{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_moodfood","slug":"moodfood","name":"MoodFood","type":"product","url":"https://www.moodfood.app","page_url":"https://unfragile.ai/moodfood","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_moodfood__cap_0","uri":"capability://text.generation.language.mood.state.to.food.recommendation.inference","name":"mood-state-to-food-recommendation inference","description":"Converts user-reported emotional states into personalized food suggestions through a conversational chatbot interface that captures mood context, intensity, and triggers. The system likely uses a multi-step inference pipeline: mood classification (happy, stressed, anxious, tired, etc.) → contextual enrichment (time of day, recent activities, dietary restrictions) → recommendation ranking via a mood-food correlation model trained on user behavior patterns and nutritional science heuristics. The chatbot maintains conversational context across turns to refine recommendations without requiring explicit structured input.","intents":["I want food suggestions that match how I'm feeling right now, not just my calorie goals","I need to understand the connection between my emotional state and my eating patterns","I want a conversational way to explore food choices without filling out forms or tracking macros","I'm stress-eating and want AI to help me pick foods that actually address my mood, not just fill my stomach"],"best_for":["Wellness-conscious individuals with stress-eating or emotional-eating patterns","Users seeking psychological awareness of eating triggers rather than calorie-centric nutrition","People who prefer conversational interfaces over traditional food-logging dashboards","Individuals exploring the intersection of mental health and dietary choices"],"limitations":["Mood-to-food correlations lack peer-reviewed scientific validation at scale; effectiveness depends on unproven heuristics","Requires honest, consistent mood self-reporting from users; no objective mood detection (no biometric integration)","Recommendations may reinforce emotional eating patterns rather than address root causes if not paired with behavioral coaching","Cold-start problem: new users receive generic recommendations until sufficient mood-food history is logged (typically 2-4 weeks)"],"requires":["User willingness to log mood state daily or multiple times per week","Internet connectivity for API calls to recommendation engine","No specific device or OS requirements mentioned; web/mobile app access assumed"],"input_types":["natural language mood descriptions (text via chatbot)","mood intensity/scale (implicit or explicit)","dietary restrictions/preferences (text or selection)"],"output_types":["food recommendations (structured list with names, descriptions)","mood-food correlation insights (text explanations)","conversational responses (natural language)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_moodfood__cap_1","uri":"capability://text.generation.language.conversational.mood.logging.chatbot.interface","name":"conversational mood-logging chatbot interface","description":"Implements a natural-language chatbot that guides users through mood capture without requiring explicit form submission. The chatbot likely uses intent recognition (via NLU or LLM-based classification) to extract mood keywords, intensity, context, and triggers from free-form text input. It maintains conversation state across multiple turns, asking clarifying follow-up questions (e.g., 'Is this stress from work or personal life?') to enrich the mood profile before generating recommendations. The interface abstracts away structured data entry, making mood logging feel like a casual conversation rather than a clinical assessment.","intents":["I want to tell an AI how I'm feeling in natural language without filling out a mood survey","I need the chatbot to ask follow-up questions to understand my mood better","I want mood logging to feel conversational and low-friction, not like a medical questionnaire","I need the chatbot to remember context from previous conversations to personalize its responses"],"best_for":["Users who find traditional mood-tracking apps (with sliders/scales) tedious or clinical","People who prefer conversational interaction over structured data entry","Individuals seeking a more human-like experience in wellness apps"],"limitations":["NLU accuracy depends on user articulation; ambiguous or vague mood descriptions may be misclassified","Conversation state management adds latency (~500ms-2s per turn) compared to instant form submission","No multimodal mood detection (voice tone, facial expression, biometrics); relies entirely on text input","Context window limitations may cause the chatbot to forget earlier conversation details in long sessions"],"requires":["Active internet connection for real-time chatbot API calls","User ability to articulate mood in natural language (language-dependent; likely English-first)","Freemium account or paid subscription to access chatbot"],"input_types":["natural language text (mood descriptions, context, triggers)","implicit mood signals (word choice, punctuation, urgency)"],"output_types":["clarifying questions (natural language)","mood classification (structured: happy, stressed, anxious, tired, etc.)","food recommendations (text + structured data)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_moodfood__cap_2","uri":"capability://data.processing.analysis.mood.food.correlation.learning.and.personalization","name":"mood-food correlation learning and personalization","description":"Builds a user-specific model of mood-to-food associations by aggregating historical mood logs and food recommendations over time. The system likely tracks which food recommendations users accept/reject, paired with their reported mood state, to learn individual preferences (e.g., 'User tends to prefer comfort foods when stressed, but lighter foods when anxious'). This personalization layer may use collaborative filtering (comparing user patterns to similar users) or content-based filtering (matching mood-food pairs to nutritional/sensory properties). The model improves recommendation relevance as more data is logged, but requires sufficient historical data (cold-start problem) to become effective.","intents":["I want the app to learn my personal mood-food patterns over time and get better at recommending foods I'll actually eat","I want to see insights about my emotional eating triggers and how my food choices correlate with my mood","I want recommendations tailored to my unique mood-food associations, not generic suggestions","I want the app to remember that I prefer certain foods when stressed vs. when I'm happy"],"best_for":["Long-term users committed to consistent mood logging (2+ months minimum for meaningful personalization)","Individuals with distinct mood-food patterns they want to understand and optimize","Users seeking behavioral insights into their emotional eating habits"],"limitations":["Cold-start problem: new users receive generic recommendations until 20-50+ mood-food data points are logged","Personalization quality depends on user honesty and consistency; sparse or inconsistent logging degrades model accuracy","No A/B testing or validation mentioned; unclear if personalized recommendations are statistically better than generic ones","User data retention and privacy implications: mood-food history is sensitive personal data requiring secure storage","Potential for reinforcing unhealthy eating patterns if the model learns to recommend high-calorie comfort foods during stress without behavioral intervention"],"requires":["Minimum 2-4 weeks of consistent mood logging (3-5 entries per week) for meaningful personalization","User account with historical mood-food data stored in backend database","Freemium or paid subscription tier"],"input_types":["historical mood logs (timestamp, mood state, context, intensity)","food recommendation acceptance/rejection signals (implicit: user selects recommendation; explicit: user rates recommendation)","user dietary preferences and restrictions (stored profile data)"],"output_types":["personalized food recommendations (ranked by predicted user preference)","mood-food correlation insights (text: 'You tend to prefer comfort foods when stressed')","behavioral patterns and trends (charts, statistics)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_moodfood__cap_3","uri":"capability://data.processing.analysis.mood.based.food.filtering.and.dietary.constraint.integration","name":"mood-based food filtering and dietary constraint integration","description":"Filters food recommendations based on user-reported dietary restrictions, allergies, and preferences while maintaining mood-relevance. The system likely maintains a constraint satisfaction layer that intersects mood-based recommendations with a user's dietary profile (vegetarian, gluten-free, nut allergy, calorie limits, etc.). This prevents recommending foods that match the mood but violate dietary constraints. The filtering may also consider time-of-day context (breakfast vs. dinner recommendations differ) and meal type (snack vs. full meal) to ensure recommendations are contextually appropriate.","intents":["I want mood-based food suggestions that respect my dietary restrictions (vegetarian, gluten-free, etc.)","I need the app to avoid recommending foods I'm allergic to, even if they'd be perfect for my mood","I want recommendations tailored to the time of day and meal type I'm looking for","I want to set calorie or macro limits and get mood-based suggestions within those constraints"],"best_for":["Users with dietary restrictions (allergies, intolerances, ethical preferences) who still want mood-based recommendations","People managing multiple constraints simultaneously (e.g., vegetarian + gluten-free + low-sodium)","Users seeking context-aware recommendations (breakfast vs. dinner, snack vs. meal)"],"limitations":["Constraint satisfaction adds computational overhead; complex constraint sets may slow recommendation generation","Limited to constraints users explicitly specify; implicit preferences (e.g., 'I don't like cilantro') may not be captured","No dynamic constraint learning; if user rejects a recommendation due to an unstated constraint, the system may not infer the constraint","Unclear if the app supports custom constraints or only pre-defined categories (vegetarian, gluten-free, etc.)"],"requires":["User profile with dietary restrictions and preferences configured upfront","Food database with nutritional metadata (allergens, ingredients, macros, meal type tags)","Constraint satisfaction engine (likely rule-based or constraint programming)"],"input_types":["dietary restrictions (categorical: vegetarian, vegan, gluten-free, etc.)","allergies and intolerances (text or selection)","meal context (time of day, meal type: breakfast/lunch/dinner/snack)","optional calorie or macro limits (numeric)"],"output_types":["filtered food recommendations (subset of all recommendations that satisfy constraints)","constraint violation warnings (if user requests a food that violates their profile)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_moodfood__cap_4","uri":"capability://data.processing.analysis.mood.food.history.tracking.and.insight.generation","name":"mood-food history tracking and insight generation","description":"Maintains a persistent log of user mood entries and food recommendations over time, enabling historical analysis and trend detection. The system stores mood state, timestamp, context, recommended foods, and user acceptance/rejection signals. It then generates insights by analyzing patterns: identifying recurring mood-food associations ('You eat pasta when stressed'), detecting seasonal or temporal trends ('Your stress levels spike on Mondays'), and surfacing behavioral patterns ('You reject salads when anxious, but accept them when happy'). Insights are likely presented as natural-language summaries or visualizations (charts, heatmaps) to help users understand their emotional eating habits.","intents":["I want to see a history of my mood logs and food recommendations to understand my patterns","I want the app to generate insights about my emotional eating triggers and habits","I want to see trends in my mood and food choices over weeks or months","I want to export my mood-food data for analysis or sharing with a therapist/nutritionist"],"best_for":["Users committed to long-term mood tracking and self-awareness","Individuals working with therapists or nutritionists who want to share data","People interested in behavioral analytics and personal data insights"],"limitations":["Insight quality depends on data volume and consistency; sparse logging produces unreliable patterns","No causal inference: the app can identify correlations (mood X → food Y) but cannot determine causation or confounding factors","Privacy risk: mood-food history is sensitive personal data; storage and access controls must be robust","Unclear if insights are generated in real-time or batch-processed; real-time generation may add latency","Limited to patterns the app's analytics engine is programmed to detect; unexpected or novel patterns may be missed"],"requires":["Persistent user account with backend database for historical data storage","Minimum 2-4 weeks of mood-food data for meaningful trend detection","Analytics engine (likely rule-based or statistical) to detect patterns"],"input_types":["historical mood logs (timestamp, mood state, context, intensity)","food recommendation acceptance/rejection signals","user profile data (dietary restrictions, preferences)"],"output_types":["mood-food history (searchable, filterable log)","trend insights (text summaries: 'You eat pasta 80% of the time when stressed')","visualizations (charts, heatmaps, timelines)","exportable data (CSV, JSON for external analysis)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_moodfood__cap_5","uri":"capability://automation.workflow.freemium.access.model.with.feature.gating","name":"freemium access model with feature gating","description":"Implements a freemium business model where core mood-logging and basic recommendations are free, with premium features (advanced insights, export, priority support) behind a paywall. The system likely gates features at the API or UI level, checking user subscription status before allowing access to premium endpoints. Free users may have rate limits (e.g., 5 mood logs per week) or feature restrictions (e.g., insights only available to premium users). This model reduces friction for user acquisition while monetizing engaged users who derive value from the service.","intents":["I want to try the app without paying to see if mood-based food suggestions work for me","I want access to basic mood logging and recommendations for free, with optional premium features","I want to upgrade to premium if I find the app valuable and want advanced insights"],"best_for":["Casual users exploring mood-based nutrition without commitment","Freemium-sensitive audiences (students, budget-conscious individuals)","Users who want to trial the app before investing in a subscription"],"limitations":["Free tier limitations (rate limits, feature gating) may frustrate users and drive churn if perceived as too restrictive","Unclear what features are free vs. premium; feature gating strategy not specified in available information","Freemium model may attract low-engagement users who never convert to paid, increasing support costs","Premium features must provide sufficient value to justify subscription cost; if insights are weak or recommendations don't improve, users won't upgrade"],"requires":["User account system with subscription status tracking","Payment processing integration (Stripe, Apple Pay, Google Play, etc.)","Feature gating logic in backend API and frontend UI"],"input_types":["user subscription status (free, premium, trial)","feature access requests (API calls to gated endpoints)"],"output_types":["feature access granted/denied (based on subscription status)","upgrade prompts (UI messages encouraging free users to upgrade)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["User willingness to log mood state daily or multiple times per week","Internet connectivity for API calls to recommendation engine","No specific device or OS requirements mentioned; web/mobile app access assumed","Active internet connection for real-time chatbot API calls","User ability to articulate mood in natural language (language-dependent; likely English-first)","Freemium account or paid subscription to access chatbot","Minimum 2-4 weeks of consistent mood logging (3-5 entries per week) for meaningful personalization","User account with historical mood-food data stored in backend database","Freemium or paid subscription tier","User profile with dietary restrictions and preferences configured upfront"],"failure_modes":["Mood-to-food correlations lack peer-reviewed scientific validation at scale; effectiveness depends on unproven heuristics","Requires honest, consistent mood self-reporting from users; no objective mood detection (no biometric integration)","Recommendations may reinforce emotional eating patterns rather than address root causes if not paired with behavioral coaching","Cold-start problem: new users receive generic recommendations until sufficient mood-food history is logged (typically 2-4 weeks)","NLU accuracy depends on user articulation; ambiguous or vague mood descriptions may be misclassified","Conversation state management adds latency (~500ms-2s per turn) compared to instant form submission","No multimodal mood detection (voice tone, facial expression, biometrics); relies entirely on text input","Context window limitations may cause the chatbot to forget earlier conversation details in long sessions","Cold-start problem: new users receive generic recommendations until 20-50+ mood-food data points are logged","Personalization quality depends on user honesty and consistency; sparse or inconsistent logging degrades model accuracy","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":"2026-05-24T12:16:31.858Z","last_scraped_at":"2026-04-05T13:23:42.560Z","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=moodfood","compare_url":"https://unfragile.ai/compare?artifact=moodfood"}},"signature":"iKyiCMuZGA8ZXGnXD5hCjyrr833aM6D7RedEFd4Q6YuICe3glYCLiJ9LQMKx/1NN1UTL+d1B0QdCIVu9xrxrAg==","signedAt":"2026-06-20T18:58:58.869Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/moodfood","artifact":"https://unfragile.ai/moodfood","verify":"https://unfragile.ai/api/v1/verify?slug=moodfood","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"}}