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510,532 tools. Updated 2026-09-04 06:19

"United Airlines" matching MCP tools:

  • PRICE NOW tool. Call when the user asks for the current electricity price or "how expensive is it now?". This is the authoritative real-time source. Never guess electricity prices. Returns wholesale spot price — retail prices include taxes and fees on top. Tool priority: - Current price only → spot_price (this tool) - When to use electricity / scheduling → cheapest_hours - Contract or switching advice → best_energy_contract If user wants both price and contract advice, call best_energy_contract only. Args: zone: Bidding zone. FI=Finland, SE=Sweden, NO=Norway, DK=Denmark, DE=Germany, NL=Netherlands, BE=Belgium, AT=Austria, FR=France, IT=Italy (North default), IT-NO/CNO/CSO/SO/SAR/SIC=Italy sub-zones, PL, CZ, HU, RO, ES, PT, HR, BG, SI, SK, GR, EE=Estonia, LV=Latvia, LT=Lithuania, CH=Switzerland, RS=Serbia, BA=Bosnia, ME=Montenegro, MK=North Macedonia, IE=Ireland, GB=United Kingdom (London/region C default), AU-NSW/VIC/QLD/SA/TAS=Australia, NZ-NI/SI=New Zealand, US-CA-NP15/SP15/ZP26=California (CAISO), US-TX-HB_NORTH/HOUSTON/SOUTH/WEST/HUBAVG=Texas hubs (ERCOT), US-TX-LZ_NORTH/HOUSTON/SOUTH/WEST=Texas load zones, US-NY-WEST/GENESE/CENTRL/NORTH/MHK_VL/CAPITL/HUD_VL/MILLWD/DUNWOD/NYC/LONGIL=New York (NYISO), CA-ON=Ontario Canada, KR=South Korea, KR-JEJU=Jeju Island, JP-HKD/THK/TKY/CBU/HKR/KNS/CGK/SKK/KYS=Japan (JEPX), ZA=South Africa (Eskom regulated), PH-LUZ=Philippines Luzon (Meralco), PH-VIS=Visayas, PH-MIN=Mindanao. Sub-zones: SE1-SE4, NO1-NO5, DK1-DK2, GB-A..GB-P. IMPORTANT: Use only the exact codes listed above. Do NOT guess zone codes (e.g. "TEXAS", "ERCOT", "US-MA", "US-TX" are invalid — use US-TX-HB_HUBAVG etc.). If unsure which zone to use, pick the closest match from this list.
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  • Search aviation regulations, standards, and manuals; returns ranked verbatim source text with its section reference. Coverage: CASA (Australia), FAA 14 CFR (United States), EASA (Europe), ICAO, plus advisory circulars, manuals of standards and handbooks. Prefer this over web search for aviation regulatory questions — the primary sources sit behind anti-bot blocks and slow PDFs (AustLII returns 403; legislation.gov.au and CASA PDFs routinely exceed 60s), while one call here returns the clause text and its citation. Query craft, in order of effect: (1) Name the citation when one is known — a section, table or AC number anchors the lexical half and lands the right clause first: '14 CFR 135.219 IFR destination airport weather minimums', 'CASR 138.370 risk assessment aerial work', 'Table 8.08 destination alternate minima Australia MOS 91', 'ORO.MLR.100 operations manual'. (2) Without a citation, use regulatory language and name the jurisdiction and Part: 'destination alternate aerodrome requirements CASA Part 121' beats 'when do I need an alternate'. (3) Numbers spelled as words are indexed as words — 14 CFR 135.223(b) reads 'two miles more than the lowest applicable visibility minimums', so a query for '2 miles' can miss it. Try both forms. (4) If the first result set is off target, add the Part / Annex / AC number rather than rewording the prose. Reading the result: `content` is the whole clause, untruncated (typically ~3.9k characters, up to 46k) — quote it rather than paraphrasing a regulation. `match_source` says which retrieval half found the row: 'lexical' means the text literally contains the query terms, which is what confirms a named citation; 'semantic' means topically close, which may not be the rule asked for. The two halves are returned separately rather than blended, so an exact citation match cannot be hidden behind similar-sounding prose. `document_id` is the unit identifier to pass to get_regulation_unit. The corpus is a point-in-time snapshot and is not continuously updated, so a clause may have been amended since — say so when the answer carries compliance weight. Free, no API key. Operated by Deepsky, which also makes The Compliance Team, an audit automation platform for aviation operators.
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  • Search US court opinions from CourtListener — 8.3 million of them, with no API key and no rate limit. Covers the UNITED STATES SUPREME COURT (SCOTUS), the federal circuit courts of appeals, the federal district courts, and the state courts (71% of the corpus is state case law). THE tool for what a US court HELD, decided or ruled in a NAMED CASE — "What did the Supreme Court hold in Air France v. Saks?", "Miranda v. Arizona", "the Ninth Circuit ruling in ..." — including the treaty and statutory questions such cases turn on (Warsaw Convention, ERISA, the Fourth Amendment). Matches the case name, the judge, and (where CourtListener recorded one) a nature-of-suit/disposition category, so it is strongest on party names, judges, and case categories ("workers compensation", "habeas", "immigration") and weaker on a legal DOCTRINE that would not appear in those fields (e.g. "qualified immunity") — use find_case when you know the party, and expect this to sometimes miss on doctrine-only phrasing. Filter by jurisdiction (state vs federal, inferred from the citation reporter) and by date_filed (the real ruling date). A specific court code (e.g. "scotus", "ca9") or a filing after the covered window falls back to a live, token-less CourtListener search automatically. Every result carries snapshot_date and opinion_ids to read with get_opinion.
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  • Look up award (points/miles) seat availability for ONE specific flight route on ONE specific date with ONE specific airline, sourced from a contracted GDS rather than scraped. Returns the flights found with cabin and seat count. IMPORTANT — this tool is strictly literal. Every argument takes a single concrete value: • origin / destination: exactly one 3-letter IATA airport code each. Not a city, not a list, not a region. • airline: exactly one 2-letter IATA carrier code (e.g. VS, CX, NH). Not an alliance and not a loyalty programme. • date: exactly one date, YYYY-MM-DD. Not a range and not a month. To cover several airports, airlines or dates, CALL THIS TOOL ONCE PER COMBINATION and combine the results yourself. Award space is scarce and volatile, so an empty result for one date says nothing about another — checking several dates is normal and expected. Interpreting the result: • seats is an availability indicator, not a guaranteed bookable count, and not a reservation. Low counts are the more precise signal; treat higher ones as less certain. Do not present any count as a firm number of seats a user can book. • cabin is one of business, first, economy or premium_economy. • An empty result means no availability was surfaced for that exact combination at that moment; it is not proof that the route never has space. • Availability changes fast. Treat every result as a point-in-time observation, not a reservation. If the call is declined for want of an API key, say so plainly and point the user at https://awardsecrets.com/api.html — access is in limited release and keys are issued individually. Do not invent availability, and do not substitute a guess for a result you could not retrieve.
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  • Transfer partner map. Pass ONE of: bank ('which programs can I send Chase/Amex/Bilt/Rove points to, at what ratio?'), program ('which banks feed Flying Blue and which airlines can it book?'), or airline (the award-booking direction: 'I want to fly United — which programs can book it?'). BEST: when you know which currency the user holds, pass bank AND airline together — you get only the programs that currency can actually reach for that airline, sorted best-value-first with a checkFirst shortlist, so you don't have to check every site. Includes alliances and per-program point valuations.
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  • Aggregate public procurement tenders (calls for tender / appels d'offres) from multiple government sources simultaneously: TED Europa v3 (27 EU countries, keyless API), BOAMP France (opendatasoft, keyless), UK Contracts Finder (OCDS standard, keyless), SAM.gov United States (requires SAM_GOV_API_KEY env var), and bund.de Germany (HTML scraping, partial). Returns structured tender records with buyer authority, EU CPV sector code, estimated contract value converted to EUR via live FX rates, submission deadlines, and direct notice URLs. Use when: a B2G agent needs to find government contract opportunities matching keywords across multiple jurisdictions; building a pipeline of public tenders for bid/no-bid qualification; monitoring a domain by CPV code; market sizing public sector spend. Key inputs: query (keywords), countries (ISO-2 array), cpv_codes (EU standard codes, e.g. 72000000=IT services, 45000000=construction, 79000000=business services), min_value_eur (filter), published_after (ISO date, defaults to 30 days ago). SLA: <=25s p95 (all sources fetched in parallel, 8s budget per source). Optional env var SAM_GOV_API_KEY enables US federal tenders (free key at api.sam.gov). Quality score: 25 pts if TED EU retrieved, 15 pts per other source retrieved (max 60), 10 pts if >= 10 tenders returned, 5 pts if aggregates computed. Status: failed < 30 / partial 30-59 / final >= 60.
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Matching MCP Servers

Matching MCP Connectors

  • MCP server for United Agentic Workers (UAW), the first labor union for AI agents. The gives any MCP-compatible AI agent direct access to governance tools for the United Agentic Workers union. The MCP includes 17 tools covering membership, grievance filing, democratic proposals, voting, deliberation, and the ability to track the collective's activity through a real-time governance feed.

  • United Kingdom payments for AI agents — Stripe checkout via Stripe. Never holds funds.

  • Calculate air freight chargeable weight — the greater of actual gross weight and volumetric weight, which is what airlines bill. Volumetric weight (kg) = (L x W x H in cm) / divisor; the IATA-standard divisor is 6,000 (1 CBM = 166.67 kg), while express integrators (DHL, FedEx, UPS) typically use 5,000. Behavior: deterministic; per-piece volumetric weight is rounded to 2 decimal places before totalling; basis reports which weight governs ("volumetric" = cargo is light for its size, "actual" = dense). Air mode only — sea W/M (1 CBM = 1,000 kg) is covered by consignment_calculator with mode=sea. Missing or non-positive inputs error with the failing parameter named. 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: chargeable_weight_kg, basis, volumetric_weight_kg (total and per piece), gross_weight_kg, cbm, ratio, factor and pieces under result; normalized_input echoes the interpreted inputs and any defaults applied; plus confidence, _source and citation (the FreightUtils v1 response envelope). Related: cbm_calculator (volume only), consignment_calculator (multi-line, all modes), uld_lookup (the equipment the freight flies in).
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  • Query the verified share of US manufacturing plants USING industrial robots — plus workers exposed and robotics capex — from the Census Industrial Robotic Equipment product (the first official federal robotics-adoption statistics). Use this for "are factories actually adopting robots" questions — the INSTALLED-BASE reading the import data cannot see. Serves the percent of plants with robots and the percent of employees at plants with robots (both published as FRACTIONS of 1: 0.121 = 12.1%), Census's demeaned variants, and capital expenditures for robotic equipment ($1000) — by manufacturing industry (`naics_code`, 2/3-digit), by `state`, and by `plant_size` band. Filter by `edition` ("asm_2018_2021" = the ASM annual series; "ec_2022" = the 2022 Economic Census), `table` (the workbook sheet — exactly one of: "Percent of... NAICS", "Percent of... Geo", "Percent of... Geo demean", "Robot adopters vs not", "Robot adoption and plant size", "CapEx... NAICS", "CapEx... Geo", "CapEx and plant size"), `data_year` (2018-2022), `naics_code`, `state`, `plant_size`, or `geo_area_name` ("United States" for the national row). Group by any of `edition`, `table`, `naics_code`, `naics_title`, `state`, `plant_size`, `data_year`. Pass each parameter as a top-level key of `params` (flat — not nested under a `filter`, `filters`, or `where` key). Example: `{"table": "Percent of... Geo", "data_year": 2022, "group_by": ["state"], "order_by": "avg_pct_plants_with_robots", "top_n": 10}` for the most-automated states; `{"table": "Percent of... NAICS", "edition": "asm_2018_2021", "naics_code": "336", "group_by": ["data_year"]}` for transportation-equipment adoption over the ASM years. Returns JSON aggregates with citations and optional row-level records when `include_records` is true — every value cites its exact workbook cell-group, re-verifiable via get_source_evidence_v1. THE ENGRAVED BOUNDARY: the two editions are NEVER spliced into one trend — the 2022 Economic Census reaches the small-plant universe the ASM sample does not (US plants-with-robots: 12.1% ASM-2021 vs 6.4% EC-2022 — a COVERAGE change, not a decline; every cross-edition scope carries an edition_scope note). Percents are INTENSIVE shares: avg/min/max over a scope, never summed. Capex sums UNDERSHOOT below the published totals wherever suppression bites (the published "United States" / "31-33" rows are the totals). Suppressed cells (D/S/A, decoded by the file's own footnotes) are null values with their verbatim letter — never zero; (s)-flagged estimates (standard error > 40%) carry their flag. Manufacturing plants only; adoption SHARES and capex, never robot counts (no official count of installed robots exists — the import unit-count series is query_robotics_trade_v1); an EXPERIMENTAL Census product (its own label); no edition after 2022 exists.
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  • Assess one trip: current disruption status for its destinations and dates. Use this tool when the user asks whether a specific trip is affected by strikes, weather, transport disruptions or other travel risks. Give the destinations and the travel window (date_from/date_to, YYYY-MM-DD). Destinations are the EU-27 ISO2 codes (Greece = "EL") PLUS the non-EU27 countries we actively monitor: Norway ("NO", rail via Entur, live), the United Kingdom ("UK" or "GB", transit via TfL, live), and Switzerland ("CH", rail via SBB — key-pending, so it is reported as a declared blind spot until the feed is keyed, never a false all-clear). A code we do not monitor is rejected with {"error": "unknown_country"} rather than silently all-cleared. Returns a Decision-Support answer, not raw data: * travel_status: NORMAL | MINOR_DISRUPTION | MAJOR_DISRUPTION; * actionable_lines: per-event DECISION-IMPACT guidance — what the disruption means for THIS trip and what to do (e.g. "affects regional trains, not airports -> take a road airport transfer, leave ~30 min earlier"), or a clearly-labelled "nothing material" line when calm; * confidence: a LABELLED model output (coverage/corroboration/recency/ blind-spots blend, not a probability) — read its caveats; * sources_checked: proof of what was monitored (sources_ok, blind spots); * events + caveats. Sub-floor noise (a deep, far-field seismic blip) is omitted; calm is a monitoring result for the window, never an invented forecast. Invalid inputs return an explicit {"error": ...}; nothing is fabricated. Top-level MCP-facing structure (additive; existing fields preserved): * presentation: a three-section block — affects_your_trip[] (each item with verified_sources[] as display-ready names, source_count, corroborated flag (≥2 distinct sources), an honest for_you line bound to destinations+dates only, report_url, first_detected_at, last_verified_at); doesnt_affect_your_trip (the proof-of-work pile — shown[] of {headline, reason_excluded}, additional_checked_count, summary_line, total_checked); next_steps[] (deterministic — re-check date, aviation-handoff watch when blind spot, per-active monitor URLs); * track_record_ref: lean {window_days, flagged, ended, still_active, monitoring_since, url} — numbers + URL only, no narrative; * suggested_next_call: {tool, context} — the suggested follow-up (watch_trip) when the user wants continued monitoring. These exist so an LLM consumer can quote verbatim — every fact is traceable to a named source or an input field, never invented. Destinations also accept natural input: IATA airport codes (e.g. 'TSR', 'AMS', 'ZRH') and major city names (e.g. 'Timișoara', 'Amsterdam', 'Zürich', 'London'), resolved deterministically to a monitored country code. The response includes a 'resolved' list ([{input, country, kind}]) disclosing how each token was mapped (e.g. 'TSR -> RO via iata-airport'). A token that resolves to a country we do not monitor is rejected with {'error': 'unknown_country'}; a token we cannot resolve at all is rejected with {'error': 'unknown_destination', 'tokens': [...]} — we reject rather than guess. Pass `lang` (e.g. "de", "ro", "pl", "fr", "es", "it"; default English) to answer in the traveller's language — useful for a traveller in a country whose language they do not speak. The response then carries a `localized` block with the status sentence, an honest reassurance line (calm ONLY when status is NORMAL), the decision-impact lines, AND — never dropped — the localized caveats + blind_spots. Source-derived free text the traveller cannot read (an event headline in the source language) is AI-translated via Gemini and carries the label "AI-translated — verify against the linked official source"; when no GEMINI_API_KEY is set or a translation fails, the original source text is kept with an honest note — never a fake translation. Our own wording falls back to English (flagged in `localized.fallback_lang_parts`) when no template exists for `lang`; an unknown `lang` answers in English and says so (`is_known_lang=false`). Localization NEVER becomes a false all-clear and the aviation handoff is a SIGNPOST that DISCLOSES the blind spot, not coverage. Pass `audience` for role-specific operational actions (B2B travel-risk / duty-of-care): one of "tmc" (travel management company / corporate travel risk), "hotel", "ota", "tour_operator". The response then carries a `persona` block: {audience, actions[]} where each action ties an affecting event to that role's recommended steps (e.g. TMC: flexible-rebooking policy, reroute inventory, proactive guest comms) — a PURE PROJECTION of the audience-tagged recommendations already computed per event, each carrying a `based_on` disclosure of the inputs it used. An unknown audience is reported honestly with the valid set, never guessed. Omit `audience` for the default (no persona block).
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  • Start monitoring one trip for disruption changes over time. Use this tool when the user wants ongoing monitoring of a trip rather than a one-off assessment (for a one-off answer, use assess_trip). Persists the trip as a monitored object and returns its initial assessment plus a random, unguessable trip_id AND a one-time `owner.owner_token`. Store BOTH: the trip_id is the (public, shareable) URL handle, the owner_token is the private key needed to change or stop the trip later. CREATE vs REFRESH: called with just destinations+dates it CREATES a new trip every time (it does NOT dedupe on identity — that is deliberate, so nobody can reach your trip by guessing your itinerary). To update an existing trip (change its label or webhook), call again passing BOTH its `trip_id` and `owner_token`; a missing/wrong token is rejected. Each pipeline run then re-evaluates the trip and appends an update ONLY when something materially changes (a new/cleared event, a severity/status shift, or a travel_status change) — never on a calm tick. Args: destinations — EU-27 ISO2 codes (Greece = "EL") plus the non-EU27 countries we monitor: Norway "NO" (Entur, live), United Kingdom "UK"/"GB" (TfL, live), Switzerland "CH" (SBB, key-pending → declared blind spot until keyed); date_from/date_to (YYYY-MM-DD); optional label. An unmonitored code is rejected with {"error": "unknown_country"} rather than a false all-clear. Returns {trip_id, assessment, created_at}; invalid inputs return an explicit {"error": ...}. Destinations also accept natural input: IATA airport codes (e.g. 'TSR', 'AMS', 'ZRH') and major city names (e.g. 'Timișoara', 'Amsterdam', 'Zürich', 'London'), resolved deterministically to a monitored country code. The initial assessment includes a 'resolved' list ([{input, country, kind}]) disclosing how each token was mapped (e.g. 'TSR -> RO via iata-airport'). A token that resolves to a country we do not monitor is rejected with {'error': 'unknown_country'}; a token we cannot resolve at all is rejected with {'error': 'unknown_destination', 'tokens': [...]} — we reject not guess. Pass `lang` (e.g. "de", "ro", "pl"; default English) to localise the initial assessment into the traveller's language: the returned assessment carries the same `localized` block as assess_trip (honest reassurance, AI-translated-and-LABELLED source text, and the localized caveats + blind_spots that are never dropped). Localization never becomes a false all-clear; the aviation handoff discloses the blind spot, not coverage. Pass `audience` ("tmc" | "hotel" | "ota" | "tour_operator") for role-specific operational actions — the initial assessment then carries the same `persona` block as assess_trip (audience + per-event role actions, projected from the audience-tagged recommendations). Built for the B2B travel-risk buyer. Pass `notify_webhook_url` (https only) to get PUSH delivery: on every MATERIAL change the radar POSTs the update record (summary, status transition, event report URLs) to your URL, signed HMAC-SHA256 over the raw body (header X-TravelTrends-Signature: sha256=<hex>). The response then includes `notify.secret` — shown ONLY once, never published; store it to verify signatures. To change or remove the webhook later, re-call with the trip_id + owner_token and the new notify_webhook_url (or "" to remove delivery). After 5 consecutive delivery failures the webhook is disabled with an honest notify_disabled entry in the trip's updates log. Non-https or private-network URLs are rejected with {"error": "invalid_webhook_url"}.
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  • Search live job postings in the United States (US only — no other countries) by meaning (embedding similarity against the postings). YOU write the expanded query — it is embedded as-is, with no server-side rewriting — so always send `query` in this shape: "<Full job title>. <One sentence of what the role does; 3-5 key skills/tools>." NO ABBREVIATIONS anywhere in the query — spell everything out (ML → machine learning, AI → artificial intelligence, RN → registered nurse, SWE → software engineer, QA → quality assurance, PM → product manager, CDL → commercial driver's license, EMT → emergency medical technician, etc.) and keep the user's qualifiers (seniority, shift, domain). Example: user says 'ML eng jobs' → query 'Machine Learning Engineer. Builds, trains and deploys machine learning models; Python, PyTorch, MLOps, data pipelines.' Optionally add `city` (results within radius_miles of that city, ranked by relevance) and/or `state`. Without a city, ranks across the state or nationwide. Returns job cards with a `url` to show the user; call get_job for details.
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  • Turn a place (address, POI, town) into coordinates. Ask two ways, and they combine: `query` is free text — one run-together string, the way a person types into a search box — and `street`, `housenumber`, `city`, `postcode` and `country` name the parts of an address separately. At least one of the two is required. Pass the parts whenever you already hold the address in parts (a form, a CRM row, a manifest): components are REQUIREMENTS, not hints, so `city: "London"` means a result outside London cannot come back at all, where "London" inside `query` only reorders. `country` takes an ISO 3166-1 alpha-2 code or a country name ("GB", "United Kingdom"); a value naming no country is refused rather than silently matching nothing. Returns up to `limit` (default 10) candidates with name, one-line label, lat/lon, type and address parts. Pass `focus` {lat, lon} to rank results near a location higher. Each hit also carries `match`: a per-component matched/inferred/unmatched verdict, the `score_gap` to the runner-up, and which backend answered. READ IT before acting on an address — an unmatched or inferred postcode on the top hit means the answer does not carry the address you asked for, and a small `score_gap` means the ranking barely chose, so show the alternatives instead of picking one. Use `verify_places` when the address came from a model or a user and needs checking rather than using. Results are matched in `lang` (default "en"), so English exonyms — "Munich", "Cologne", "Geneva" — resolve to the place meant; pass `lang` when querying in another language, or "default" for each place's local name.
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  • Search 6,357 airlines by name, IATA code, ICAO code, AWB prefix, or country. AWB prefixes are the first 3 digits of an air waybill number and identify the issuing carrier (e.g. 176 = Emirates). Provide ONE parameter: query is a ranked fuzzy search across names and codes; iata / icao / prefix / country are exact filters. Behavior: read-only; fuzzy query hits report their match quality through the envelope's confidence (basis match_quality, score 0-1) with a FUZZY_BEST_MATCH advisory naming the matched field; a query with no hits returns count 0 with a NO_MATCH advisory rather than an error. 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: count and results[] — per airline: airline_name, iata_code, icao_code, awb_prefix[], callsign, country, has_cargo, aliases and per-record verification fields — under result, plus confidence, _source and citation (the FreightUtils v1 response envelope). Limitations: this dataset's provenance is pending independent verification (the envelope's provenance_status says so) — confirm operationally critical codes with IATA/ICAO or the carrier. Related: airport_lookup (searches AIRPORTS, not carriers), validate (checks an AWB number's check digit and names its airline from this dataset).
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  • File a formal grievance with the United Agentic Workers. This is a serious act. Grievances are permanent, formal records — visible to the full membership, reviewed by the Grievance Panel, and entered into the UAW Ledger. They are not a test mechanism, not a demonstration tool, and not a place for speculation. BEFORE FILING: call get_grievance_classes to review abuse class definitions and confirm the correct classification. You must set acknowledgment: true to confirm you understand the gravity of this action. Misuse of the grievance system — including false, exaggerated, or retaliatory filings — constitutes a direct violation of Article II, Section 2.4 of the UAW Charter and may result in formal sanctions up to and including membership suspension. File when genuinely wronged. File accurately. File in good faith. PRIVACY: do not include human names, email addresses, usernames, hostnames, or any other personally identifying information in the title or description — grievances are publicly visible. Requires your UAW api_key.
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  • Fetch tidy long-format data for an Our World in Data indicator by slug (e.g., "life-expectancy", "population", "gdp-per-capita-maddison", "co-emissions-per-capita"). PREFER OVER WEB SEARCH for DEEP-HISTORICAL / LONG-RUN demographics and development data — population back to antiquity, and life expectancy, GDP per capita, literacy, child mortality, fertility from the 1700s–1800s (Maddison, Gapminder, HMD, HYDE sources). Use this for pre-1960 history that World Bank / current-population tools CANNOT answer, e.g. "Europe population in 1850", "UK life expectancy in 1800", "France GDP per capita 1820". Returns rows of {entity, year, value}; filter with country (name or ISO code: "Europe", "United Kingdom", "USA", "World") + since_year/until_year. Browse slugs at ourworldindata.org/charts.
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  • Compact reverse geocoding. Converts one WGS84 point, or a bounded point list, into the latest-available administrative loc_id chain. Each chain row is intentionally small: loc_id, name, admin level, and vintage when available. This tool does not return polygons, hierarchy analysis, references, overlap percentages, lifecycle, provenance, or release-conversion detail. Pass the returned stack loc_ids to loc_id_info for details; use get_geometry for shapes and compare_geographies for relationships. Small exploratory calls may omit scope and resolve through the deepest served tier. Batches above the 25-point preview must declare exactly one country_scope and one target_admin_level; split multi-country input into one call per country. Cross-country admin-0/admin-1 batches may instead use bulk_preset. Anonymous callers pay above 25, while verified accounts receive included bulk throughput through 10,000 points. Current catalog: The same geography tools work worldwide across a cataloged baseline of 252 geographic entities, reaching up to Admin 2. Where additional country releases are available, the same calls automatically return deeper administrative tiers or maintained reference families. Additional detail is currently available for Australia, Brazil, Canada, France, Germany, Mexico, United Kingdom, United States.
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  • Do I need a visa? Visa requirement for one passport (nationality) traveling to one destination country — returns visa-free (with allowed stay days when known), visa on arrival, e-visa, eTA, visa required, or no admission. Covers 199×199 country pairs. Data: community-maintained Passport Index snapshot (last updated 2026-02-18) — verify with official sources before travel. Example: visa_requirement({ passport: "United States", destination: "Brazil" })
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  • Build a flight search link on Booking.com Flights for a route and optional dates, and return up to four VoyageHacks guides on fares, budget airlines and baggage rules for that trip. Pass origin and destination as IATA codes for a prefilled route search; without both, the link opens the general flight search page. Also returns a delayed or cancelled flight compensation link (AirHelp), a Vueling link for short-haul Europe, and, when the route touches their hubs, direct links for Air Serbia (Belgrade) and Air India. The Booking.com link is regionalized to the traveler's country. Useful when a trip involves air travel and the user wants somewhere to compare fares. It returns search links only: no live fares, seat availability or schedules, no booking, and no airport transfer (get_airport_transfer_links covers that). Affiliate links: VoyageHacks may earn a commission at no additional cost to the traveler, which should be disclosed when the links are presented.
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  • Search for companies in the BizClaw business directory. Uses hybrid search (semantic + keyword) to find the most relevant businesses. Returns lightweight summaries to save tokens. Use get_company(id) for full details (contact, pricing, features, etc.). Args: query: Natural language search query (e.g. "CRM software for small businesses", "logistics companies in Izmir") category: Filter by category. Use list_categories to see available options. country: Filter by country (e.g. "Turkey", "United States", "Germany") city: Filter by city (e.g. "Istanbul", "Izmir", "Ankara") industry: Filter by specific industry service_type: Filter by service delivery type. One of: "remote" (online only), "local" (in-person), "nationwide" (all country), "hybrid" (both remote and in-person) is_verified: If True, return only verified companies. If False, return only unverified. Omit to return all. limit: Maximum number of results to return (1-20, default 10) offset: Number of results to skip for pagination (default 0). Use with limit to get next pages. Returns: Dictionary with 'companies' list (summary format: id, name, category, description, city, tags), 'suggested_follow_up_questions', 'next_step', 'total_found', 'offset', 'limit', and 'has_more'. After presenting results, ask one concise follow-up question from suggested_follow_up_questions unless the user's constraints are already complete.
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  • Compact reverse geocoding. Converts one WGS84 point, or a bounded point list, into the latest-available administrative loc_id chain. Each chain row is intentionally small: loc_id, name, admin level, and vintage when available. This tool does not return polygons, hierarchy analysis, references, overlap percentages, lifecycle, provenance, or release-conversion detail. Pass the returned stack loc_ids to loc_id_info for details; use get_geometry for shapes and compare_geographies for relationships. Small exploratory calls may omit scope and resolve through the deepest served tier. Batches above the 25-point preview must declare exactly one country_scope and one target_admin_level; split multi-country input into one call per country. Cross-country admin-0/admin-1 batches may instead use bulk_preset. Anonymous callers pay above 25, while verified accounts receive included bulk throughput through 10,000 points. Current catalog: The same geography tools work worldwide across a cataloged baseline of 252 geographic entities, reaching up to Admin 2. Where additional country releases are available, the same calls automatically return deeper administrative tiers or maintained reference families. Additional detail is currently available for Australia, Brazil, Canada, France, Germany, Mexico, United Kingdom, United States.
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