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467,900 tools. Updated 2026-08-22 09:51

"Exploring the term 'Make' or related resources" matching MCP tools:

  • 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 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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  • Look up the 99 Names of Allah (Asma ul Husna). Returns Arabic, transliteration, English and Bengali. Give a number for one name, a search term to match by meaning or transliteration, or neither to get all 99.
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  • 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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  • Fuzzy text search across route names, descriptions, and category labels. Resolves natural-language queries like "electricity retail sales by state" or "natural gas imports" to matching route paths. Multi-term queries are also matched term by term, so combining a commodity, a metric, and a sector — "electricity price residential", "coal generation industrial sector" — reaches the route carrying that data even when no single entry reads like the whole phrase. STEO series names are indexed so queries like "ethanol net imports" or "crude oil production forecast" also resolve, and so are facet values, so a fuel type or sector term like "wind" or "anthracite coal" resolves to the route that exposes it, with filter_hint carrying the filter to pass on. Results include isLeaf so you know whether to browse further or query directly. Results with score > 0.72 are weak matches — try a more specific query or use eia_browse_routes to explore the taxonomy. The first call after server start waits 24-30s while the index warms, and at most 45s; every later call returns in milliseconds. Check indexComplete before reading anything into a short or empty result set.
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  • 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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Matching MCP Servers

  • A
    license
    Not graded
    quality
    B
    maintenance
    Enables AI agents to query OpenRouter model information including prices, ELO rankings, context, and perform comparisons.
    445
    1
    MIT
  • A
    license
    Not graded
    quality
    D
    maintenance
    A persistent, self-organizing memory MCP server for AI assistants, using semantic search, knowledge graphs, and reinforcement learning to automatically manage and retrieve memories.
    MIT

Matching MCP Connectors

  • Still losing time to small decisions? Spin or Flip brings randomization into Claude so you can offload mental load to chance instantly.

  • the-committee MCP — wraps StupidAPIs (requires X-API-Key)

  • Searches and lists already-published IBGE news articles and press releases. Use this to find recent IBGE publications or announcements about a survey or topic — when an indicator was released, or news mentioning a term like "censo". Results are sorted newest-first; with no parameters it returns the 10 most recent items. Parameters: - busca: free-text term to match (e.g. "PIB", "censo") - tipo: "release" (official publication of survey results) or "noticia" (general news); omit for both - de / ate: date range, format DD/MM/AAAA (e.g. de="01/01/2024", ate="31/12/2024") - destaque: true to return only featured items - quantidade: how many to return (default 10, max 100); pagina: page number to page through more Each item returns: title, type (release/news), publication date, editoria (section), related products/surveys, a featured flag, a plain-text summary, and a link to the full article. The header reports the total count and current page. Examples: - Latest 10 news: (no parameters) - Search census: busca="censo" - 2024 news: de="01/01/2024", ate="31/12/2024" - Releases only: tipo="release" Use a different tool when: - Scheduled/upcoming release dates (not yet published) → ibge_calendario Behavior: read-only and idempotent — a live GET against the public IBGE Notícias API. Returns a Markdown list.
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  • Compile one callable third-party API brief: base URL, auth scheme, required parameters and types, request body, and documented response codes. Service is required and endpoint optionally narrows the operation. Set responseFormat="compact" for tokenizer-measured context savings; the backward-compatible default returns the full brief plus compact form. Uses metered access. Prefer factreason_api_schema when exploring multiple endpoints.
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  • BROWSING / DISCOVERY search — cities, neighbourhoods, or mixed venues near a location. Use this when the user is exploring a REGION rather than looking for a specific category. Supports population filtering ('cities > 100k'), distance/population sorting, and layer filtering (locality / neighbourhood / venue / address / street). For specific POI categories (gas, food, charging, etc.), use `search_places` instead.
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  • 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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  • 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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  • 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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  • 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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  • Pure keyword (BM25) search — fastest option, optimal for exact-term lookups: paper titles, author names, method names (e.g. "LoRA", "RLHF"), arXiv IDs. Does NOT use semantic vectors. Use this when you know the specific term you're looking for. For paraphrased or conceptual queries, prefer "search_semantic" or "search".
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  • 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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  • 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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  • Compile one callable third-party API brief: base URL, auth scheme, required parameters and types, request body, and documented response codes. Service is required and endpoint optionally narrows the operation. Set responseFormat="compact" for tokenizer-measured context savings; the backward-compatible default returns the full brief plus compact form. Uses metered access. Prefer factreason_api_schema when exploring multiple endpoints.
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  • List the env vars a project's code can use and the resources behind them: (1) resources CONNECTED to the project — usable as process.env.<NAME> in endpoint code now; (2) the owner's other account-level credentials — reusable, but not usable in code until connected; (3) everything Floot can add. Call it to learn what env vars exist before writing backend code, and BEFORE provisioning or requesting any credential (the owner may already have the one you need). Pass query (case-insensitive substring over names, descriptions, types, and env var names) to filter when the account has many resources. Read-only. Details: get_guides('resources').
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  • 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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  • Store a long-term memory about the household. Use sparingly for durable preferences, routines, constraints, or insights worth recalling in a future conversation. Recall first to avoid duplicates.
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