{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_alcotravel","slug":"alcotravel","name":"Alcotravel","type":"product","url":"https://aicotravel.com","page_url":"https://unfragile.ai/alcotravel","categories":["app-builders"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_alcotravel__cap_0","uri":"capability://planning.reasoning.preference.aware.itinerary.generation.with.constraint.satisfaction","name":"preference-aware itinerary generation with constraint satisfaction","description":"Generates multi-day travel itineraries by processing user preferences (interests, budget, pace, dietary restrictions) through a constraint satisfaction engine that balances competing objectives (cost, time, experience diversity). The system likely uses a combination of preference embeddings and rule-based filtering to rank and sequence activities, accommodations, and dining options that satisfy stated constraints while optimizing for user satisfaction based on learned preference patterns.","intents":["I want an AI to create a 5-day itinerary for Tokyo that respects my $100/day budget and preference for street food","Generate multiple itinerary options with different themes (cultural vs. adventure) so I can choose","Create an itinerary that avoids crowded tourist spots and prioritizes local experiences"],"best_for":["Budget-conscious solo travelers planning trips with specific constraints","Spontaneous travelers who need quick itineraries without manual research","Users seeking personalized recommendations beyond generic guidebooks"],"limitations":["Constraint satisfaction may fail or produce suboptimal results in destinations with limited data coverage or niche preferences","Preference learning requires historical user interaction data; cold-start users receive generic recommendations","Cannot dynamically adjust for real-time constraint changes (e.g., sudden budget reduction mid-trip) without re-generation"],"requires":["User profile with stated preferences (interests, budget, travel style, dietary restrictions)","Access to destination activity/accommodation database with pricing and ratings","Internet connectivity for real-time data lookups"],"input_types":["text (destination, dates, preferences, budget)","structured data (user profile with preference vectors)"],"output_types":["structured itinerary (day-by-day activities with times, locations, costs)","text (narrative descriptions of activities and recommendations)"],"categories":["planning-reasoning","personalization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_alcotravel__cap_1","uri":"capability://automation.workflow.real.time.flight.and.accommodation.price.monitoring.with.change.alerts","name":"real-time flight and accommodation price monitoring with change alerts","description":"Continuously monitors flight prices, hotel rates, and availability for planned trips by polling third-party travel APIs (likely Skyscanner, Kayak, or Booking.com APIs) at configurable intervals and comparing against baseline prices or user-set thresholds. Detects price drops, availability changes, or schedule disruptions and delivers alerts via push notification, email, or in-app messaging. Uses time-series analysis to identify price trends and predict optimal booking windows.","intents":["Alert me when flight prices drop below my target price for my planned trip","Notify me immediately if my booked hotel becomes unavailable or changes significantly","Show me price trends over time so I know when to book flights"],"best_for":["Price-sensitive travelers planning trips weeks or months in advance","Users who want to optimize booking timing without manual daily price checking","Travelers with flexible dates seeking the cheapest available options"],"limitations":["Real-time accuracy depends entirely on third-party API reliability and update frequency; gaps in lesser-known destinations or regional carriers","Price monitoring adds latency (typically 5-30 minutes between price change and alert delivery) due to API polling intervals","Cannot predict or alert on dynamic pricing changes from airlines/hotels that occur within seconds","Historical price data retention is limited; trend analysis requires weeks of data collection before becoming reliable"],"requires":["Active integration with flight/hotel booking APIs (Skyscanner, Kayak, Booking.com, or similar)","User notification infrastructure (push notifications, email service, SMS gateway)","Time-series database for storing historical price data","Internet connectivity and API rate limits (typically 100-1000 requests/day per user)"],"input_types":["structured data (flight routes, dates, hotel details, price thresholds)","text (destination, travel dates)"],"output_types":["alerts (push notification, email, in-app message with price change details)","structured data (price history, trend analysis, booking recommendations)"],"categories":["automation-workflow","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_alcotravel__cap_2","uri":"capability://automation.workflow.weather.and.local.event.real.time.integration.with.itinerary.adaptation","name":"weather and local event real-time integration with itinerary adaptation","description":"Fetches real-time weather forecasts and local event data (concerts, festivals, sports events, cultural activities) from weather APIs (OpenWeatherMap, WeatherAPI) and event aggregators (Eventbrite, local tourism APIs) and cross-references against the user's planned itinerary. Detects conflicts (outdoor activity scheduled during rain) or opportunities (festival happening during travel dates) and suggests itinerary modifications with rationale. Uses geolocation and temporal matching to identify relevant events within the user's travel radius and dates.","intents":["Warn me if my outdoor hiking activity is scheduled during heavy rain and suggest alternatives","Show me what festivals or concerts are happening during my trip dates in my destination","Automatically adjust my itinerary if weather becomes severe (e.g., move outdoor activities indoors)"],"best_for":["Travelers in destinations with unpredictable weather who want proactive itinerary adjustments","Event-seeking travelers who want to discover and incorporate local activities into their plans","Users planning trips to multiple cities who need location-aware event discovery"],"limitations":["Weather forecast accuracy degrades beyond 7-10 days; long-term itineraries may have unreliable weather-based recommendations","Event data coverage is sparse in rural or lesser-known destinations; major cities have comprehensive event listings but small towns may have none","Automatic itinerary adaptation may conflict with user preferences or pre-booked activities; requires user confirmation before applying changes","Geolocation matching has a fixed radius (typically 5-50km); events outside this radius are missed"],"requires":["Real-time weather API access (OpenWeatherMap, WeatherAPI, or similar)","Event aggregator API access (Eventbrite, local tourism APIs, or web scraping of local event listings)","Geolocation data for all planned activities and destinations","Natural language generation for explaining itinerary modification rationale"],"input_types":["structured itinerary (activities with dates, times, locations)","text (destination names, travel dates)"],"output_types":["alerts (weather warnings, event recommendations)","structured data (modified itinerary with alternative activities)","text (explanation of why changes are recommended)"],"categories":["automation-workflow","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_alcotravel__cap_3","uri":"capability://planning.reasoning.multi.destination.trip.orchestration.with.transportation.routing","name":"multi-destination trip orchestration with transportation routing","description":"Coordinates multi-city itineraries by calculating optimal transportation routes (flights, trains, buses, driving) between destinations based on cost, time, and user preferences. Uses routing optimization algorithms (likely variants of traveling salesman problem solvers or dynamic programming) to sequence destinations and select transportation modes. Integrates with transportation booking APIs to fetch real-time availability and pricing, and embeds transportation logistics (travel time, layovers, border crossings) into the itinerary timeline.","intents":["Create a 3-week Europe trip visiting 5 cities with optimal flight/train routing and minimal travel time","Show me the cheapest way to get between my planned destinations using any combination of flights, trains, and buses","Build an itinerary that accounts for travel time between cities so activities don't overlap with transportation"],"best_for":["Multi-city travelers planning complex regional trips (Europe, Southeast Asia, etc.)","Budget travelers optimizing for lowest total transportation cost across multiple legs","Travelers with time constraints who need to minimize travel time between destinations"],"limitations":["Routing optimization is NP-hard; solutions for 10+ destinations may be suboptimal or computationally expensive, requiring heuristic approximations","Transportation API coverage varies by region; developing countries and remote areas have limited booking options","Border crossing requirements, visa processing times, and customs delays are not modeled; itineraries may be infeasible in practice","Real-time transportation availability changes frequently; itineraries generated hours before booking may have stale pricing or availability"],"requires":["Transportation booking APIs (Skyscanner, Kayak, Trainline, BlaBlaCar, or regional equivalents)","Routing optimization library (OR-Tools, Concorde TSP solver, or custom implementation)","Geolocation data for all destinations with travel time matrices","User preferences for transportation mode (flight, train, bus, driving) and cost/time tradeoffs"],"input_types":["structured data (list of destinations, travel dates, transportation preferences, budget)","text (destination names, travel dates, constraints)"],"output_types":["structured itinerary (day-by-day with transportation legs, arrival/departure times, costs)","text (routing explanation and optimization rationale)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_alcotravel__cap_4","uri":"capability://memory.knowledge.personalized.recommendation.learning.from.user.interaction.history","name":"personalized recommendation learning from user interaction history","description":"Builds user preference profiles by tracking interactions with generated itineraries (activities clicked, saved, booked, or skipped; ratings provided; time spent viewing recommendations). Uses collaborative filtering or content-based filtering to identify patterns in user preferences and applies these patterns to future itinerary generation. Stores preference embeddings in a user profile database and uses similarity matching to surface recommendations aligned with historical behavior.","intents":["The system should learn that I prefer cultural activities over nightlife and adjust future recommendations accordingly","Show me activities similar to ones I've previously saved or booked","Recommend destinations that match my historical travel style and preferences"],"best_for":["Repeat users planning multiple trips who benefit from accumulated preference learning","Users with specific travel styles (adventure, cultural, luxury, budget) who want consistent recommendations","Platforms seeking to improve recommendation quality and user retention through personalization"],"limitations":["Cold-start problem: new users have no interaction history, so recommendations are generic until sufficient data is collected (typically 5-10 interactions)","Preference drift: user preferences change over time; old historical data may become stale or misleading","Data sparsity: users interact with a tiny fraction of available activities/destinations; collaborative filtering may struggle with sparse matrices","Privacy concerns: storing detailed user preference profiles raises data retention and GDPR compliance issues"],"requires":["User interaction tracking infrastructure (event logging, analytics database)","User profile storage (database with preference embeddings or feature vectors)","Recommendation algorithm (collaborative filtering, content-based filtering, or hybrid approach)","Activity/destination feature vectors for similarity matching"],"input_types":["user interaction events (clicks, saves, bookings, ratings, time spent)","structured data (activity/destination metadata with features)"],"output_types":["user preference profile (embedding vector or feature weights)","ranked recommendations (activities/destinations sorted by predicted user preference)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_alcotravel__cap_5","uri":"capability://data.processing.analysis.budget.aware.activity.and.accommodation.filtering.with.cost.optimization","name":"budget-aware activity and accommodation filtering with cost optimization","description":"Filters activities, accommodations, and dining options based on user-specified daily or total trip budget by querying a pricing database and applying cost constraints. Uses dynamic programming or greedy algorithms to optimize activity selection within budget constraints, prioritizing high-rated or user-preferred activities when multiple options exist at similar price points. Provides cost breakdowns (accommodation, food, activities, transportation) and identifies cost-saving opportunities (free activities, budget accommodations, meal deals).","intents":["Show me only activities and restaurants within my $50/day budget for food and entertainment","Find the cheapest accommodation options that still meet my minimum quality/location requirements","Optimize my itinerary to stay within my total $2000 trip budget while maximizing experience quality"],"best_for":["Budget-conscious travelers with strict spending limits","Backpackers and solo travelers optimizing for cost-per-experience","Users planning trips with fixed total budgets who need cost allocation across categories"],"limitations":["Pricing data is often stale or inaccurate; real-time prices may differ from database values by 10-50%","Budget optimization assumes fixed prices; dynamic pricing (surge pricing, seasonal rates) is not modeled","Hidden costs (tips, taxes, service charges, transportation between activities) are often omitted from displayed prices","Quality/price tradeoffs are subjective; algorithm may recommend cheap but low-quality options if not properly weighted"],"requires":["Pricing database for activities, accommodations, and dining with regular updates","User budget constraints (daily budget, total trip budget, category-specific budgets)","Activity/accommodation quality ratings or user preference weights","Cost optimization algorithm (dynamic programming, greedy selection, or integer linear programming)"],"input_types":["structured data (budget constraints, activity/accommodation preferences, quality thresholds)","text (destination, travel dates, budget)"],"output_types":["filtered recommendations (activities/accommodations within budget)","structured data (cost breakdown by category, total trip cost estimate)","text (cost-saving suggestions and optimization rationale)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_alcotravel__cap_6","uri":"capability://memory.knowledge.collaborative.itinerary.sharing.and.social.feedback.aggregation","name":"collaborative itinerary sharing and social feedback aggregation","description":"Enables users to share generated itineraries with other users (via link, email, or social media) and collect feedback, ratings, and comments on activities and recommendations. Aggregates feedback across users to identify popular activities, problematic recommendations, and emerging travel trends. Uses feedback signals to improve recommendation quality and identify low-quality or outdated data in the activity/accommodation database.","intents":["Share my generated itinerary with friends and get their feedback before booking","See what other travelers thought of activities I'm considering for my trip","Contribute my travel experience feedback to help improve recommendations for future users"],"best_for":["Group travelers planning trips together and needing collaborative input","Community-driven platforms seeking to crowdsource travel data and improve recommendations","Users wanting social validation before committing to bookings"],"limitations":["Feedback quality varies widely; spam, fake reviews, and biased opinions can skew aggregated ratings","Feedback aggregation requires sufficient user volume; niche destinations or activities may have too few reviews to be meaningful","Privacy concerns: sharing itineraries may expose personal travel plans or preferences to other users","Feedback lag: aggregated insights take time to accumulate; real-time feedback is not available for new activities or destinations"],"requires":["User authentication and authorization for sharing and feedback","Itinerary storage and sharing infrastructure (database, link generation, access control)","Feedback collection UI (rating scales, comment fields, photo uploads)","Feedback aggregation and analysis pipeline (sentiment analysis, outlier detection, trend identification)"],"input_types":["structured itinerary data (activities, accommodations, dates, costs)","user feedback (ratings, comments, photos, experience descriptions)"],"output_types":["shared itinerary link (with access control and view/edit permissions)","aggregated feedback (average ratings, comment summaries, photo galleries)","insights (popular activities, problematic recommendations, emerging trends)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_alcotravel__cap_7","uri":"capability://automation.workflow.offline.itinerary.access.with.local.map.and.activity.data.caching","name":"offline itinerary access with local map and activity data caching","description":"Caches generated itineraries, maps, activity descriptions, and essential travel information (addresses, phone numbers, hours) locally on the user's device for offline access during travel. Uses data compression and selective caching to minimize storage footprint while maintaining usability. Syncs cached data with server when connectivity is restored to update prices, availability, and real-time information.","intents":["Access my itinerary and maps while traveling in areas with poor or no internet connectivity","Look up activity details, addresses, and phone numbers without using mobile data","Sync my itinerary changes back to the server when I regain connectivity"],"best_for":["International travelers visiting destinations with unreliable internet or expensive data plans","Users in remote areas or developing countries with limited connectivity","Travelers who want to minimize mobile data usage"],"limitations":["Cached data becomes stale; real-time information (prices, availability, weather, events) is not updated offline","Storage constraints on mobile devices limit the amount of data that can be cached; large trips or multi-destination itineraries may exceed available space","Offline map quality depends on map provider (Google Maps, OpenStreetMap); some regions have poor offline map coverage","Sync conflicts may occur if user modifies itinerary offline and server data has changed; conflict resolution requires user intervention"],"requires":["Mobile app with local storage capability (SQLite, Realm, or similar)","Map data provider with offline support (Google Maps offline, OpenStreetMap, Mapbox)","Data compression and selective caching strategy to minimize storage footprint","Sync mechanism to reconcile offline changes with server data"],"input_types":["structured itinerary data (activities, accommodations, dates, costs, locations)","map tiles and geographic data"],"output_types":["cached itinerary (stored locally on device)","offline maps (with activity locations marked)","sync status and conflict notifications"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["User profile with stated preferences (interests, budget, travel style, dietary restrictions)","Access to destination activity/accommodation database with pricing and ratings","Internet connectivity for real-time data lookups","Active integration with flight/hotel booking APIs (Skyscanner, Kayak, Booking.com, or similar)","User notification infrastructure (push notifications, email service, SMS gateway)","Time-series database for storing historical price data","Internet connectivity and API rate limits (typically 100-1000 requests/day per user)","Real-time weather API access (OpenWeatherMap, WeatherAPI, or similar)","Event aggregator API access (Eventbrite, local tourism APIs, or web scraping of local event listings)","Geolocation data for all planned activities and destinations"],"failure_modes":["Constraint satisfaction may fail or produce suboptimal results in destinations with limited data coverage or niche preferences","Preference learning requires historical user interaction data; cold-start users receive generic recommendations","Cannot dynamically adjust for real-time constraint changes (e.g., sudden budget reduction mid-trip) without re-generation","Real-time accuracy depends entirely on third-party API reliability and update frequency; gaps in lesser-known destinations or regional carriers","Price monitoring adds latency (typically 5-30 minutes between price change and alert delivery) due to API polling intervals","Cannot predict or alert on dynamic pricing changes from airlines/hotels that occur within seconds","Historical price data retention is limited; trend analysis requires weeks of data collection before becoming reliable","Weather forecast accuracy degrades beyond 7-10 days; long-term itineraries may have unreliable weather-based recommendations","Event data coverage is sparse in rural or lesser-known destinations; major cities have comprehensive event listings but small towns may have none","Automatic itinerary adaptation may conflict with user preferences or pre-booked activities; requires user confirmation before applying changes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:29.133Z","last_scraped_at":"2026-04-05T13:23:42.561Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=alcotravel","compare_url":"https://unfragile.ai/compare?artifact=alcotravel"}},"signature":"p7gsP9EPDG2uy5UJ3IQvXEAKyHQrjK/F0DMueDgWDxbWjnVTfOvqTwSKmQPyGpLTlA9Ouh4uyyainaZPiHRHCA==","signedAt":"2026-06-20T13:39:44.983Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/alcotravel","artifact":"https://unfragile.ai/alcotravel","verify":"https://unfragile.ai/api/v1/verify?slug=alcotravel","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}