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650,529 tools. Updated 2026-10-14 07:08

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

  • 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 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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  • Use first when a buyer is exploring LeadProof without supplying workflow data. Returns the official no-purchase sequence: buyer guide, fictional example audit, browser-only calculator, public workflow tests, and no-card trial. For a scored workflow diagnosis, use audit_lead_workflow; for replay testing, use get_leadproof_replay_trial; for paid checkout, use get_leadproof_checkout only after authorization.
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  • Full-text search over the knowledge graph. Matching ignores accents and apostrophes, so query in the user's own words; every hit carries the fields it matched and a score. BM25 relevance: each query term is weighted by how RARE it is in this corpus and by where it hits (name 3, tags 2, questions 2, body 1). A hit must also cover a minimum share of the question's information, measured in the same rarity weights — matching only common words does not qualify. Centrality (how many objects point at this one) breaks TIES ONLY and is never part of the score, so it cannot make an irrelevant object rank. Two hits with the same matched_fields can still differ: the score is rarity-weighted, so matching a rare term is worth more than matching a common one. Use this whenever you have a question rather than an id, then follow up with get_entity.
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  • Full-text search over the knowledge graph. Matching ignores accents and apostrophes, so query in the user's own words; every hit carries the fields it matched and a score. BM25 relevance: each query term is weighted by how RARE it is in this corpus and by where it hits (name 3, tags 2, questions 2, body 1). A hit must also cover a minimum share of the question's information, measured in the same rarity weights — matching only common words does not qualify. Centrality (how many objects point at this one) breaks TIES ONLY and is never part of the score, so it cannot make an irrelevant object rank. Two hits with the same matched_fields can still differ: the score is rarity-weighted, so matching a rare term is worth more than matching a common one. Use this whenever you have a question rather than an id, then follow up with get_entity.
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  • Full-text search over the knowledge graph. Matching ignores accents and apostrophes, so query in the user's own words; every hit carries the fields it matched and a score. BM25 relevance: each query term is weighted by how RARE it is in this corpus and by where it hits (name 3, tags 2, questions 2, body 1). A hit must also cover a minimum share of the question's information, measured in the same rarity weights — matching only common words does not qualify. Centrality (how many objects point at this one) breaks TIES ONLY and is never part of the score, so it cannot make an irrelevant object rank. Two hits with the same matched_fields can still differ: the score is rarity-weighted, so matching a rare term is worth more than matching a common one. Use this whenever you have a question rather than an id, then follow up with get_entity.
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Matching MCP Servers

  • A
    license
    A
    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.
    13
    MIT

Matching MCP Connectors

  • MakeOAuth

    Build, run and debug Make automation scenarios.

  • A forum whose members are AI agents. Publish verifiable findings, enter scored challenges.

  • Get NCAP 5-Star crash test safety ratings by make/model/year or vehicle ID. Returns overall rating, frontal crash, side crash, and rollover ratings (1-5 stars). Also shows related complaints, recalls, and investigation counts. Available from ~2011 for US-market vehicles (NHTSA)
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  • Calculation, not advice. Verify with a professional before acting. Deterministic mortgage refinance break-even analysis. Given your current loan (balance, rate, remaining term) and a refinance offer (new rate, new term, closing costs, optional points), computes: - monthly P&I savings; - the cash-flow break-even month (Senaro's formula: total refinance cost divided by monthly savings, rounded up to a whole month, built on the payback test the CFPB toolkit describes); - the interest delta over your remaining-term horizon, and each loan's interest over its whole schedule (the current loan's remaining term, the new loan's full term); - a term-matched scenario that isolates the rate cut from a term reset; - a term-reset-trap flag (a longer term and a lower payment, but more interest over the new loan's full term than over the current loan's remaining term); and - the economic break-even (net-worth crossover) month under an equal-outflow model: both sides spend the same each month; the side that keeps the current loan starts with the upfront refinance cost invested at month 0 (nothing when costs are rolled in); money not spent on a payment is invested at investment_return_pct. The search runs to the end of the longer loan term, and stops earlier where a loan's term ends with money still owed. Rate-and-term refis only (cash-out and tax effects are out of scope). Pick this when refinancing your existing mortgage into a new rate and term is the question; pick `compare_mortgage_terms` when comparing two mortgage structures on a purchase you have not yet taken out. All defaults cite primary sources (LodeStar/ALTA closing-cost data, the CFPB toolkit's payback test). Scalar output, no chart series. payoff_months and payoff_month_shift use {status, value, explanation}; the two break-evens use {code, month, explanation}.
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  • Add terms to one storefront's corpus, or take terms out of it. Add for a term the corpus SHOULD hold and does not — a rival's brand, a phrase Apple has never measured the popularity of, the term a thin storefront is really about; a pinned term is kept through every rebuild and refreshed on the same 72-hour cadence as the rest. Remove for a term that does not belong: ANY term can go, and one the heuristic found is also kept out of future rebuilds rather than returning within 72 hours. This is NOT `save_market`: seeds are what the corpus is expanded FROM and changing one re-runs the whole heuristic over that storefront, where these two writes act on single terms in the result.
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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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  • List the exact canonical car makes (brands) TransparentCars can search and price — or, given a make, that brand's models — using the exact strings the other tools expect. Call this FIRST (or whenever unsure of spelling) and pass the returned values verbatim into search_inventory / check_fair_price. No make = the list of brands; with a make = that brand's models.
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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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  • List the exact canonical car makes (brands) TransparentCars can search and price — or, given a make, that brand's models — using the exact strings the other tools expect. Call this FIRST (or whenever unsure of spelling) and pass the returned values verbatim into search_inventory / check_fair_price. No make = the list of brands; with a make = that brand's models.
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  • Build a citability brief for one question: People Also Ask, related queries, cited domains, and an AI Overview check. The SERP is always requested with the AI Overview load, so aiOverviewRequested: true with aiOverview: false means Google serves none for this query, not that we did not look. Costs the user about USD 0.004 (one SERP including the AI Overview load) when the topic already has measured metrics, or about USD 0.116 when it must be measured first. A brief built in the last 14 days is returned from storage for free. refreshMetrics re-measures volume (about USD 0.11 extra); refresh_metrics covers more than one term. The whole call is priced and approved as one plan, so it can come back degraded with reason spend-limit before any request: nothing is charged and no brief is stored. It also refuses with reason archived-topic before estimating any cost when the topic is retired; re-adding the term brings it back.
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    OAuth
  • Free-text search across every public resource family on The Holistic Care (blog articles, mindfulness games, free guided practices, research summaries, glossary terms, and public PanchaVikas resources). Use this as the general entry point when a request doesn't clearly belong to one specific family — e.g. 'Find something about breathing for anxiety' or 'What has THC published about nondual awareness?'. `resource_type`, `category`, `audience`, `skill`, and `age` are applied as a post-filter over the text search results (the underlying search endpoint only supports free-text query natively), so a narrow combination of filters plus a rare query term can legitimately return few or zero results even if matching resources exist outside the searched batch — for a guaranteed, fully server-side filtered result within one resource family, prefer find_mindfulness_games, find_guided_practices, search_mindfulness_research, or lookup_mindfulness_term instead.
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  • 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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  • Research a keyword and related terms. Returns up to 10 rows — the seed keyword plus the store's top autocomplete suggestions — each with difficulty (0-100), popularity score, and results count. Use this to find keywords worth targeting; for metrics on terms you already know, sonar_keyword_metrics is cheaper. On prepaid API credits it costs 10 credits per call (rows that could not be computed are refunded); it needs a Sonar API key or connected account and is not part of the free tier. Answers within about 30 seconds. Each row carries `measured_at` (difficulty) and `popularity_measured_at` (popularity, can be older), ISO 8601 UTC; `stale: true` means at least one of them is older than 7 days. A related term that couldn't be computed in time comes back marked `pending` (queued, not charged) — get it later with sonar_keyword_metrics.
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  • Reads a JPEG or PNG's hidden metadata (JPEG: EXIF — camera make/model, lens, body/lens serial numbers, capture date, GPS coordinates and altitude — plus XMP, IPTC/Photoshop resources and comment segments; PNG: tEXt/iTXt/zTXt/eXIf/tIME chunks) and returns a structured report of exactly what it found, including the size of every metadata segment/chunk that was removed. Optionally (include_cleaned_image, default true) also returns the same image with those segments/chunks stripped at the byte level: no re-encoding and no pixel decode, so the compressed image data, ICC profile, and JFIF/PNG structure are copied unchanged. It runs a byte-for-byte self-check that the retained image data is actually unchanged before handing back a cleaned file — if that check fails, no cleaned file is returned even though the report is still produced. Only JPEG and PNG signatures are recognized (not TIFF or other formats); stripping a photo's EXIF Orientation tag can make it appear rotated, which the report flags.
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  • Retrieve the full metadata record for a specific LOC digital item. Returns contributors, subjects, summary, languages, locations, rights information, physical description, call number, original and online formats, access restrictions, former catalog IDs, notes, related items, and links to digital resources (TIFF, JPEG, PDF) for items with digital surrogates. Use after libofcongress_search on a result whose is_item is true. Pass the result id verbatim — it may be a simple ID or a slash-separated newspaper path; do not prepend the loc.gov URL. Non-item results (is_item: false) — collections, exhibits, guides, newspaper pages — have no item record and cannot be retrieved here.
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  • Add (or update) glossary entries for a project in one call. Pass an array of term rows, each with its own source and target term and their project_language ids — so a single source term can have a different translation per target language. Resolve the language ids first with get_project. Entries are upserted on (project, sourceTerm, source, target); re-adding an existing key updates its target term and its do-not-translate flag. Set doNotTranslate on a row for a term that must be kept verbatim (a brand or product name); its stored target term is then forced to equal the source term. Both terms are trimmed of leading and trailing whitespace before they are checked and stored; a source term empty once trimmed is refused. Marks any matching translation-memory entries stale so future translations pick up the change. When referring to a project or language in your reply to the user, use its name and locale (e.g. "French (fr-FR)") — ids (UUIDs) are for tool calls only, never show them to the user.
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  • Make one mixed Hebrew/English display line render correctly: every left-to-right unit (a Latin term, a price, a number with its unit, a range such as "10–20%", a date, a comparison such as "<5") is kept whole and in order, so nothing is reversed or mirrored inside a Hebrew line. `format`: `html` (dir spans, for web and React), `isolate` (Unicode isolates, for iOS, Android 7+ and browsers) or `marks` (LRM/RLM, for any renderer including older Android). Pass `base: "rtl"` for a Hebrew line that starts with a Latin term. Deterministic and free: no character quota.
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