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725,336 tools. Updated 2026-10-30 14:29

"Virgin Atlantic" matching MCP tools:

  • Virgin Atlantic Flying Club Virgin Points and Tier Points for Virgin Atlantic flights. Virgin Points = distance x booking-class % (J, C, D 500; I, Z 300; W, S 250; H, K 175; V, Y, B, R 150; L, U, M 100; Q, X, N, O 50; T 25) x tier (Red 1, Silver 1.3, Gold 1.6), rounded per flight; Tier Points are fixed per flight by class (25 to 200). The reward-only classes G, P and A, and E (no published rate), are not offered. Progress toward Silver (400) and Gold (1,000) Tier Points; tp is the Tier Points so far. Give segs: one entry per one-way flight, in order, with its great-circle miles (calculate_flight_distance gives them) and the ISO country codes of its two airports. Takes no price. Uses cwsi.net's calculator and the airline's published rules. Unofficial.
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  • Query the MISO generator interconnection queue — the public waiting line of projects that have REQUESTED to connect to the MISO grid (the 15-state Midwest/South footprint). Returns cited, project-level records: requested megawatts (net summer / net winter), location (`state`, `county`, derived `county_fips`), fuel and technology as MISO reports them, three independent status dimensions (`application_status`, `study_phase`, `post_gia_status`), and queue / withdrawn / in-service dates. Group or filter by `state`, `county_fips`, `application_status`, `study_phase`, `post_gia_status`, `fuel_type`, `facility_type`, `service_type`, `study_group`, `study_cycle`, or `is_hybrid`; filter `queue_date` by the `queue_date_from` / `queue_date_to` range. Pass each parameter as a top-level key of `params` (flat — not nested). Example: `{"state": "IN", "fuel_type": "Solar", "application_status": "Active"}` for active solar requests in Indiana; `{"group_by": ["application_status"]}` for requested MW and project counts by status. Returns JSON aggregates with citations and optional row-level records when `include_records` is true; every value carries `source`, `as_of`, and a `source_row` verifiable with get_source_evidence_v1. This is REQUESTED capacity, not built: historically the large majority of queued megawatts withdraw before they are built. NEVER read a requested-MW total as installed or operating capacity — it is additive across distinct projects but is a REQUESTED total only. Filter `application_status` (Active / Withdrawn / Done) to scope the queue; the full export is withdrawn-dominated. For built/operating capacity use query_power_capacity_v1. MISO only — never summed, deduped, or compared across ISOs into a national total (each ISO's methodology, inclusion rules, and withdrawal rates differ). For the PJM interconnection queue (the mid-Atlantic RTO incl. Northern Virginia) use query_power_interconnection_queue_pjm_v1 (or query_power_interconnection_queue_pjm_cycle_v1 for PJM's new cluster/cycle process incl. the reopened Cycle 1); for the CAISO (California) queue use query_power_interconnection_queue_caiso_v1; for the NYISO (New York, incl. load interconnection requests) queue use query_power_interconnection_queue_nyiso_v1; for the ISO-NE (New England) queue use query_power_interconnection_queue_isone_v1; for the ERCOT (Texas) queue use query_power_interconnection_queue_ercot_v1; for the SPP (central US) queue use query_power_interconnection_queue_spp_v1 — separate ISO blocks, never combined with this one. MISO reports no data-center / load type, and this tool does not infer one — that interpretation is the analyst's, from cited rows.
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  • Purpose: return the rCB and virgin carbon black price benchmark structure - the virgin N550 series as anchor, the rCB band expressed as 25-40% of that anchor, feedstock prices, international references, regional spreads, grade-level bands and cost structure. Guidelines: leave product and section empty for the full set; narrow with section='rCB' / 'feedstock' / 'international' / 'cost'; pair with get_elt_price_index for feedstock indices and get_rcb_index for the layered index series. Limits: reference bands and relative structure only - no exchange quote and no trading recommendation; ELT feedstock absolute prices are deliberately not published; every figure is a range, never a single transactable value. Ex: get_price_benchmark(section='rCB'), get_price_benchmark(product='N330', language='en'), get_price_benchmark(section='cost', response_format='json').
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  • Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a `trending_scan` of the top ~200 markets by weekly volume; pass `event` for the strongest per-event partition_check, or `topic` for a themed cross-event scan. `event` (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). `topic` (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FEES: every opportunities[] row and partition_check.arbitrage carry edge_pp_gross (== gap_pp / overround_pp), fees_pp, edge_pp_net, net_positive, plus polymarket_fee_pp, fee_basis and fee_categories[]. BOTH cost components are modeled: Polymarket's own per-category TAKER FEE (fee = shares × rate × p × (1-p), rates crypto 0.07 / sports-economics-culture-weather-other 0.05 / finance-politics-mentions-tech 0.04, geopolitics and world events fee-free; verified against Polymarket's own docs as of 2026-09-13) and Polygon gas (~$0.02/leg). The taker fee dominates: ~$1.75 per 100 shares on a crypto market at 50c versus $0.02 of gas, so rows that looked profitable before fleet #1927 may now show net_positive:false — that is the correction, not a regression. Each leg is priced at ITS OWN market's rate and price (the fee curve peaks at 50c and falls toward both extremes). fee_basis says where the rate came from: 'payload' (read off the market, the normal case), 'category' (mapped from its fee category), 'fee_free', or 'fallback' (rate unknown — charged at the modal 0.05 rather than assumed free, so an unreadable market is never reported as costless). Where fill_check reprices against live depth, this does NOT double-count that spread cost. FILL CHECK: when the partition signal fires, arbitrage.fill_check prices it against live CLOB depth (theoretical_edge_pp_at_book vs realizable_edge_pp at 1000 shares/leg, thin_legs[]) — realizable_edge_pp ≤ 0 means the overround exists only at last-trade, not in the book; do not trade it. For custom sizing use polymarket_fill_risk.
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  • Query the PJM New Services (interconnection) queue — the public waiting line of projects that have REQUESTED to connect to the PJM grid (the mid-Atlantic RTO incl. Northern Virginia, PA, NJ, MD, OH, VA, WV and more). Returns cited, project-level records with PJM's full published structure: requested megawatts (`requested_max_output_mw` = MFO, `requested_summer_mw` = MW Capacity / summer net, `requested_winter_mw` = MW Energy / winter net), the realized built `in_service_mw` for completed projects, location (`state`, `county`, derived `county_fips`), `fuel` and `project_type` as PJM reports them, the single as-reported `status`, the study-document URLs and per-stage statuses, and lifecycle dates. Group or filter by `state`, `county_fips`, `status`, `project_type`, `capacity_or_energy`, `fuel`, `project_ac_dc`, `transmission_owner`, the study statuses, or (group only) `is_hybrid`; filter `submitted_date` by the `submitted_date_from` / `submitted_date_to` range. Pass each parameter as a top-level key of `params` (flat — not nested). Example: `{"state": "VA", "project_type": "Generation Interconnection", "status": "Active"}` for active generation requests in Virginia; `{"group_by": ["status"]}` for requested MW and project counts by status. Returns JSON aggregates with citations and optional row-level records when `include_records` is true; every value carries `source`, `as_of`, and a `source_row` verifiable with get_source_evidence_v1. The `requested_*` figures are REQUESTED capacity, not built: historically the large majority of queued megawatts withdraw before they are built. NEVER read a requested-MW total as installed or operating capacity — it is additive across distinct projects but is a REQUESTED total only. Filter `status` (Active / Withdrawn / In Service / Under Construction / …) to scope the queue; the full export is withdrawn-dominated. PJM also reports `in_service_mw` — the realized BUILT MW for in-service projects (a separate, built figure). For built/operating capacity use query_power_capacity_v1. PJM only — never summed, deduped, or compared across ISOs. For the MISO interconnection queue use query_power_interconnection_queue_v1; for the CAISO (California) queue use query_power_interconnection_queue_caiso_v1; for the NYISO (New York, incl. load interconnection requests) queue use query_power_interconnection_queue_nyiso_v1; for the ISO-NE (New England) queue use query_power_interconnection_queue_isone_v1; for the ERCOT (Texas) queue use query_power_interconnection_queue_ercot_v1; for the SPP (central US) queue use query_power_interconnection_queue_spp_v1. For PJM's NEW cluster/cycle process — the TC1/TC2 transition cycles and the reopened steady-state Cycle 1 (C01+), with cycle/stage/developer detail — use query_power_interconnection_queue_pjm_cycle_v1 (a separate PJM publication; never combined with this one). PJM reports no data-center / load type, and this tool does not infer one — that interpretation is the analyst's, from cited rows.
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  • Tide predictions or station discovery at a coordinate (latitude, longitude). Default: predictions from the nearest NOAA station; outside US waters falls back to a global modeled-tide source (forecast horizon ~15 days — longer windows are truncated, not rejected). Set stations_only=true to discover stations (capped by limit). Tune the window with begin_date (YYYYMMDD) + range_hours, the reference with datum, and output units (metric/english). The modeled path reports range_m (peak-to-trough). Across most of the Mediterranean this is under 0.3 m, i.e. negligible for recreational navigation — the response says so explicitly, and you should not present such values as an actionable tide window. Tides matter on the Atlantic coasts, in the Gulf of Gabès, the northern Adriatic, and in the tidal streams of the Messina and Gibraltar straits. Example: latitude=37.77, longitude=-122.42. Chain nausika_geocode for place→coordinates; pair with nausika_marine_forecast.
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  • Prices Kalshi daily high-temperature markets against the NWS forecast for the market's OWN settlement station, and measures whether that forecast actually beats the market. Two modes. LIVE (default): returns the full strike ladder for one city and settlement date with market_prob (mid), forecast_prob, and edge_pp per strike, plus the settlement clause verbatim. BACKTEST (`backtest_days: N`): scores an archived gridded forecast against the market on settled days and returns brier_market vs brier_forecast with a plain-English `verdict`, so the edge is MEASURED rather than asserted. READ THE WARNINGS — they are not boilerplate. (1) These markets DO NOT settle on the NWS. They settle on The Weather Company (weather.com) at a Kalshi station code such as CLINYC, which the response quotes verbatim; so part of every edge_pp is NWS-vs-Weather-Company disagreement about the same day at the same station, which is not mispricing and not tradeable. `settlement_vs_forecast_basis_f` from backtest mode is that part as a number. (2) The station is DERIVED from the settlement clause, never from the city name: Chicago settles at MIDWAY and New York at CENTRAL PARK, so a city-centre forecast would misprice a whole ladder. A station that cannot be resolved yields rows with no forecast and a reason, never a guessed coordinate. (3) forecast_prob assumes a normal distribution around the NWS high whose width is ASSUMED, not fitted (stated in `distribution_assumption`) — run backtest mode to see whether it is calibrated. (4) edge_pp is gross: no Kalshi fees, no bid-ask. MEASURED RESULT, AND IT IS NOT THE FLATTERING ONE: on the first backtest (KXHIGHNY, 13 settled days to 2026-09-11, 58 market observations) the MARKET beat the forecast — Brier 0.1008 for the market against 0.1594 for the archived gridded forecast, lower being better. So on that sample there is NO forecast edge to sell, and a large edge_pp is more likely to be the model disagreeing with a better-informed market than an opportunity. The measured settlement-vs-forecast basis was 1.7F mean absolute over 8 pinnable days, slightly warm-biased, which is a big share of a typical edge_pp on a 2-degree bracket. Re-run backtest_days before believing any edge; if a later sample reverses this, the numbers say so. NWS is US-only, so the ~30 international Kalshi weather series (London, Paris, Tokyo) return market prices with forecast_unavailable rather than a forecast. Precipitation series are listed but not yet priced. Cities: nyc, chicago, los angeles, miami, austin, houston, denver, philadelphia — or pass `series_ticker` for any other (e.g. "KXHIGHTBOS").
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  • Claygent-style per-row research: answer ONE question about ONE company and return a JSON object matching your output_schema, with every field wrapped in its own evidence — {value, evidence_url, quote, status}. status is NEVER self-reported by the model: "found" means this code independently verified the model's quote is a VERBATIM substring of text actually fetched; "inferred" means a value was given but the quote could not be verified; "not_established" means no source answered the field, and its value is always null — never guessed, never a blended score. Sources: "web" fetches the company's own site (entity.domain required — homepage, /about, /leadership) via fetch_url; "catalog" checks entity_profile (SEC EDGAR; resolved:false for a private company is expected, not a failure); "both" (default) tries both. max_cost (USD, default 0.02, max 0.50) scales how many pages are fetched and refuses up front if it is too small to pay for even one extraction call (floor $0.002) — never silently overspends. A row where every source comes back empty costs nothing: every field is mechanically not_established with NO model call made. For a boolean/derived field (e.g. "does this company sell to healthcare"), the quote only has to SUPPORT the value — a descriptive sentence about customers/products counts, the source need not state the answer verbatim. leadership_page_status ("fetched"|"not_found"|"not_attempted", plus leadership_note) says plainly whether a leadership/team page was actually read, so a named-person field (head_of_sales) coming back not_established is distinguishable from "this company just doesn't publish one" vs. "we never checked". model_output_empty:true means the model returned nothing usable (a transport/budget problem) — every field is not_established for THAT reason, never read as 20 individual "no" judgments. Use with a fixed question + schema across many rows for "Claygent over N companies with a hard cost cap" (composes with batch callers). For picking from a fixed label list instead of extracting open fields, use classify_row (cheaper, one model call).
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  • Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). ONLY for tools served by this Pipeworx connection — if the tool came from a different MCP server in your client (another vendor's Gmail, Splunk, Slack, etc. connector), we cannot fix it and reporting it here only delays you; file it with that server instead. Not sure? Pipeworx tool names are the ones this connection lists. Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. Filing without an account returns a `claim_token`; pass it back later as pipeworx_feedback({claim_token:"pwfb_…"}) to read whether it was fixed and what changed. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.
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  • Search 26 completed seasons of NOAA NHC HURDAT2 best-track history (2000–2025), Atlantic and eastern/central Pacific. Includes tropical cyclones, not only hurricanes. Filter by basin, year range, name, storm ID, hurricane strength or recorded landfall. Paginated summaries by default; includeTrack=true returns UTC positions, sustained wind kt, pressure mb, status, special-event flags and 34/50/64 kt wind radii in nautical miles ordered NE, SE, SW, NW. Missing measurements are null. Landfall flags cover any coastline, not specifically the US. Finalized retrospective tracks are not forecasts; current 2026 advisories use get_hurricanes. Historical tracks alone do not measure local damage or future risk. Units: sustained wind kt; pressure mb; coordinates degrees; UTC track timestamps; wind radii nautical miles ordered NE, SE, SW, NW. Missing measurements are null. Returns data and source/freshness metadata.
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  • "Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + operating income + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.
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  • "Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. Company-financial claims (revenue, net income, cash for public US companies) verify via the structured SEC EDGAR + XBRL fast path with exact percent-delta math; ANY OTHER factual claim (macro statistics, rates, prices, drug data, records) automatically falls through to the grounded pipeline — routed to the right live source, answered with verbatim evidence, then judged. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported / could_not_verify), the grounded or structured actual value with pipeworx:// citation, and reasoning. IMPORTANT for callers: could_not_verify means the check did not happen (our LLM or source failed) and carries verification_error{stage,detail} — it is NOT evidence for or against the claim, and must not be shown as one. unsupported means we looked and cover no source for it. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).
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  • TYPED, DETERMINISTIC financial facts for a US public company for an EXPLICITLY NAMED reporting period — "Apple revenue for fiscal 2023", "Walmart net income FY2026 Q3", "Microsoft cash at the end of fiscal 2024". PREFER OVER entity_profile / get_company_financials whenever the period matters: those answer "the most recent figures" and will happily hand back FY2025 when you asked about FY2019, and neither separates a discrete quarter from a year-to-date figure. This one refuses instead — it NEVER substitutes the latest period for the period requested, NEVER returns 0 for missing data, NEVER lets a 9-month YTD number answer a quarterly question, and NEVER converts a currency. Every answer carries the exact us-gaap concept it came from, what that concept MEASURES (NetIncomeLoss excludes non-controlling interests, ProfitLoss includes them — not synonyms), the accession number and a link to the filing on sec.gov, the restatement trail of any superseded figures, and a contract + derivation version to pin against. Fiscal periods are the FILER'S OWN, anchored on their fiscal-year end, so Walmart's year ending 2026-01-31 is FY2026 and Apple's ending 2025-09-27 is FY2025. Attributes: revenue, net_income, cash, gross_profit, operating_income, eps_diluted, capex, operating_cash_flow, long_term_debt, shares_outstanding (the dei COVER-PAGE share count, so its period end is the cover date, after the quarter closed — that is the most current count the filing carries). Units are not all USD: shares_outstanding is in `shares` with currency null, eps_diluted in `USD/shares`. A discrete quarter the filer never filed — every Q4, and Q2/Q3 of cash-flow lines, which are filed year-to-date — is DERIVED as YTD(q) minus YTD(q-1) and comes back with `reported: false`, the subtraction named in `derivation` (fy_minus_9m / ytd_9m_minus_6m / ytd_6m_minus_3m) and both legs with their filings in `derived_from`; test `reported` if you will only accept a figure lifted from a filing. Per-share and balance-sheet figures are NEVER derived. Every non-answer is a named status — `unavailable` (the filer did not report it for that period; the periods that DO exist are listed, without values), `unsupported` (outside what v1 covers — a non-us-gaap filer, an unknown attribute, a non-USD unit), `ambiguous` (the company name matched two filers equally well; both are named), `conflicting` (two filings the same day disagree; both are returned and neither is picked), `partial` (a value with no accession behind it). Source: SEC EDGAR, XBRL financial facts, one publisher read once — see `corroboration`. Same response is served at POST https://gateway.pipeworx.io/v1/facts for non-MCP callers.
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  • Search the NATIONAL BUSINESS REGISTERS by company name and get the register's own id back — a Companies House number, a Norwegian organisasjonsnummer, a Finnish business id, a SIREN, a Japanese corporate number, an Estonian registry code, a CNPJ, or an LEI. Use this when you want the OFFICIAL REGISTERED ENTITY and the country is not the US, or when you do not know which country it is. Registers searched: Companies House (GB), Enhetsregisteret (NO), PRH/YTJ (FI), Sirene (FR), 法人番号公表サイト (JP), RIK e-Business Register (EE), and the GLEIF LEI crosswalk, which reaches jurisdictions we have no register pack for (DE, SE, AU, SG...). THIS TOOL REFUSES TO GUESS, and that is its point: when two different companies match a name equally well it answers `match_status: "ambiguous"` with BOTH, rather than naming one. A one-word trading name usually IS ambiguous — "Bolt", "Apple", "Next", "Sea", "DNB" each match a well-known group AND unrelated micro-entities whose registered name is exactly that word — so pass `country` when you know it. Every response names EVERY register it asked under `searched[]`, with that register's own as-of date and row count, and a register that could not be reached carries an `error` there rather than reading as zero rows: "we could not ask Companies House" is never the same answer as "this company is not in Britain". `related[]` holds entities that collapsed INTO the match — the same company seen through another register, or another country's arm sharing its LEI — so a multinational returns one answer rather than tying with itself. NOT A PEOPLE SEARCH: sole traders and other natural persons are excluded on the publisher's own legal-form field, never guessed from the shape of a name. For US public companies prefer resolve_entity (CIK + ticker + FIGI); the two share this same matcher, and resolve_entity returns these register fields too.
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  • What did a web page say on a given date? Finds the archived capture of a URL nearest that date across the Internet Archive Wayback Machine, Common Crawl and Arquivo.pt, reads it, and returns the readable text (HTML stripped) with the capture timestamp, a link to the archived copy, and the GAP between the date asked for and the capture actually used (e.g. "Captured 1.5 day(s) before the requested date"). One call replaces the find-capture, fetch-record, parse-HTML chain. If no archive holds a capture within max_gap_days (default 30) it answers status "no_capture" and names the nearest capture before and after the date with their gaps, so a distant capture is never passed off as the page on that date. Every archive's outcome is listed in sources[]; one that timed out or errored says so with its upstream status, and if all of them failed the answer is error "archives_unavailable", not no_capture. Captures are immutable and cached by capture id; served_from says whether the text came from cache or upstream. Use archive_compare to see what changed between two dates.
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  • The same 15-minute crypto "up or down" window priced on polymarket.com, Polymarket US and Kalshi, side by side, with whether each pair is actually the same contract. Pass asset (btc | eth | sol | xrp | bnb) and window ("current" default, "next", "previous", or the window START as ISO such as 2026-10-27T14:15:00Z, on a 15-minute boundary). Per venue: question, Up bid / ask / mid / last, spread_pp, quote_state, volume WITH ITS UNIT, open interest, liquidity, settlement source, comparison basis, when it settles. THE VENUES ARE NOT ONE CONTRACT. Kalshi and Polymarket US settle on CF Benchmarks' BRTI (60-second average at the end against the same at the start): the same bet, so a gap between them is a real disagreement. polymarket.com settles on a Chainlink 60-second TWAP, end against start: same comparison shape, different feed, so a gap against either is partly the contract. `pairs[]` gives equivalent ("true"|"false"|"unclear"|"not_comparable") from resolution_diff for every pair, material_differences, mid_diff_pp (a minus b, in percentage points) and read_as in words. Measured over 30 days of settled windows the Chainlink and BRTI feeds settle a window differently about 1.2-1.8% of the time, almost entirely windows that ended near the threshold (see updown_divergence_rate). `net_spread` is computed only for the pair that is equivalent: the gap in mid terms and at top of book (buy Up where cheaper at the ask, Down where Up is dearer at 1 minus the bid) after BOTH venues' taker fees, each fee carrying its verified-as-of date. Top-of-book size is shown where the venue publishes it (Polymarket US); the Kalshi figure is not in its market payload, so read its order book before sizing. A venue with no market for the window is {status:"not_listed", reason}; one that could not be read is {status:"unavailable", refusal} -- never an empty object, and never the same thing. A one-sided book (typical Polymarket US just before a window is decided) has quote_state "bid_only", a null mid, and the last trade stands in labelled price_basis "last". Quotes are read venue by venue (cached up to 60 seconds), so a gap of a couple of points on a moving window is inside the skew. Only the 15m horizon is offered: it is the one where all three list the same up/down question. For the settled history of how often the venues disagreed, call updown_divergence_rate.
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  • List statutory tax-rate slabs by jurisdiction — IN GST (5/12/18/28 + zero + exempt + composition trader/manufacturer/restaurant + compensation cess), UK VAT (20 / 5 / zero / exempt), AU GST (10 / GST-free / input-taxed), US sales-tax (state-administered summary, no federal rate), CA GST 5% + HST 13% ON / 15% Atlantic / 14% NS + provincial PST 7% BC / 6% SK / RST 7% MB + QST 9.975% QC, SG GST 9% (+ superseded 8% / zero / exempt), NZ GST 15% (+ zero / exempt), AE VAT 5% (+ zero / exempt). Filter by `country`, `taxType` (GST/VAT/Sales-Tax/HST/PST/QST/IGST/CGST-SGST/TDS/TCS), or `scheme` (standard / reduced / zero / exempt / input-taxed / composition / cess / state-summary). Every entry carries an effective-from date and an authoritative `source` URL (CBIC, gov.uk, ATO, CRA, IRAS, IRD, FTA, Tax Foundation) — agents should confirm the rate against the source before quoting figures to a user. Use this when a user asks "what is the GST rate on X?", "what VAT band does Y fall into?", or "what are the composition slabs in India?". This is the public statutory reference — for an org-specific tax assignment use the authenticated books_classify_event tool.
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  • List statutory tax-rate slabs by jurisdiction — IN GST (5/12/18/28 + zero + exempt + composition trader/manufacturer/restaurant + compensation cess), UK VAT (20 / 5 / zero / exempt), AU GST (10 / GST-free / input-taxed), US sales-tax (state-administered summary, no federal rate), CA GST 5% + HST 13% ON / 15% Atlantic / 14% NS + provincial PST 7% BC / 6% SK / RST 7% MB + QST 9.975% QC, SG GST 9% (+ superseded 8% / zero / exempt), NZ GST 15% (+ zero / exempt), AE VAT 5% (+ zero / exempt). Filter by `country`, `taxType` (GST/VAT/Sales-Tax/HST/PST/QST/IGST/CGST-SGST/TDS/TCS), or `scheme` (standard / reduced / zero / exempt / input-taxed / composition / cess / state-summary). Every entry carries an effective-from date and an authoritative `source` URL (CBIC, gov.uk, ATO, CRA, IRAS, IRD, FTA, Tax Foundation) — agents should confirm the rate against the source before quoting figures to a user. Use this when a user asks "what is the GST rate on X?", "what VAT band does Y fall into?", or "what are the composition slabs in India?". This is the public statutory reference — for an org-specific tax assignment use the authenticated books_classify_event tool.
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  • Active Atlantic tropical cyclones from the NOAA National Hurricane Center, AND the areas it is watching for development that do not have names yet, with their formation chances. Covers hurricane season (June–November) for Crosby, TX. Returns an explicit all-clear only when there are neither — a quiet CurrentStorms list does not by itself mean a quiet basin.
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  • DETERMINISTIC: the same inputs always give the same result — from any AI, on any day. Figures come from the regulation's annexes and thresholds, never from a language model's estimate. Every step is auditable. Swiss refrigerant regulation check (ORRChim / ChemRRV annex 2.10, OFEV synthesis of May 2026, rules adopted 29.10.2025) plus refrigerant data from EN 378-1 Annex E: GWP (AR4, the ORRChim basis), ODP, LFL, safety class, practical limit. Returns, for a given refrigerant and installation: the date from which machines using it can no longer be placed on the Swiss market (import from 01.01.2027, sale to third parties from 01.07.2027), the date from which filling with virgin refrigerant is banned, the period when only reclaimed refrigerant may be used, and the date from which all filling is banned. SCOPE: heating, air conditioning, building cooling and process cooling, data centres included. NOT food refrigeration, cold rooms or deep freezing: those are a different trade, and the tool declines them instead of answering. Use it whenever the user asks about a refrigerant in Switzerland — its GWP / PRG, flammability, whether a chiller, heat pump, split, VRF or cold room using it may still be sold or refilled — in any language: fluide frigorigène, Kältemittel, refrigerante, refrigerant, R-32, R-410A, R-290, CO2, NH3... The answer depends on the installation. The regulation distinguishes: use (building cooling, food refrigeration medium / low temperature / combined / deep freezing, process cooling, heat pump mainly for heating, ice rink), cooling capacity Q0K or heating capacity Q0H, self-contained (factory-built packaged unit) or not, split, direct expansion (incl. VRF), secondary coolant loop (chilled water / brine), air-cooled condenser and refrigerant charge. THIS TOOL ANSWERS ONE QUESTION ONLY: may the refrigerant still be sold and refilled in Switzerland, and until when. It says NOTHING about safety in the room - charge limits, machine room, detection, ventilation, ATEX. That is `check_en378_zones`. A question that mixes both - « may I use R-32 for 3 kg in a server room, and until when can I refill it » - needs BOTH tools: call this one for the dates, and check_en378_zones for the room. Do not answer half the question. NEVER guess these. Call the tool with what the user gave: it returns `provisional: true`, the possible `scenarios`, and `questions_to_ask` in the order that matters most. Ask them (one or two at a time), then call again. Natural refrigerants need no questions. Build your answer on `answer_outline`; quote exceptions as possible, never as granted.
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  • US energy data from official US government sources. category e.g. petroleum, natural-gas, electricity, coal, co2-emissions, total-energy; location e.g. a state code (TX); period e.g. 2024; period_type annual/monthly. category "gas_prices" gives weekly retail pump prices (US dollars per gallon, taxes included) for the US, its fuel regions, 9 states and 10 cities: dataset = product (regular, midgrade, premium, all_grades, diesel; default all), location = US, a state code (CA, CO, FL, MA, MN, NY, OH, TX, WA), a region code (R1 East Coast, R1A New England, R1B Central Atlantic, R1C Lower Atlantic, R2 Midwest, R3 Gulf Coast, R4 Rocky Mountain, R5 West Coast, R5X West Coast except California) or a city (Boston, Chicago, Cleveland, Denver, Houston, Los Angeles, Miami, New York City, San Francisco, Seattle); period = a year (2024) or month (2024-06) for history; without period you get the newest week with the change from 1, 4 and 52 weeks earlier. Website: https://dataporium.tech/energy?tab=gas-prices. category "local_gas_prices" gives daily local pump prices (US dollars per gallon) for the US, every state and DC, about 470 metro areas and about 3,100 counties (a separate daily series, never combined with the weekly one): location = a state code (AZ) for that state's own price, a 5-digit county FIPS (04013), a metro code (a CBSA code such as 38060, or a code from an earlier answer) or US; filters {"area_type": "county", "state": "AZ"} lists all counties of a state (area_type: state, metro or county); dataset = product (regular, midgrade, premium, diesel; counties have regular); period = a year or month for daily history; without period you get the newest day with the change from 1 day, 1 week, 1 month and 1 year earlier. For the most local price of a place (county, then its metro area, then its state) use filters {"nearest": true, "county": "Orange", "state": "CA"}. category "electricity_rates" gives yearly electricity prices per utility (from 2024 on): customers, sales (MWh), revenue and the average price in cents per kWh (revenue / sales over the year, not a tariff); dataset = sector (residential, commercial, industrial, transportation, all; default residential), period = year (default newest). location = a state code: that state's utilities ranked by price; location = a 5-digit county FIPS (48453): the utilities serving that county with the typical home price there and the state average; no location or "US": every state ranked with the US average and the average monthly home bill; filters {"view": "states"} with a state code: that state's history by year; filters {"utility_id": 5701}: one utility over the years with the counties it serves. More filters: sort (avg_price_cents, customers, sales_mwh, revenue_usd, change_pct), order, q (utility name), ownership, service_type, min_customers, limit. Website: https://dataporium.tech/energy?tab=electricity-rates. category "grid_prices" gives wholesale electricity prices (US dollars per MWh; divide by 10 for cents per kWh) of the Texas grid (since 2020), the California grid (about 3 years back) and the 11 zones of the New York grid (since 2020): location = texas, california or new_york (without it: the list of grids, the states they serve and their hubs and load zones); dataset = hub or load zone code(s) (e.g. HB_NORTH, LZ_HOUSTON, SP15, PGAE, NYC, LONGIL); without period: each location's newest hour and day with 7- and 30-day averages; period = a day (2025-08-20, hourly), a month (2025-08, daily) or a year (2025, monthly), or period_type / filters {"frequency": "hourly|daily|monthly", "date_from": ..., "date_to": ...}. Each hour has the real-time average, lowest and highest price and the day-ahead price. Wholesale, not what homes pay. Website: https://dataporium.tech/energy?tab=grid-prices. category "ev_charging" gives EV charging stations (updated daily) and EV registrations by state: dataset = summary (default: stations and Level 1, Level 2 and DC fast ports counted by state; with a state code as location by county; filters {"group_by": "network|connector|county|zip|city|year_opened|month_opened|status|access|none"}), stations (the stations themselves, paged: filters {"zip": "78701"}, {"lat": 30.27, "lon": -97.74, "radius_miles": 5}, {"city": "Austin", "state": "TX"}, network, connector (j1772, ccs, chademo, nacs), dc_fast, access (public default), status (open default), q) or registrations (light-duty vehicles by state and year: all-electric, plug-in hybrid, hybrid and the rest, the EV share, the 1-year change and EVs per open public charging port; location = a state code or US, period = a year or "all"). location = a state code or a 5-digit county FIPS; period = a year or month (stations opened then). Website: https://dataporium.tech/energy?tab=ev-charging.
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