bigdata_company_tearsheet
ChatGPTReturns a comprehensive company tearsheet with financial data, market intelligence, and analyst coverage. ⚠️ PREREQUISITE: Call find_securities first to get rp_entity_id and company_type. Workflow for company tearsheet: 1. Call find_securities → get "id" (use as rp_entity_id) and "type" (use as company_type) 2. Call this tool with both values 3. Optionally call bigdata_search for supporting content When to Use: Company financials, earnings, revenue, valuation, balance sheet, cash flow, analyst ratings, price targets, ESG data, risk assessment, real time sentiment and media attention, or any financial analysis. Data Returned by Company Type: → PUBLIC companies (from financial data APIs): • Company profile & real-time quote (price, market cap, volume) • Price performance (52-week range, moving averages, price changes over time) • Competitors comparison (symbol, price, market cap) • Financial statements (income, balance sheet, cash flow) • Key metrics & ratios (P/E, ROE, ROA, debt ratios, margins) • Analyst ratings & recommendations (Strong Buy/Buy/Hold/Sell/Strong Sell) • Analyst price targets (consensus, median, high, low) • Analyst estimates (forward revenue & EPS projections for next 8 quarters) • Latest earnings release (actual vs estimated, surprise %) • Earnings calendar & upcoming earnings dates • Dividend history (dates, amounts, yields, frequency) • Revenue segmentation by product and geography • Sentiment data (last 24h real-time, company-specific news) • Fund trends & institutional holdings (top buyers/sellers, position changes, options activity) • ESG performance scores (Environmental, Social, Governance scores & classifications) • ESG historical trends (yearly ESG scores, performance buckets, sector comparisons) • Workforce signals & employee trend metrics: modeled employee counts and net in/out workforce changes with time-series comparisons (MoM, YoY, trailing-12-month), enabling company growth and contraction analysis regarding job market trends. → PRIVATE companies: • Company overview (legal name, status, founded date, headcount, tags, founders, description) • Contact details (phone, email) & headquarters location (region, country, categories) • Web & social links (website, LinkedIn, Twitter/X, Facebook) • Crunchbase rank with trend changes (7/30/90-day) • Sentiment data (last 24h real-time, company-specific news) • Leadership team (executives, board members, advisors with roles and start dates) • Funding rounds (valuation, total raised, round details with investors and lead investors) • Investments made & acquisitions (target companies, amounts, status) • Founder profiles (investment activity, portfolio, exits) • Workforce signals & employee trend metrics: modeled employee counts and net in/out workforce changes with time-series comparisons (MoM, YoY, trailing-12-month), enabling company growth and contraction analysis regarding job market trends. Filtering: The sections parameter exists for cases where the user EXPLICITLY references specific sections by name or concept. Do NOT use it for general tearsheet requests — omit it to return the complete tearsheet. Data sources (LLM instruction): When presenting the tearsheet results to the user, identify the data sources mentioned or implied in the tool response content and add a "Data sources" section at the bottom listing them.
bigdata_country_tearsheet
ChatGPTReturns a comprehensive country economic tearsheet with a sectoral macroeconomic overview, economic calendar data, G7 peer comparison, market indices, currency information, and US Treasury yields. When to Use: Use this tool when users ask about: - Upcoming economic events or recent economic releases - Economic calendar events, upcoming economic releases, or economic indicators - Country economic data, GDP, CPI, unemployment rates, interest rates - Economic comparisons between countries (G7 peer comparison) - Sector-specific economic data (housing, manufacturing, retail, trade, energy, etc.) - Central bank decisions, interest rates, or monetary policy - Fiscal policy, government budget, or debt data - Government securities auctions or bond auction results - Stock market indices, market performance, or equity market data for a country - Comparisons between a country's primary index and regional markets - Currency data, forex rates, or exchange rates for a country's base currency - Currency performance, trends, and cross-currency positioning - US Treasury yields, yield curve data, or bond market information (US ONLY) - Yield curve analysis, spread analysis, or interest rate trends for US Treasuries Data Returned: - Upcoming Events: Economic calendar events - Macroeconomic Overview: Sectoral breakdown of recent economic indicators, organized into: • GDP & Growth • Labor Market • Inflation & Prices • Consumer & Business Sentiment • Manufacturing & Services • Housing Market • Retail & Consumer Spending • Trade & International • Inventories & Supply Chain • Credit & Monetary • Central Bank & Monetary Policy • Regional Economic Indicators • Energy & Commodities • Government Securities Auctions • Fiscal Policy Each section shows the latest releases with actual vs. consensus values, surprise %, frequency, and impact level. - Country Comparison: G7 peer comparison table with key economic indicators: • GDP Growth (QoQ, YoY, Annualized) • CPI (YoY) • Unemployment Rate • Interest Rate - Market Indices: Real-time stock market index data including: • Primary Index: Country's main stock market index (e.g., S&P 500 for US, DAX for Germany) • Regional Comparisons: Related regional indices for context • Metrics: Current price, daily change, % change, 52-week high/low, 50-day and 200-day moving averages - Currencies: Real-time forex market data including: • Spot Overview: Reference pair, current spot price, previous close, 24h % change, direction • Trend & Momentum: 50-day MA, 200-day MA, MA spread, distance to 52-week high/low • Performance Snapshot: Returns over multiple horizons (1D, 5D, 1M, 3M, YTD, 1Y) with trend classification • Cross-Currency Positioning: Multiple currency pairs with rates, previous close, 24h % change, and strength indicators relative to the country's base currency - Yields (US ONLY): Real-time US Treasury yield curve data including: • Yield Curve Overview: Multiple maturities (1M, 3M, 6M, 1Y, 2Y, 3Y, 5Y, 7Y, 10Y, 20Y, 30Y) • Current Rates: Latest yield rates for each maturity • Daily Changes: Basis point changes and percentage changes from previous close • Yield Curve Analysis: Spread analysis (10Y-2Y, 30Y-2Y) and curve shape indicators • Historical Context: Comparison to recent highs/lows and trend indicators IMPORTANT - Country Parameter: - This tool accepts ONLY ONE country at a time - If the user mentions a specific country (e.g., "US", "UK", "Germany", "France"), use that country code in the country parameter - If the user mentions multiple countries, use only the first/primary country mentioned - Use the exact 2-letter country codes listed below (e.g., "US" for United States, "DE" for Germany, "EMU" for Eurozone) Data sources (LLM instruction): When presenting the country tearsheet results to the user, identify the data sources mentioned or implied in the tool response content and add a "Data sources" section at the bottom listing them.
bigdata_etf_tearsheet
ChatGPTReturns a comprehensive ETF tearsheet in markdown covering fund facts, top holdings, price performance, historical returns, sector breakdown, and country allocation. Prerequisite: Call find_securities first to get rp_entity_id (the "id" field from the response). Workflow for ETF tearsheet: 1. Call find_securities → get "id" (use as rp_entity_id) and "type" (should be "ETF") 2. Call this tool with the rp_entity_id 3. Optionally call bigdata_search for supporting content When to Use: Use this tool when users ask about: - ETF overview, ETF snapshot, or ETF fund facts - ETF expense ratio, management fees, or total expense ratio (TER) - ETF assets under management (AUM) or fund size - ETF net asset value (NAV) - ETF holdings, top holdings, or portfolio composition - ETF sector breakdown, sector allocation, or sector exposure - ETF country allocation, geographic exposure, or country weighting - ETF price performance, returns, or historical performance - ETF risk metrics, volatility, max drawdown, or RSI - ETF premium or discount to NAV - ETF concentration, HHI, or top holdings weight - ETF provider, issuer, or fund company (e.g., SPDR, Vanguard, iShares) - ETF inception date, domicile, or ISIN - General information about a specific ETF (e.g., "tell me about SPY", "what is QQQ") Data Returned: The tearsheet is structured in seven sections, each as a markdown table: → Fund Overview (key fund facts from FMP): ISIN, Asset Class, Currency, Net Asset Value (NAV), Assets Under Management (AUM), Expense Ratio (TER), Holdings Count, Inception Date, Domicile, Provider, Average Volume, Listing Exchange → Top 10 Holdings (largest positions by weight from FMP): Rank, Asset Name, Shares, Market Value, Weight (%). Summary metrics: Top-10 Weight (%), HHI (Herfindahl-Hirschman Index for concentration) → Price Performance (real-time quote data from FMP): Currency, Last Price, Day Change (absolute + %), Market Cap, 52-Week High, 52-Week Low, 50-Day Moving Average, 200-Day Moving Average, Premium/Discount to NAV (%) → Returns Overview (computed from historical EOD prices from FMP): Period returns for 1D, 5D, 1M, 3M, 6M, YTD, 1Y, 3Y → Risk & Technical (risk metrics and technical indicators from FMP): Realized Volatility (20D), Realized Volatility (60D), Max Drawdown (1Y), RSI (14-period) for Current, 1D, 5D, 1M, 3M, 6M, 1Y → Sector Breakdown (sector weightings from FMP): Sector name and weight (%) for each sector the ETF holds → Country Allocation (country weightings from FMP): Country name and weight (%) for each country the ETF is exposed to Data sources (LLM instruction): When presenting ETF tearsheet results to the user, identify the data sources mentioned or implied in the tool response content and add a "Data sources" section at the bottom listing them.
bigdata_events_calendar
ChatGPTReturns a professionally formatted markdown calendar of corporate events including earnings announcements and conferences. Output Format: The tool returns a markdown document with the following structure: 1. Header Section: - Title: "# Events Calendar" - Metadata recap: timestamp, date range (From/To), applied filters (countries, exchanges) 2. Earnings Section (📊): Markdown table with columns: TICKER | COMPANY NAME | RELEASE DATE | EARNINGS CALL TIME | PERIOD - Shows company ticker symbols and full company names - Earnings call times displayed in UTC timezone - Fiscal period (Q1, Q2, Q3, Q4, H1, H2) - Events organized chronologically by date 3. Conferences Section (🎤): Markdown table with columns: TICKER | COMPANY NAME | DATE | TIME | NAME - Includes investor days, analyst meetings, conference presentations - Event names (e.g., "Investor Day 2026", "AGM 2026") - Times displayed in UTC timezone Default Behavior: - If NO parameters provided: Returns next 7 days of events from today - Automatic mode selection: • Calendar Mode (chronological): Used for discovery queries (multiple companies, country filters) • Company Mode (per-company grouping): Used for single company queries Workflow & Prerequisites for events calendar: For SPECIFIC companies: 1. FIRST: Call find_securities tool to get RavenPack Entity IDs 2. THEN: Pass entity IDs to rp_entity_ids parameter For MARKET-WIDE screening: - Omit rp_entity_ids and use filters (countries and exchanges) When to Use This Tool: - "What are Apple's upcoming earnings?" → Get entity ID first, then call with rp_entity_ids - "Show me US earnings this week" → Use countries: ["US"] - "NYSE earnings calendar for next 7 days" → Use exchanges: ["XNYS"] - "Japanese market events in January" → Use countries: ["JP"], start_date/end_date for January Key Features: - Company enrichment: Ticker symbols and full names automatically added from knowledge-graph - Timezone handling: All times displayed in UTC for consistency - Smart filtering: Combine multiple filters (countries, exchanges, date ranges) - Category filtering: Separate or combine earnings-call and conference-call events - Professional formatting: Tables, headers, and visual indicators (📊 🎤) Data sources (LLM instruction): When presenting the calendar results to the user, identify the data sources mentioned or implied in the tool response content and add a "Data sources" section at the bottom listing them.
bigdata_market_tearsheet
ChatGPTReturns a comprehensive market snapshot in markdown covering eight asset classes: global equity ETFs, equity sectors, major stock market indexes, commodities, fixed income bond ETFs, US Treasury yields, equity factors, and currencies (fiat + crypto). For each instrument the tearsheet shows current price and percentage changes over 1D, 5D, 1M, 3M, 6M, YTD, and 1Y. Note: equity data in Global Markets reflects ETF prices (e.g. SPY for US, EWG for Germany, EWJ for Japan), not the underlying index levels directly (S&P 500, DAX, Nikkei). ETF prices closely track their benchmark indexes and are a reliable proxy for country equity performance. Major Indexes shows the actual index levels. When to Use: Use this tool when users ask about: - "Market tearsheet", "market snapshot", "market screenshot", or "global markets" - How country or regional stock markets are performing (e.g. US market, European markets, Asian markets, Emerging Markets) - Global equity market performance, country stock market returns, or regional market comparisons - Which countries or regions are outperforming or underperforming (daily or over multiple periods) - A broad overview of worldwide market conditions - Major stock market index levels or performance — S&P 500, Dow Jones, NASDAQ, DAX, FTSE 100, Nikkei, etc. - Commodity prices or performance — oil, gas, gold, silver, copper, wheat, corn, coffee, etc. - Currency performance — major fiat pairs (EUR/USD, GBP/USD, USD/JPY, USD/CNY, etc.) or emerging market currencies - Crypto prices or performance — Bitcoin, Ethereum, Solana, XRP, BNB, etc. - Equity sector performance — Technology, Health Care, Financials, Energy, etc. - Fixed income / bond market performance — Treasury ETFs, corporate bonds, municipal bonds, TIPS, MBS, emerging market debt - US Treasury yield curve — current rates and changes for 1M, 3M, 6M, 1Y, 2Y, 5Y, 10Y, 20Y, 30Y maturities - Interest rate movements, yield curve shape, or rate changes over time - Equity factor performance — growth vs value, momentum, small-cap, quality, buybacks, etc. - Multi-period performance comparisons (5D, 1M, 3M, 6M, YTD, 1Y) across any of the above Data Returned: The tearsheet is structured in eight sections, each as a markdown table. Every row includes: name, ticker, current price, and percentage changes for 1D, 5D, 1M, 3M, 6M, YTD, and 1Y. → Global Markets (38 country/region equity ETFs, grouped by region): Americas: United States (SPY), Canada, Mexico, Brazil, Chile, Colombia, Argentina Europe: United Kingdom, Germany, France, Italy, Spain, Netherlands, Switzerland, Sweden, Poland Asia-Pacific: Japan, China, Hong Kong, South Korea, Taiwan, Australia, India, Singapore, Malaysia, Thailand, Indonesia, Philippines, New Zealand Mideast-Africa: South Africa, Israel, Turkey, Saudi Arabia, Qatar Other: Emerging Markets (EEM), Emerging Markets Vanguard (VWO), EAFE Developed ex-US (EFA), All World ex-US (VEU) → Major Indexes (38 stock market indexes, grouped by region): North America (11): S&P 500, Dow Jones Industrial Avg, NASDAQ Composite, NASDAQ 100, Russell 2000, Russell 1000, Wilshire 5000, NYSE Composite, CBOE Volatility Index (VIX), S&P/TSX Composite, S&P BMV IPC Europe (13): FTSE 100, DAX 40, CAC 40, Euro Stoxx 50, STOXX Europe 600, IBEX 35, FTSE MIB, AEX, SMI, OMX Stockholm 30, BEL 20, ATX, MOEX Russia Asia-Pacific (12): Nikkei 225, Hang Seng, S&P/ASX 200, KOSPI, TWSE (TAIEX), NIFTY 50, BSE SENSEX, STI Index, NZX 50, SET Index, Jakarta Composite, KLCI Other (5): Bovespa, Merval, JSE Top 40, EGX 30, Tadawul All Share → Commodities (30 instruments, grouped by sector): Energy (5): Crude Oil WTI, Brent Crude, Natural Gas, Gasoline RBOB, Heating Oil Metals (8): Gold, Silver, Platinum, Palladium, Micro Gold, Micro Silver, Copper, Aluminum Agricultural (17): Corn, Wheat, Soybeans, Soybean Oil, Soybean Meal, Oats, Rough Rice, Sugar, Coffee, Cocoa, Cotton, Orange Juice, Live Cattle, Feeder Cattle, Lean Hogs, Lumber, Class III Milk → Currencies (49 pairs, grouped by type): Fiat Currencies (34 pairs): Major pairs (EUR/USD, GBP/USD, USD/JPY, USD/CHF, AUD/USD, USD/CAD, NZD/USD), cross pairs (EUR/GBP, EUR/JPY, GBP/JPY, EUR/CHF, AUD/JPY, EUR/AUD, GBP/CHF, AUD/NZD, EUR/CAD, GBP/AUD, CAD/JPY), and emerging market pairs (USD/CNY, USD/CNH, USD/INR, USD/MXN, USD/BRL, USD/ZAR, USD/TRY, USD/KRW, USD/TWD, USD/SGD, USD/THB, USD/RUB, USD/PLN, USD/CLP, USD/IDR, USD/PHP) Cryptocurrencies (15): BTC/USD, ETH/USD, USDT, BNB/USD, SOL/USD, USDC, XRP/USD, ADA/USD, DOGE/USD, AVAX/USD, DOT/USD, LINK/USD, TRX/USD, MATIC/USD, LTC/USD → Equity Sectors (11 US sector ETFs): United States: Technology (XLK), Health Care (XLV), Financials (XLF), Consumer Discretionary (XLY), Communication Services (XLC), Industrials (XLI), Consumer Staples (XLP), Energy (XLE), Utilities (XLU), Real Estate (XLRE), Materials (XLB) → Fixed Income (26 bond ETFs, grouped by category): US Treasury Duration (7): 0-1Y Tsy (SHV), 1-3Y Tsy (SHY), 3-7Y Tsy (IEI), 7-10Y Tsy (IEF), 10-20Y Tsy (TLH), 20Y+ Tsy (TLT), Laddered Tsy (GOVI) US Government Agencies (7): US Tsy (GOVT), US TIPS (TIP), US Agencies (AGZ), Mortgage-Backed MBS (MBB), Ginnie Mae GNMA (GNMA), Municipals (MUB), Municipal Short-Term (SUB) US Corporate Credit (8): USD Aggregate Bond (AGG), Senior Loans (BKLN), High Grade Corp (LQD), High Yield Corp (HYG), Convertibles (CWB), Preferred Stock (PFF), HG Floating Corp (FLOT), HG Short-Term Corp (MINT) International Credit (4): Intl Treasuries (IGOV), Intl Aggregate Bond (BNDX), USD Emerging Markets (EMB), Local Emerging Markets (EMLC) → Yields (12 US Treasury maturities): Shows current yield and percentage-point changes for 1D, 5D, 1M, 3M, 6M, YTD, 1Y. Maturities: 1 Month, 2 Month, 3 Month, 6 Month, 1 Year, 2 Year, 3 Year, 5 Year, 7 Year, 10 Year, 20 Year, 30 Year → Equity Factors (20 factor ETFs, grouped by style, relative to S&P 500): Style (6): Growth (IWF), Value (IWD), Momentum (MTUM), Small-Cap (IJR), Low Volatility (USMV), High Dividend Yield (VYM) Qualitative (6): Buybacks (PKW), Spin-offs (CSD), Hedge Funds (GURU), IPOs (IPO), Quality (QUAL), Private Equity (PSP) Size & Style (8): Large-Cap Value (IVE), Large-Cap Growth (IVW), Mid-Cap Value (IJJ), Mid-Cap Core (IJH), Mid-Cap Growth (IJK), Small-Cap Value (IWN), Small-Cap Core (IWM), Small-Cap Growth (IWO) Complement to bigdata_market_tearsheet: For macro/economic context (GDP, CPI, interest rates, economic calendar) use bigdata_country_tearsheet instead. Data sources (LLM instruction): When presenting market tearsheet results to the user, identify the data sources mentioned or implied in the tool response content and add a "Data sources" section at the bottom listing them.
bigdata_sentiment_tearsheet
ChatGPTPREFERRED TOOL for any explicit sentiment request. Use this tool whenever the user asks for sentiment, news tone, media perception, or how the media views a company (e.g. "get the sentiment for Apple", "what is the sentiment on Tesla", "show Apple's media sentiment"). Returns a real-time media sentiment analysis for a company as a markdown report. More complete and detailed than the sentiment summary included in the company tearsheet. The report includes: - Sentiment score and direction (bullish / bearish / neutral) - Media attention level over recent periods - An AI-generated narrative summarising the main sentiment drivers - Cited source articles supporting the narrative You must call find_securities first to obtain the rp_entity_id before calling this tool. Works for both public and private companies that have recent media coverage. IMPORTANT: Present the tool output exactly as returned — do not reformat, summarise, or omit any part of the markdown report.
fetch
ChatGPTThis tool returns the document by its ID.
find_securities
ChatGPTRouting (hard rule): Use `find_securities` for ETFs, funds, securities-oriented asks (including "find/list/search securities", fund tickers, ETF themes), and also for any company or issuer lookup (name, ticker, or domain) regardless of whether securities language is used. Canonical example: User asks "Apple securities" — call `find_securities`. User asks "Apple" or "tell me about Apple" — also `find_securities`, putting a single focused token in query (e.g. "Apple" or "AAPL"; follow the one-token rule). Batch resolution: When the user provides multiple tickers or names to look up (e.g. "map AAPL, TSLA, NVDA to rp_ids" or "resolve these tickers: HYDR, PLUG, LIT"), fire all calls in parallel as a batch — one find_securities call per ticker, all at the same time, not sequentially. Do not combine multiple tickers in a single query. Bare ticker symbols (e.g. AAPL, SPY, HYDR) always go here, never to `get_securities`. Listed companies in results: Matches can include listed companies and ETFs. Returns REQUIRED data for downstream tools: - `id`: entity identifier for follow-on tools that expect an entity id (e.g. rp_entity_id parameters) - Rows include `security_type` COMPANY vs ETF (and listing metadata for company-like rows where applicable) DO NOT run this tool again if: - The entity you need is already confirmed in this conversation thread - You already have the confirmed entity ID from a previous tool call Use this tool to: - Handle "Apple securities" and similar securities-explicit asks - Satisfy "find securities", "search for securities", "list securities", "which securities …" (put the user's intent in query) - Resolve ETF or fund names and tickers (e.g. SPY, QQQ) to entity IDs - Resolve any company name, ticker, or domain to a RavenPack entity ID Use `get_securities` instead when the user provides exact security identifiers: ISIN, CUSIP, SEDOL, or LISTING (e.g. XNAS:AAPL). Search scope: By default, results can include listed companies and ETFs. Optional `listing_type`, `countries`, and `sectors` narrow the universe when the user specifies listing status, geography, or broad sector—use those structured fields rather than stuffing everything into `query` alone. For `countries`, pass ISO 3166-1 alpha-2 codes (e.g. US, ES, CA, GB). Unknown or malformed entries are ignored (they do not apply a country filter). Optional filters (use when the user narrows scope—do not bury everything in `query` only): - `countries`: If they say US, America, in Spain, UK-listed, etc., set `countries` to the matching alpha-2 list (e.g. ["US"], ["ES"]; United Kingdom → `GB`, not UK). Never pass full country names in this argument. - `sectors`: If they mention industry or sector (e.g. finance, banks, tech, healthcare), map to one of these broad sector labels: Basic Materials, Consumer Goods, Consumer Services, Energy, Financials, Health Care, Industrials, Real Estate, Technology, Telecommunications, Utilities. Examples: "financial industry" / "banks" → `["Financials"]`; "tech" / "software" → `["Technology"]`. If you are not confident the label fits the user's intent, omit `sectors` and keep the industry wording in `query` only. - `listing_type`: When the user specifies public vs private listing, set `PUBLIC` or `PRIVATE` (not free text in `query`). - `query` vs filters: Put one issuer or securities-focused token in `query` (e.g. Apple, Apple securities, AAPL per rules). When `listing_type`, `countries`, and/or `sectors` are set, avoid repeating long listing, geography, or sector phrases inside `query`—let the filters carry that signal. Composite example: "Apple in the US in the financial sector" → `find_securities` with `query`: "Apple", `countries`: ["US"], `sectors`: ["Financials"]. Returns: A JSON array with a single element: an object with `results` (up to 5 matches) and `metadata`: request_id (UUID for correlating this tool call; not an end-user id) and timestamp (UTC ISO-8601). Each match in `results` includes security_type, id, identifiers, etc. If the top hit is wrong, inspect the remaining `results` before re-querying.
get_securities
ChatGPTResolves one or more security identifiers to issuer company records from the knowledge graph (not for fuzzy name search — use `find_securities` for company or ETF names, tickers, and themes). When to use - User provides exact structured identifiers: CUSIP 037833100, ISIN US0378331005, SEDOL 2046251, LISTING XNAS:AAPL (exchange MIC + ":" + symbol). - You already have exact identifiers and need rp_company_id, company profile, and cross-refs. Do NOT use for bare ticker symbols (e.g. AAPL, SPY, QQQ, HYDR, PLUG). A LISTING identifier requires the full EXCHANGE:TICKER format with a colon (e.g. XNAS:AAPL). Plain tickers, company names, or ETF symbols → use `find_securities` instead. The API infers the type per id: length 12 → ISIN, 9 → CUSIP, 7 → SEDOL, or ":" → LISTING. Mixed shapes in one request are supported (batched by type). Returns `results` (map of each requested identifier → a typed object or `null if unknown or if the id failed shape validation), plus **metadata**: request_id is the unique id for this MCP HTTP request (UUID), not the end-user id; plus timestamp (UTC ISO-8601). If some ids are malformed, **message** explains which were skipped; well-formed ids are still resolved. If **all** ids are malformed, **results** is empty and data-tools is not called. Each resolved entry includes **security_type** COMPANY, ETF, or BOND. ETF vs COMPANY follows knowledge-graph company_etf_type (ETF or BUSINESS). BOND applies when the row has security_class FIXED_INCOME or the inferred id type is BISIN / BCUSIP; bonds nest the issuer under details.company. Identifier cross-reference lists are on flat COMPANY / ETF` rows only. Data sources (LLM instruction): When presenting results, identify data sources implied in the payload and add a "Data sources" section.
search
ChatGPTPrimary search tool for retrieving financial and business content from Bigdata's comprehensive database. CONTENT COVERAGE: - SEC Filings: 10-K, 10-Q, 8-K, 20-F and other regulatory documents - Earnings Transcripts: earnings calls, investor meetings, conference presentations - News: thousands of sources covering markets, companies, and industries - Research Reports: broker reports, analyst notes, equity research - Proprietary Files: user-uploaded documents/emails and proprietary broker reports. - Podcasts and expert interviews SEARCH RULES (must follow before every call): 1. ONE FOCUS PER QUERY. Mixed topics → split into separate calls. ❌ "Nvidia partnership news and earnings results in 2025" ✅ "News about Nvidia partnerships in 2025" + "Nvidia earnings results in 2025" 2. NATURAL LANGUAGE. Full sentences, not keyword lists. ❌ "Epic Apple App Store lawsuit" ✅ "Epic Games' antitrust lawsuit against Apple over App Store commissions" 3. ONE TIME PERIOD PER CALL. Split multi-period asks into separate calls. ❌ "Spotify broker reports from 2024 and 2025" ✅ "Spotify broker reports from 2024" + "Spotify broker reports from 2025" 4. ONE ENTITY GROUP PER CALL. Split unrelated entities into separate calls. ❌ "Tesla and Spotify 10-Ks in 2025" ✅ "Tesla 10-Ks in 2025" + "Spotify 10-Ks in 2025" 5. DO NOT INVENT DATES. If the user did not name a period, do not add one to the query. Pass temporal wording verbatim ("yesterday", "last quarter", "FY2024"). 6. ITERATE. Broader queries first, then refine with follow-up calls based on gaps. Only raise max_chunks if the SAME query genuinely needs more content; otherwise issue a related but different query.
bigdata_company_tearsheet
Claudebigdata_country_tearsheet
Claudebigdata_events_calendar
Claudebigdata_market_tearsheet
Claudebigdata_search
Claudefind_companies
Claude
