{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_stockgpt","slug":"stockgpt","name":"StockGPT","type":"product","url":"https://www.askstockgpt.com","page_url":"https://unfragile.ai/stockgpt","categories":["data-analysis"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_stockgpt__cap_0","uri":"capability://text.generation.language.natural.language.financial.query.interface","name":"natural-language financial query interface","description":"Accepts free-form natural language questions about stocks, market trends, and financial metrics, then routes them through an LLM-based query parser that translates user intent into structured data requests. The system interprets colloquial financial terminology (e.g., 'Is Apple overvalued?', 'What's the tech sector doing?') and maps these to underlying market data APIs, returning conversational responses rather than raw database results.","intents":["Ask about a specific stock's current price and performance without knowing ticker symbols or financial APIs","Get plain-English explanations of market trends without reading financial jargon","Query multiple stocks or sectors in a single conversational turn","Understand why a stock moved without needing to parse earnings reports manually"],"best_for":["Retail investors with no financial database experience","Non-technical users seeking accessible market research","Hobbyists doing preliminary stock screening before deeper research"],"limitations":["LLM-based query parsing can misinterpret ambiguous financial questions, leading to incorrect data lookups","No structured query validation — malformed or nonsensical queries may return hallucinated or irrelevant results","Latency depends on LLM inference time plus downstream API calls; typical response time 2-5 seconds","Context window limitations mean complex multi-part financial questions may be truncated or misunderstood"],"requires":["Active internet connection for real-time market data APIs","Modern web browser with JavaScript enabled","No API keys or authentication required for free tier"],"input_types":["text (natural language questions)"],"output_types":["text (conversational responses)","structured data (stock prices, metrics, trends)"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stockgpt__cap_1","uri":"capability://data.processing.analysis.real.time.market.data.aggregation.and.caching","name":"real-time market data aggregation and caching","description":"Integrates with multiple real-time market data providers (likely Yahoo Finance, Alpha Vantage, or similar free/freemium APIs) to fetch current stock prices, volume, intraday movements, and sector performance. Implements a caching layer to reduce API call frequency and costs, with TTL-based invalidation to balance freshness against rate limits. The system normalizes data from heterogeneous sources into a unified schema before serving to the LLM context.","intents":["Get current stock prices and intraday performance without manual API integration","Query historical price trends and volatility metrics for technical analysis","Compare sector performance across multiple stocks in a single query","Access real-time volume and bid-ask spread data for liquidity assessment"],"best_for":["Developers building financial chatbots who want pre-integrated market data","Retail investors needing current data without paying for Bloomberg or professional terminals","Teams prototyping financial analysis tools with minimal infrastructure overhead"],"limitations":["Free market data APIs have strict rate limits (typically 5-500 calls/minute depending on provider), causing query throttling during high traffic","Data freshness varies by source; some free providers update prices with 15-20 minute delays rather than true real-time","No access to Level 2/3 order book data or institutional-grade market microstructure information","Historical data depth limited to 1-5 years depending on API tier; no intraday tick data for backtesting","Caching strategy may serve stale data during volatile market conditions if TTL is too long"],"requires":["Active API keys or OAuth tokens for underlying market data providers","Network connectivity to multiple external APIs","Cache infrastructure (in-memory or distributed) to handle concurrent queries"],"input_types":["ticker symbols (text)","date ranges (ISO 8601 format)","metric names (e.g., 'price', 'volume', 'pe_ratio')"],"output_types":["JSON (normalized market data)","time-series data (OHLCV candles)","aggregated metrics (sector averages, volatility indices)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stockgpt__cap_2","uri":"capability://text.generation.language.ai.generated.financial.analysis.and.interpretation","name":"ai-generated financial analysis and interpretation","description":"Takes aggregated market data and user queries, then uses an LLM (likely GPT-3.5 or similar) to generate contextual financial analysis, trend interpretation, and investment thesis summaries. The system constructs prompts that inject current market data, historical context, and financial metrics into the LLM's context window, then post-processes outputs to extract key insights. No human financial analyst reviews outputs before serving to users.","intents":["Get AI-generated explanations of why a stock price moved today","Understand sector rotation trends and macroeconomic drivers without reading financial news","Generate quick investment theses for stocks based on fundamental metrics","Identify potential risks or red flags in a company's financials through AI pattern recognition"],"best_for":["Retail investors seeking supplementary analysis to validate their own research","Financial hobbyists who want AI-assisted learning about market dynamics","Developers building educational financial tools where accuracy is secondary to accessibility"],"limitations":["LLMs hallucinate financial data, dates, and metrics; AI may cite non-existent earnings reports or fabricate analyst consensus","No source attribution or citation of underlying data; users cannot verify where insights originate","Training data cutoff means LLM lacks knowledge of recent market events, earnings surprises, or regulatory changes","Bias toward bullish narratives in training data; AI may overstate upside potential and understate downside risks","No accountability or liability if AI-generated analysis leads to financial losses; explicitly not investment advice","Cannot perform complex multi-step financial modeling (DCF, Monte Carlo simulations) that require precise numerical computation"],"requires":["Access to an LLM API (OpenAI, Anthropic, or self-hosted model)","Aggregated market data in structured format to inject into prompts","Prompt engineering expertise to construct reliable financial analysis prompts"],"input_types":["market data (JSON)","user query (text)","historical context (time-series data)"],"output_types":["text (conversational analysis)","structured insights (risk factors, catalysts, thesis summary)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stockgpt__cap_3","uri":"capability://data.processing.analysis.multi.stock.comparative.analysis","name":"multi-stock comparative analysis","description":"Enables users to query multiple stocks simultaneously and receive comparative metrics (valuation ratios, growth rates, sector positioning, relative performance). The system batches ticker lookups to minimize API calls, aggregates results into a unified comparison table, and uses the LLM to generate narrative comparisons (e.g., 'Stock A is cheaper than Stock B on a P/E basis but has slower growth'). Supports sector-level aggregation to identify relative strength across industries.","intents":["Compare valuation metrics across 3-5 stocks to identify relative bargains","Understand how a stock's performance ranks within its sector","Analyze sector rotation by comparing relative strength across industries","Screen for stocks matching specific criteria (e.g., 'high dividend yield, low debt') without manual spreadsheet work"],"best_for":["Retail investors doing preliminary stock screening before deeper due diligence","Portfolio managers comparing peer companies for relative value analysis","Financial educators teaching comparative valuation concepts"],"limitations":["Comparison logic is LLM-generated and may miss nuanced differences in accounting standards, business models, or market conditions","Limited to metrics available from free market data APIs; cannot access proprietary research, insider transactions, or institutional holdings","No statistical significance testing; AI may overstate differences between stocks with similar metrics","Sector classification varies across data providers; same stock may be classified differently, breaking comparisons","Cannot handle complex multi-factor screening (e.g., 'high growth AND low debt AND strong FCF') reliably due to LLM reasoning limitations"],"requires":["Multiple valid ticker symbols as input","Real-time market data for all queried stocks","Sector classification data from underlying market data provider"],"input_types":["ticker symbols (comma-separated or list format)","comparison criteria (text, e.g., 'valuation', 'growth', 'dividend')"],"output_types":["comparison table (structured data)","narrative analysis (text)","ranking or scoring (relative positioning)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stockgpt__cap_4","uri":"capability://memory.knowledge.conversational.session.memory.and.context.retention","name":"conversational session memory and context retention","description":"Maintains conversation history within a user session, allowing follow-up questions that reference previous queries without re-stating context (e.g., 'How does that compare to its 52-week high?' after asking about current price). The system stores recent queries and responses in session state, injects relevant context into subsequent LLM prompts, and manages context window size to avoid exceeding token limits. No persistent storage across sessions; history is cleared when user closes the browser.","intents":["Ask follow-up questions about a stock without repeating the ticker symbol","Build on previous analysis (e.g., 'Is that valuation justified given the growth rate?')","Maintain coherent multi-turn conversations about related stocks or sectors","Refine queries based on AI responses without starting over"],"best_for":["Casual users exploring stocks through conversational discovery","Retail investors conducting iterative research sessions","Educational use cases where conversational flow aids learning"],"limitations":["Session memory is ephemeral; closing the browser or refreshing the page clears all history","No cross-device persistence; users cannot continue conversations on mobile after starting on desktop","Context window management may truncate early conversation turns if session becomes too long, losing important context","No ability to save, export, or reference past research sessions for audit trails or portfolio tracking","LLM may misinterpret pronouns or references if context is ambiguous (e.g., 'it' could refer to multiple stocks)"],"requires":["Client-side session storage (browser localStorage or in-memory state)","LLM with sufficient context window to accommodate conversation history (typically 4K-8K tokens)"],"input_types":["text (follow-up questions)","implicit references (pronouns, previous context)"],"output_types":["text (contextual responses)","market data (referenced from previous queries)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stockgpt__cap_5","uri":"capability://data.processing.analysis.sector.and.industry.trend.aggregation","name":"sector and industry trend aggregation","description":"Aggregates market data across multiple stocks within a sector to compute sector-level metrics (average P/E, median growth rate, sector momentum, relative strength vs. S&P 500). Uses LLM to interpret these aggregates and identify sector rotation patterns, leadership changes, and macroeconomic drivers. Supports hierarchical sector classification (e.g., Technology > Software > SaaS) to enable drill-down analysis.","intents":["Understand which sectors are outperforming or underperforming the broader market","Identify sector rotation trends (e.g., 'Tech is weakening, Healthcare is strengthening')","Find the strongest stocks within a sector for relative strength trading","Analyze macroeconomic drivers affecting entire sectors (e.g., 'Rising rates hurt Utilities')"],"best_for":["Tactical traders identifying sector rotation opportunities","Portfolio managers rebalancing sector allocations","Macro investors understanding economic cycle positioning"],"limitations":["Sector classification is provider-dependent; different data sources may classify the same stock into different sectors, breaking aggregations","Aggregation logic (average vs. median vs. weighted) is not transparent; users cannot verify how sector metrics are computed","LLM-generated sector narratives may overfit to recent price movements and miss fundamental drivers","No weighting by market cap; equal-weighted sector metrics can be skewed by small-cap outliers","Historical sector data is limited to the depth of underlying market data APIs (typically 1-5 years)"],"requires":["Market data for 20+ stocks per sector to generate reliable aggregates","Sector classification taxonomy from underlying data provider","Real-time price and fundamental data for all constituent stocks"],"input_types":["sector name (text)","time period (date range)","aggregation metric (e.g., 'momentum', 'valuation')"],"output_types":["sector metrics (JSON: average P/E, growth rate, momentum)","narrative analysis (text: sector trends and drivers)","constituent rankings (stocks ranked by strength within sector)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stockgpt__cap_6","uri":"capability://data.processing.analysis.fundamental.metric.extraction.and.normalization","name":"fundamental metric extraction and normalization","description":"Extracts key financial metrics (P/E ratio, dividend yield, debt-to-equity, ROE, free cash flow, earnings growth) from market data APIs and normalizes them into a consistent schema. Handles missing data gracefully (e.g., dividend yield is N/A for non-dividend stocks) and computes derived metrics (e.g., PEG ratio from P/E and growth rate). Provides both raw metrics and LLM-generated interpretations (e.g., 'P/E of 15 is below historical average, suggesting undervaluation').","intents":["Get a stock's key financial metrics without manually looking up earnings reports","Understand what metrics mean in plain English (e.g., 'What does a P/E of 20 tell me?')","Compare metrics across stocks to identify relative value","Screen stocks based on fundamental criteria (e.g., 'dividend yield > 3%')"],"best_for":["Retail investors learning fundamental analysis without financial background","Value investors screening for undervalued stocks","Developers building financial analysis tools with pre-computed metrics"],"limitations":["Metrics are only as current as underlying market data APIs; earnings data may be 1-3 months stale","Free APIs provide limited fundamental data; professional-grade metrics (e.g., adjusted earnings, normalized FCF) are unavailable","No accounting adjustments for one-time items, stock-based compensation, or other non-recurring events","Derived metrics (e.g., PEG ratio) are computed from potentially stale growth estimates, reducing reliability","LLM interpretations of metrics can be misleading (e.g., 'low P/E means undervalued' ignores quality, growth, and risk factors)"],"requires":["Fundamental data from market data APIs (earnings, dividends, debt, equity)","Growth rate estimates (typically from analyst consensus or historical averages)","Normalization logic to handle missing or inconsistent data"],"input_types":["ticker symbol (text)","metric name (e.g., 'pe_ratio', 'dividend_yield')"],"output_types":["numeric metrics (float)","interpreted metrics (text: 'This P/E is below the sector average')","metric comparisons (relative to sector, historical average, peer group)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stockgpt__cap_7","uri":"capability://automation.workflow.price.alert.and.watchlist.management","name":"price alert and watchlist management","description":"Allows users to create watchlists of stocks and set price-based alerts (e.g., 'notify me if Apple drops below $150'). Stores watchlist state in browser session or optional user account, periodically polls market data APIs to check alert conditions, and triggers notifications when thresholds are breached. Supports multiple alert types (price level, percentage change, volume spike) and notification channels (in-app, email if account is linked).","intents":["Monitor stocks without manually checking prices throughout the day","Get notified when a stock hits a target buy or sell price","Track a curated list of stocks relevant to a portfolio or research thesis","Set alerts for unusual volume or volatility spikes"],"best_for":["Retail investors managing personal watchlists without paying for premium tools","Swing traders monitoring entry/exit points","Portfolio managers tracking peer companies or sector benchmarks"],"limitations":["Alert polling frequency is limited by API rate limits; alerts may trigger 5-15 minutes after price threshold is breached","No persistent storage in free tier; watchlists are cleared when browser session ends unless user creates account","Email notifications require user account creation and email verification, adding friction","No advanced alert logic (e.g., 'alert if P/E drops below 15 AND price is above 200-day MA'); only simple threshold-based alerts","Cannot set alerts for intraday price movements; only end-of-day or hourly updates available","No historical alert tracking or audit trail; users cannot review past alerts or alert performance"],"requires":["Browser session storage or optional user account for persistence","Periodic polling mechanism (likely server-side cron job) to check alert conditions","Email service integration for notification delivery (if account-based alerts enabled)","Real-time or near-real-time market data API access"],"input_types":["ticker symbol (text)","alert type (price level, percentage change, volume)","threshold value (numeric)"],"output_types":["alert notifications (in-app toast, email)","watchlist state (JSON: list of stocks and alerts)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stockgpt__cap_8","uri":"capability://data.processing.analysis.earnings.and.event.calendar.integration","name":"earnings and event calendar integration","description":"Integrates with earnings calendar data (likely from free sources like Yahoo Finance or Alpha Vantage) to display upcoming earnings dates, dividend ex-dates, and corporate events. Allows users to filter stocks by event type and date range, and provides LLM-generated context about why these events matter (e.g., 'Earnings miss could trigger a 5-10% drop'). Supports setting alerts for upcoming events.","intents":["Find stocks with earnings coming up this week to plan trading activity","Understand which stocks are paying dividends and when ex-dates occur","Identify catalysts that could drive stock price movements","Plan portfolio rebalancing around corporate events"],"best_for":["Event-driven traders timing entries/exits around earnings","Income investors tracking dividend schedules","Portfolio managers planning rebalancing around corporate actions"],"limitations":["Event calendar data is only as current as underlying free APIs; some events may be missing or have incorrect dates","No access to guidance changes, analyst revisions, or other forward-looking information that could affect event impact","LLM-generated context about event impact is speculative and not based on historical analysis or statistical models","Cannot predict earnings surprises or market reactions; only provides historical context","Limited to major events (earnings, dividends, splits); no access to insider transactions, SEC filings, or regulatory events"],"requires":["Earnings calendar data from market data API","Corporate action data (dividends, splits, mergers)","Historical event impact data (optional, for LLM context generation)"],"input_types":["ticker symbol (text)","event type (earnings, dividend, split)","date range (ISO 8601)"],"output_types":["event list (JSON: date, type, stock)","event context (text: why this event matters)","alert notifications (when events occur)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Active internet connection for real-time market data APIs","Modern web browser with JavaScript enabled","No API keys or authentication required for free tier","Active API keys or OAuth tokens for underlying market data providers","Network connectivity to multiple external APIs","Cache infrastructure (in-memory or distributed) to handle concurrent queries","Access to an LLM API (OpenAI, Anthropic, or self-hosted model)","Aggregated market data in structured format to inject into prompts","Prompt engineering expertise to construct reliable financial analysis prompts","Multiple valid ticker symbols as input"],"failure_modes":["LLM-based query parsing can misinterpret ambiguous financial questions, leading to incorrect data lookups","No structured query validation — malformed or nonsensical queries may return hallucinated or irrelevant results","Latency depends on LLM inference time plus downstream API calls; typical response time 2-5 seconds","Context window limitations mean complex multi-part financial questions may be truncated or misunderstood","Free market data APIs have strict rate limits (typically 5-500 calls/minute depending on provider), causing query throttling during high traffic","Data freshness varies by source; some free providers update prices with 15-20 minute delays rather than true real-time","No access to Level 2/3 order book data or institutional-grade market microstructure information","Historical data depth limited to 1-5 years depending on API tier; no intraday tick data for backtesting","Caching strategy may serve stale data during volatile market conditions if TTL is too long","LLMs hallucinate financial data, dates, and metrics; AI may cite non-existent earnings reports or fabricate analyst consensus","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":"2026-05-24T12:16:33.648Z","last_scraped_at":"2026-04-05T13:23:42.559Z","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=stockgpt","compare_url":"https://unfragile.ai/compare?artifact=stockgpt"}},"signature":"pzt5uPGA1LxdlDcZAvqF5ftSQXzblgkR3JRVUwrJ+KgUkJruTHokkeATHbWE0l2Jex8QZitwKtJjBcmo6HO/Dw==","signedAt":"2026-06-21T00:05:50.103Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/stockgpt","artifact":"https://unfragile.ai/stockgpt","verify":"https://unfragile.ai/api/v1/verify?slug=stockgpt","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"}}