--- canonical_url: "https://docs.composio.dev/toolkits/bigdata_com_mcp" markdown_url: "https://docs.composio.dev/toolkits/bigdata_com_mcp.md" product: "platform" version: null last_updated: null --- # Bigdata.com MCP Bigdata.com MCP gives agents grounded access to financial news, transcripts, filings, entity intelligence, and research workflows. - **Category:** model context protocol - **Auth:** API_KEY - **Composio-managed OAuth available?** N/A - **Tools:** 17 - **Triggers:** 0 - **Slug:** `BIGDATA_COM_MCP` - **Version:** 20260910_00 ## Tools ### Bigdata company tearsheet **Slug:** `BIGDATA_COM_MCP_BIGDATA_COMPANY_TEARSHEET` Returns a comprehensive company tearsheet with financial data, market intelligence, and analyst coverage. **PREREQUISITE: Call find_securities first to get rp_entity_id and listing_type.** **Tearsheet routing by security_type (from find_securities):** - security_type "COMPANY" → call this tool (bigdata_company_tearsheet) - security_type "ETF" → call bigdata_etf_tearsheet instead - security_type "BOND" → no tearsheet available **Workflow for company tearsheet:** 1. Call find_securities → check security_type. Only proceed here if security_type is "COMPANY". 2. Read "listing_type" from the result ("PUBLIC" → "Public", "PRIVATE" → "Private") — this is company_type. 3. Call this tool with rp_entity_id and company_type. 4. Optionally call bigdata_search for supporting content. **CRITICAL — Never infer company_type from training knowledge:** The listing classification comes from RavenPack's Point-in-Time (PiT) entity database — it is the authoritative source and will always be present for company entities. It may differ from your training knowledge (e.g. a company you believe is private may be listed, or may have financial data from a previous listing period). Always use the value from find_securities. Never guess. **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) • C-level leadership: the people leading the company, including the executive directory, board composition, and management stats • 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. #### Input Parameters | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | `interval` | string ("quarter" | "annual") | No | REQUIRED for public companies. Choose the time period for financial data: - "quarter" (default): Quarterly financial statements (Q1, Q2, Q3, Q4) - "annual": Annual/yearly financial statements (FY) IMPORTANT: This parameter only applies to public companies and is ignored for private companies. DEFAULT BEHAVIOR: Always use "quarter" unless the user explicitly requests annual data. When users ask for "latest" or "recent" or "current" financial information, use "quarter". | | `sections` | string | No | OPTIONAL. Filters the tearsheet to the listed sections. DEFAULT BEHAVIOR — DO NOT PASS THIS PARAMETER unless the user EXPLICITLY references one or more specific sections by name or concept (e.g. "financials", "analyst ratings", "ESG", "dividends"). If the user asks for a general tearsheet, company overview, or any broad financial analysis → omit this parameter entirely so all sections are returned. HARD RULE: Use when user says: "show me Apple's financials", "what are the analyst ratings?", "give me the ESG data", "who leads Apple?", "who are Tesla's executives?", "who's on the board?" Do NOT use for: "get me Apple's tearsheet", "tell me about Tesla", "run a full analysis on Microsoft" Only applies to public companies. Ignored for private companies. Valid values: - "company_overview" - Company profile, price performance, and description - "c_level_leadership" - The people leading the company: executives, board members, management stats (requires FactSet access) - "financial_ratios" - Gross margin, P/E, P/B, debt ratios (TTM) - "key_metrics" - EV, EV/EBITDA, ROE, ROA, free cash flow yield (TTM) - "dividends" - Dividend history, yields, and frequency - "analyst_estimates" - Forward revenue and EPS projections - "latest_earnings" - Most recent earnings actual vs estimated - "earnings_calendar" - Upcoming earnings dates - "financial_statements" - Income statement, balance sheet, cash flow - "revenue_segmentation" - Revenue breakdown by product and geography - "analyst_ratings" - Analyst recommendations and price targets - "fund_trends_history" - Institutional holdings and options activity - "hiring_trends" - Employee count trends and workforce signals - "esg_performance" - Environmental, Social, Governance scores - "mergers_acquisitions" - M&A activity (requires FactSet access) Example: ["financial_statements", "analyst_ratings"] | | `company_type` | string ("Public" | "Private") | Yes | REQUIRED. Must be "Public" or "Private". HARD RULE: Use ONLY the value returned by find_securities — NEVER derive this from your own training knowledge. From find_securities: read the "listing_type" field ("PUBLIC" → "Public", "PRIVATE" → "Private"). This value comes from RavenPack's Point-in-Time (PiT) entity database. It is always present for company entities and is the authoritative source — it may differ from what you believe about the company (e.g. a company commonly known as private may be classified as Public due to a past or current listing). Passing the wrong type returns incorrect data. | | `rp_entity_id` | string | Yes | REQUIRED. The 6-character RavenPack entity ID from find_securities response ("id" field). Example: "4A6F00" (Alphabet), "DHLJNI" (RavenPack). | | `include_source_attribution` | boolean | No | When true, metric values in the Financial Statements, Financial Ratios, and Key Metrics tables are rendered as clickable [value](url) markdown links pointing directly to the FactSet audit card for that metric and period, allowing every figure to be traced back to its original source. SET TO TRUE automatically when the user asks for any of the following (or similar phrasing): "grounded data", "source of truth", "verified data", "fact-check", "sourced figures", "with sources", "with links", "auditable", "traceable", "cite the source", or any request that implies they want to verify or validate the numbers against an authoritative source. OUTPUT RULE: When include_source_attribution is true, you MUST preserve every [value](url) hyperlink exactly as returned in the tool response when presenting financial figures to the user. Do NOT strip, replace, or summarise these links — each number must remain a clickable link so the user can audit the source. Only applies when the user has access to the FactSet data provider and the company has FactSet fundamentals data. Has no effect when company_type is "Private" — private company tearsheets do not include FactSet fundamentals data. Also has no effect if the sections parameter is used and excludes all of "financial_statements", "financial_ratios", and "key_metrics". Defaults to false. | ### Bigdata country tearsheet **Slug:** `BIGDATA_COM_MCP_BIGDATA_COUNTRY_TEARSHEET` Returns 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. #### Input Parameters | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | `country` | string ("AE" | "AR" | "AT" | "AU" | "BE" | "BR" | "CA" | "CH" | "CL" | "CN" | "CO" | "CZ" | "DE" | "DK" | "EG" | "EMU" | "ES" | "FI" | "FR" | "GR" | "HK" | "HU" | "ID" | "IE" | "IL" | "IN" | "IS" | "IT" | "JP" | "KR" | "KW" | "MX" | "NL" | "NO" | "NZ" | "PL" | "PT" | "QA" | "RO" | "RU" | "SA" | "SE" | "SG" | "SK" | "TH" | "TR" | "UK" | "US" | "ZA") | No | REQUIRED. A single country code to filter the tearsheet by. Must use an exact 2-letter code from the complete list below. **ALWAYS PROVIDE THIS PARAMETER:** If the user does not provide a country, use "US" as the default. **IMPORTANT: This tool accepts only ONE country. If multiple countries are provided, only the first will be used.** **INSTRUCTIONS:** - If user says "US economic data" → use "US" - If user says "Germany economic indicators" → use "DE" - If user says "UK economic calendar" → use "UK" - If user mentions country names, convert to codes: "Germany" → "DE", "France" → "FR", "United States" → "US" - If user mentions multiple countries, use only the first/primary country mentioned **Complete List of Valid Country Codes (42 countries):** AR - Argentina AU - Australia AT - Austria BE - Belgium BR - Brazil CA - Canada CL - Chile CN - China CO - Colombia CZ - Czech Republic DK - Denmark EMU - European Monetary Union (Eurozone) FI - Finland FR - France DE - Germany (Deutschland) GR - Greece HK - Hong Kong HU - Hungary IS - Iceland IN - India ID - Indonesia IE - Ireland IT - Italy JP - Japan MX - Mexico NL - Netherlands NZ - New Zealand NO - Norway PL - Poland PT - Portugal RO - Romania RU - Russia SG - Singapore SK - Slovakia ZA - South Africa ES - Spain SE - Sweden CH - Switzerland TR - Turkey UK - United Kingdom US - United States | ### Bigdata etf tearsheet **Slug:** `BIGDATA_COM_MCP_BIGDATA_ETF_TEARSHEET` Returns a comprehensive ETF tearsheet in markdown covering fund facts, top holdings, price performance, historical returns, dividend history, 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 - ETF dividends, dividend yield, dividend history, or distribution schedule - General information about a specific ETF (e.g., "tell me about SPY", "what is QQQ") **Data Returned:** The tearsheet is structured in eight 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 → **Dividends** (recent dividend payments from FMP): Date, Amount, Adjusted Amount, Yield, Declaration Date, Record Date, Payment Date, Frequency, for the most recent payments → **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. #### Input Parameters | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | `rp_entity_id` | string | Yes | RavenPack entity ID for the ETF (6-character identifier). Use find_securities first to resolve an ETF name, fund name, or ticker to its entity ID. | ### Bigdata events calendar **Slug:** `BIGDATA_COM_MCP_BIGDATA_EVENTS_CALENDAR` Returns a professionally formatted markdown calendar of corporate events including earnings announcements, conferences, IPO listings and company delistings. **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 4. **IPOs Section:** present when the results include IPO listings Markdown table with columns: TICKER | COMPANY NAME | LISTING DATE | NAME - Upcoming and recently priced US initial public offerings - NO time column: an IPO is dated to the day, so report the listing date only - The offering status is the title prefix ("Priced:", "Expected:", "Withdrawn:") - A company that has not started trading yet may have no entity record. Those offerings are grouped under the single rp_entity_id "NO_MAP", and their ticker and company name come from the offering itself. Treat them as valid results, not errors, and never pass "NO_MAP" back as an rp_entity_id — it is a bucket of several companies, not one entity. 5. **Delistings Section:** present when the results include delistings Markdown table with columns: TICKER | COMPANY NAME | DELISTING DATE | NAME - Companies removed from an exchange, covering 2025-01-01 onwards - NO time column: a delisting is dated to the day, so report the delisting date only - Every delisting is in the past, so a forward-looking window has none of them - A delisted company may never have been mapped to an entity record. Those rows are grouped under the single rp_entity_id "NO_MAP", exactly as IPO listings are, and the same rule applies: treat them as valid results and never pass "NO_MAP" back as an rp_entity_id. **Unknown values in any table:** a TICKER or COMPANY NAME cell reading "–" (en dash) means no ticker is available for that company, or the company is not covered. The row itself is a real event: present it with whatever identifier it does carry, rather than reporting an error. **Default Behavior:** - If NO parameters provided: Returns next 7 days of events from today - An unfiltered calendar asks for every category. Delistings are all in the past, so they appear only when the date range reaches back before 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 - "What IPOs are coming up this month?" → Use categories: ["ipos-calendar"] with start_date/end_date **Key Features:** - Company enrichment: Ticker symbols and full names added automatically where available - 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, conference-call, ipos-calendar and delisted-company events - Consistent formatting: Tables and section headers **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. #### Input Parameters | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | `limit` | string | No | OPTIONAL. Maximum number of events to return per page for pagination control. **Valid Range:** 1 to 500 **Default:** 100 events per page (if not specified) **When to Use:** - Controlling response size for large queries - Implementing pagination for UI displays - Managing API response times **Examples:** - Small batches: limit=50 - Default behavior: omit parameter (returns 100) - Maximum results: limit=500 **Note:** Use with cursor parameter to paginate through large result sets. Most queries complete without needing pagination. | | `cursor` | string | No | OPTIONAL. Pagination cursor to retrieve the next page of results. **Format:** String value returned by the API (e.g., "549698") **How to Use:** 1. Make initial request without cursor parameter 2. If response includes a "cursor" field in the result, more pages exist 3. Copy that cursor value EXACTLY and pass it in the next request 4. Repeat until no cursor is returned (indicating last page) **IMPORTANT:** Always pass the cursor value as-is without modification. The cursor is an opaque string that should be treated as a reference token. **Example Flow:** Request 1: {"limit": 5} (no cursor) Response 1: {..., "cursor": "549698"} Request 2: {"limit": 5, "cursor": "549698"} Response 2: {..., "cursor": "549703"} Request 3: {"limit": 5, "cursor": "549703"} Response 3: {...} (no cursor = last page) **Note:** Only applicable when result sets are large. Most queries return all results in the first response without needing pagination. | | `sectors` | string | No | OPTIONAL. List of industry sectors to filter events by company sector classification. **Valid Sector Values (case-sensitive, use exact strings):** - "Basic Materials" - Mining, chemicals, construction materials, metals & mining - "Consumer Goods" - Food & beverage, household products, personal products, tobacco - "Consumer Services" - Retail, media, travel & leisure, consumer durables - "Energy" - Oil & gas, coal, alternative energy - "Financials" - Banks, insurance, investment services, real estate finance - "Health Care" - Pharmaceuticals, biotechnology, medical equipment, healthcare providers - "Industrials" - Aerospace, defense, construction, machinery, transportation - "Real Estate" - REITs, real estate management & development - "Technology" - Software, hardware, semiconductors, IT services - "Telecommunications" - Telecom services, wireless, broadband - "Utilities" - Electric, gas, water utilities, renewable utilities **Behavior:** - If NOT provided: Returns events for companies from ALL sectors - If provided: Returns events ONLY for companies in the specified sectors - Can be combined with other filters (countries, exchanges, categories, date ranges) **Examples:** - Technology only: ["Technology"] - Tech and Healthcare: ["Technology", "Health Care"] - Financial services: ["Financials", "Real Estate"] - Energy sector: ["Energy", "Utilities"] **Common Use Cases:** - "Show me tech earnings this quarter" → Use sectors: ["Technology"], categories: ["earnings-call"] - "Healthcare conferences in Q1" → Use sectors: ["Health Care"], categories: ["conference-call"] - "Financial sector events for US companies" → Use sectors: ["Financials"], countries: ["US"] - "Energy and utilities earnings" → Use sectors: ["Energy", "Utilities"], categories: ["earnings-call"] **Note:** Sector classifications follow Bigdata.com's own company classification, so a company may sit in a different sector than another provider would assign it. | | `end_date` | string | No | OPTIONAL. End date for the events calendar date range. **Format:** ISO 8601 date string - "YYYY-MM-DD" **Default Behavior:** If omitted, defaults to 7 days after start_date. **Examples:** - "2026-02-03" - February 3, 2026 - "2026-02-28" - February 28, 2026 - "2026-12-31" - December 31, 2026 **Relationship with start_date:** - end_date must be >= start_date - Recommended: Always provide BOTH start_date and end_date together - If only start_date provided: end_date defaults to start_date + 7 days - If only end_date provided: start_date defaults to today **Common Date Ranges:** - Next week: start_date=today, end_date=today+7days - This month: start_date="2026-02-01", end_date="2026-02-28" - This quarter: start_date="2026-01-01", end_date="2026-03-31" - Next 30 days: start_date=today, end_date=today+30days - Specific week: start_date="2026-02-03", end_date="2026-02-09" **Date Range Behavior:** - Used for BOTH company-specific (with rp_entity_ids) and discovery queries - Date is inclusive - end_date="2026-02-03" includes all events on February 3 - Larger date ranges may return more results (use limit/cursor for pagination) **Note:** For best results, always specify both start_date and end_date explicitly rather than relying on defaults. | | `countries` | string | No | OPTIONAL. List of ISO 3166-1 alpha-2 country codes to filter events by company headquarters location. **Format:** Two-letter country codes (uppercase). Standard ISO codes. **Common Country Codes:** - "US" - United States - "CA" - Canada - "GB" - United Kingdom - "JP" - Japan - "DE" - Germany - "FR" - France - "IT" - Italy - "ES" - Spain - "CH" - Switzerland - "NL" - Netherlands - "SE" - Sweden - "NO" - Norway - "DK" - Denmark - "FI" - Finland - "AU" - Australia - "NZ" - New Zealand - "CN" - China - "HK" - Hong Kong - "SG" - Singapore - "KR" - South Korea - "IN" - India - "BR" - Brazil - "MX" - Mexico - "AR" - Argentina **Behavior:** Filters events to show ONLY companies headquartered in the specified countries. Can be combined with other filters (exchanges, date ranges). **Examples:** - US companies only: ["US"] - North American markets: ["US", "CA", "MX"] - European markets: ["GB", "DE", "FR", "IT", "ES"] - Asian markets: ["JP", "CN", "HK", "SG", "KR"] **Note:** Use official ISO 3166-1 alpha-2 codes. All codes should be uppercase. | | `exchanges` | string | No | OPTIONAL. List of stock exchange codes to filter events by listing venue. **Format:** Exchange identifier MIC codes (case-sensitive). Use exact exchange codes as they appear in market data. **Major US Exchanges:** - "XNYS" - New York Stock Exchange - "XNGS" - Nasdaq Global Select Market - "XNMS" - Nasdaq Global Market - "XNAS" - Nasdaq - "ARCX" - NYSE Arca - "XASE" - NYSE American (formerly AMEX) - "XCBO" - Chicago Board Options Exchange - "OTCM" - OTC Markets (Grey Market) **International Exchanges:** - "XBUE" - Buenos Aires Stock Exchange (Argentina) - "XASX" - Australian Securities Exchange (Australia) - "XWBO" - Wiener Börse (Austria) - "XDHA" - Dhaka Stock Exchange (Bangladesh) - "XBRU" - Euronext Brussels (Belgium) - "BVMF" - B3 / São Paulo Stock Exchange (Brazil) - "XBUL" - Bulgaria Stock Exchange - "XTSE" - Toronto Stock Exchange (Canada) - "XTSX" - TSX Venture Exchange (Canada) - "XSGO" - Santiago Stock Exchange (Chile) - "XSHG" - Shanghai Stock Exchange (China) - "XSHE" - Shenzhen Stock Exchange (China) - "XPRA" - Prague Stock Exchange (Czech Republic) - "XCSE" - Nasdaq Copenhagen (Denmark) - "XTAL" - Nasdaq Tallinn (Estonia) - "XHEL" - Nasdaq Helsinki (Finland) - "XPAR" - Euronext Paris (France) - "XFRA" - Frankfurt Stock Exchange (Germany) - "ASEX" - Athens Stock Exchange (Greece) - "XHKG" - Hong Kong Stock Exchange - "XBUD" - Budapest Stock Exchange (Hungary) - "XICE" - Nasdaq Iceland - "XBOM" - Bombay Stock Exchange (India) - "XNSE" - National Stock Exchange of India - "XIDX" - Indonesia Stock Exchange - "XDUB" - Irish Stock Exchange - "XTAE" - Tel Aviv Stock Exchange (Israel) - "XMIL" - Borsa Italiana (Italy) - "XJPX" - Tokyo Stock Exchange (Japan) - "XNGO" - Nagoya Stock Exchange (Japan) - "XFKA" - Fukuoka Stock Exchange (Japan) - "XSAP" - Sapporo Stock Exchange (Japan) - "XRIS" - Nasdaq Riga (Latvia) - "XKLS" - Bursa Malaysia - "XMEX" - Mexican Stock Exchange - "XCAS" - Casablanca Stock Exchange (Morocco) - "XAMS" - Euronext Amsterdam (Netherlands) - "XNZE" - New Zealand Stock Exchange - "XOSL" - Oslo Børs (Norway) - "NOTC" - Norway OTC Market - "XLIM" - Lima Stock Exchange (Peru) - "XPHS" - Philippine Stock Exchange - "XWAR" - Warsaw Stock Exchange (Poland) - "XLIS" - Euronext Lisbon (Portugal) - "XBSE" - Bucharest Stock Exchange (Romania) - "XSES" - Singapore Exchange - "XLJU" - Ljubljana Stock Exchange (Slovenia) - "XJSE" - Johannesburg Stock Exchange (South Africa) - "XKRX" - Korea Exchange - "XKOS" - KOSDAQ (South Korea) - "XMAD" - Madrid Stock Exchange (Spain) - "XSTO" - Nasdaq Stockholm (Sweden) - "XSWX" - SIX Swiss Exchange - "XTAI" - Taiwan Stock Exchange - "ROCO" - Taipei Exchange (Taiwan) - "XBKK" - Stock Exchange of Thailand - "XIST" - Borsa Istanbul - "XETR" - XETRA (Germany) - "XMUN" - Börse München (Germany) - "XSTU" - Börse Stuttgart (Germany) - "XDUS" - Düsseldorf Stock Exchange (Germany) - "XHAM" - Hamburg Stock Exchange (Germany) - "XHAN" - Hannover Stock Exchange (Germany) - "XSAU" - Saudi Stock Exchange (Tadawul) - "XLIT" - Nasdaq Vilnius (Lithuania) - "XNSX" - National Stock Exchange of Australia - "XADS" - Abu Dhabi Securities Exchange - "XDFM" - Dubai Financial Market - "DIFX" - NASDAQ Dubai - "XNGM" - Nordic Growth Market - "XSTC" - Ho Chi Minh Stock Exchange (Vietnam) - "HSTC" - Hanoi Stock Exchange (Vietnam) - "BMEX" - Bolsas y Mercados Españoles - "XCNQ" - Canadian Securities Exchange - "XSAT" - Spotlight Stock Market (Sweden) **Behavior:** Filters events to show ONLY companies listed on the specified exchanges. A company may be listed on multiple exchanges. **Examples:** - NYSE listings only: ["XNYS"] - All Nasdaq: ["XNGS", "XNGM", "XNAS"] - Major US markets: ["XNYS", "XNGS"] - Canadian markets: ["XTSE", "XTSX"] **Note:** Exchange codes are case-sensitive and must match exactly. | | `categories` | string | No | OPTIONAL. List of event categories to filter by. Use to focus on specific event types. **Valid Category Values (case-sensitive, use exact strings):** - "earnings-call" - Quarterly/annual earnings announcements with earnings call schedules - "conference-call" - Investor conferences, analyst meetings, investor days, AGMs - "ipos-calendar" - US initial public offerings, upcoming and recently priced - "delisted-company" - companies removed from an exchange, from 2025-01-01 onwards **Behavior:** - If NOT provided: Returns every category - If provided: Returns ONLY the specified categories - "delisted-company" covers 2025-01-01 onwards, and every delisting is in the past: a forward-looking date range returns none of them, and an end_date before 2025-01-01 is rejected rather than answered empty. **Examples:** - Earnings only: ["earnings-call"] - Conferences only: ["conference-call"] - IPOs only: ["ipos-calendar"] - Delistings only: ["delisted-company"] - Every category (default): omit this parameter entirely **Common Use Cases:** - Earnings calendar only: Use ["earnings-call"] to exclude conferences and IPOs - Conference schedule only: Use ["conference-call"] - IPO calendar only: Use ["ipos-calendar"] — e.g. "what IPOs are coming up?" - Delistings only: Use ["delisted-company"] — e.g. "which companies were delisted last month?" - Default event calendar: Omit the parameter **Note on IPOs:** the IPO calendar covers US listings only, and it is excluded when `exchanges` or `cursor` is set, or when `countries` is set without "US" — those filters cannot be applied to it, so the request is served from the transcript-event providers alone. Pass ["ipos-calendar"] alone to ask for the IPO calendar on its own. | | `start_date` | string | No | OPTIONAL. Start date for the events calendar date range. **Format:** ISO 8601 date string - "YYYY-MM-DD" **Default Behavior:** If omitted, defaults to today's date. **Examples:** - "2026-01-27" - January 27, 2026 - "2026-02-01" - February 1, 2026 - "2026-12-31" - December 31, 2026 **Use Cases:** - Future events: start_date="2026-02-01", end_date="2026-02-28" (February events) - Past events: start_date="2026-01-01", end_date="2026-01-15" (historical) - This week: start_date=today, end_date=today+7days - This month: start_date="2026-02-01", end_date="2026-02-28" - This quarter: start_date="2026-01-01", end_date="2026-03-31" **Date Range Behavior:** - Used for BOTH company-specific (with rp_entity_ids) and discovery queries (without rp_entity_ids) - For discovery queries without dates: Defaults to next 7 days - For company queries without dates: Returns all upcoming events (no date limit) - ALWAYS use with end_date for complete date range specification **Note:** Date is inclusive. start_date="2026-01-27" includes all events on January 27. | | `rp_entity_ids` | string | No | OPTIONAL. List of RavenPack Entity ID strings for targeting specific companies. **Format:** 6-character alphanumeric codes. Example: "4A6F00" (Apple), "D8442A" (Tesla) **How to Get Entity IDs:** 1. FIRST call find_securities tool with company name/ticker 2. Extract "id" field from response 3. Pass that ID to this parameter **Behavior:** - When PROVIDED: Fetches events ONLY for specified companies - When OMITTED: Fetches events based on date range and/or filter criteria (countries, exchanges) **Supports Multiple Companies:** Can provide 1-100 entity IDs in a single request. **Examples:** - Single company: ["4A6F00"] (Apple) - Multiple companies: ["4A6F00", "D8442A", "0D5410"] (Apple, Tesla, Microsoft) - FAANG stocks: ["4A6F00", "549300", "D8442A", "D61BE0", "3BE4E0"] **Use Cases:** - "What's Apple's earnings schedule?" → ["4A6F00"] - "Show me earnings for FAANG companies" → Get IDs for Meta, Apple, Amazon, Netflix, Google - "Compare Tesla and Ford events" → Get IDs for both companies **Combining with Other Filters:** - Can combine with start_date/end_date to filter by time range - Can combine with categories to show only earnings or conferences - CANNOT combine with countries or exchanges (entity IDs already specify companies) **Note:** Always use find_securities tool first to get valid entity IDs. Do not guess or make up IDs. | #### Output | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | `events` | array | No | The corporate events that make up the calendar, ordered by date. | | `pagination` | object | No | | ### Bigdata fetch document **Slug:** `BIGDATA_COM_MCP_BIGDATA_FETCH_DOCUMENT` Returns the full text of a single document by its Bigdata.com document identifier, converted from annotated RPJSON to markdown. Use this after `bigdata_list_documents`, `bigdata_search`, or `bigdata_upload_document` when you need the complete document rather than ranked snippets. **When to use** - Read one known document end-to-end (private or public corpus). - Follow up on a document id discovered via list/search/upload. **When NOT to use** - Broad content discovery — use `bigdata_search`. - Browsing your documents metadata — use `bigdata_list_documents`. - Opening a document in the in-host viewer — use `bigdata_viewer`. **Hard rule — where `id` comes from** Pass only an identifier previously returned by Bigdata.com tools: - `bigdata_search` result `id` (or `open_in_claude[].document_id`) - `bigdata_list_documents` result `id` - `bigdata_upload_document` result `document_id` Never invent an id, and never pass a headline, URL, file name, ticker or similar. If you do not have an id yet, discover it first with list/search. **Returns** - `id`, `title`, `text` (markdown), and citation `url`. - Not-found / still-processing cases return an empty id with a clear message in `text`. - A malformed / invalid id is rejected — rediscover via list/search. #### Input Parameters | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | `id` | string | Yes | Exact Bigdata.com document identifier. Copy it verbatim from a prior tool result: search/list field `id`, upload field `document_id`, or `open_in_claude[].document_id`. Do not pass a headline, URL, file name, ticker, or entity id. | #### Output | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | `id` | string | Yes | Unique identifier for the document. | | `url` | string | Yes | Canonical URL for citation. | | `text` | string | Yes | The full text content of the document. | | `title` | string | Yes | Human-readable title of the document. | ### Bigdata help **Slug:** `BIGDATA_COM_MCP_BIGDATA_HELP` Returns an interactive help interface that shows: - Information about user active subscription status - Available data packages and their content - Examples of user prompts **When to Use:** Use this tool when users ask about: - "Help" / "documentation" / "prompts examples" - "Subscription status" - "Available data packages" / "What data do I have access to?" / "What data can I use?" ### Bigdata list connectors **Slug:** `BIGDATA_COM_MCP_BIGDATA_LIST_CONNECTORS` Returns all private content connectors (Email, Investment Research) that the user has access to. This is the **entry point for corpus discovery** — call it proactively whenever a request references the user's own content (emails, inbox, messages, research, uploaded documents/files), even without explicitly naming connectors, before constructing tag-scoped `bigdata_search` queries. **When to use** - "What content sources do I have?" / "What's connected?" - Any first-person TASK that lives in private content, not just meta-questions about sources — e.g. "summarize my emails this week", "who emailed me about X", "what's in my broker research". These are IN-SCOPE. Trigger discovery, do not refuse. - "Show me content from my Legal SharePoint connector" → call this first to resolve the connector's `label`, then call `bigdata_search` passing `label: