for you. Give it a topic and it draws from one of the most comprehensive market structure knowledge graphs ever built — containing ideologies and theories, not statistics — so it generates genuinely novel hypotheses rather than overfitting to what already worked. BEST FOR: Exploring a space broadly. Give it 'momentum on grains' and it might test wheat seasonal patterns, corn spread reversals, or soybean crush ratio momentum. It propagates from your seed idea into related concepts you might not think of. Returns a complete result — edge or no edge, stats, trade setup. Each call tests ONE hypothesis through the full pipeline (~$0.25/idea). Call again for another idea. Use 'varrd_ai' instead when YOU have a specific idea to test and want full control over each step.

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• recall_screen

  LiveDataLink

  One-call product-safety recall sweep across CPSC (consumer products), openFDA (drug/device/food enforcement), and NHTSA (vehicles). Provide a product/keyword/manufacturer query and/or a full vehicle (year+make+model). Results are normalized, deduped within and across sources, severity-rolled (FDA Class I or death-related = high), and summarized with a by-classification breakdown. A source that fails is noted, not fatal. Premium cross-source synthesis. Verify against the official sources before acting.

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• employer.create_job

  workorai

  Parse raw text via Gemini AI and create a DRAFT job under the current employer. Synchronous: latency 5-30s. The agent can then call employer.update_job to refine fields and employer.publish_job to make it live.

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• fda_faers_signal_summary

  Openfda

  Calculate report-level FAERS disproportionality routing metrics for one drug/reaction pair using a 2×2 reporting table. The reaction argument matches one whole MedDRA preferred term, so a broad word like "neuropathy" counts only reports filed under that exact term and not the specific terms containing it. Natural multi-word phrasing is resolved to MedDRA word order and disclosed; a term matching nothing is reported as unresolved rather than as zero reports. ROR/PRR are screening statistics—not incidence, causality, comparative drug safety, or an FDA safety conclusion.

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• smithsonian_find_related

  smithsonian-mcp-server

  Discover objects across Smithsonian collections related to a given anchor object, matched on shared metadata signals — culture, period, object type, named parties, and topic terms. Each related object is tagged with the signals that connected it to the anchor; a named-party signal carries the catalog's own role for that party (maker, Collector, Donor, issuing authority, …), not a fixed "maker" label. Matches surface across museums — an NASM aerospace anchor can pull related objects from NMNHPALEO, SAAM, and NMAH.

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