Bigdata.com MCP + Stream Deck

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.

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.

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.

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.

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.

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?"

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:<label> verbatim in the smart-query context field, prefixed with tags: (e.g. context: "tags: label:<label>"). - Whenever you need a connector_id for bigdata_list_documents. When NOT to use - To retrieve or read documents — that is bigdata_search (broad content) or bigdata_fetch_document (single private document). - To enumerate tags — use bigdata_list_tags. Behaviour guidelines - Present connectors with human-friendly context: type, label, last sync time (relative — *"2 hours ago"*), and document count. - Filter out archived: true connectors from user-facing summaries unless the user explicitly asks about inactive sources. - Surface last_sync_status proactively when it is FAILURE or DISABLED — e.g. *"your Goldman connector last synced 3 days ago but encountered an error"*. - DISABLED status means that connector failed to sync several times in a row and have been disabled by the system. - The label value returned here equals the value used in the label:<label> auto-tag applied to every document ingested via the connector. Use it to construct subsequent tag-scoped searches. - Cache connector_id values within the session — do not re-fetch between calls. Output An array of connector objects. Each contains: connector_id, label, type (email | investment_research | sharepoint), description, share_with_org, archived, files_count, last_sync_at, last_sync_status, last_sync_error_msg, last_sync_count, created_at, updated_at.

Returns a paginated, filterable list of document metadata from the user's documents in Bigdata.com. Use this tool for preliminary discovery and navigation — understanding what documents exist within a connector, what tags are present, how content is organised by date. When to use - "What was uploaded yesterday?" / "What's the latest in my Legal connector?" / "What broker reports came in this week?" - To enumerate documents matching filters (connector, origin, tags, date, file-name fragment). - To resolve a document_id for the bigdata_fetch_document tool from a tag / connector / date query. When NOT to use - To retrieve or synthesise content — that is bigdata_search, which goes through Bigdata's relevance/ranking layer. - To read a single private document — that is the bigdata_fetch_document tool. - To enumerate connectors → bigdata_list_connectors. - To enumerate tags → bigdata_list_tags. Filter scoping guidelines - The tags filter uses OR logic — passing multiple tags returns documents matching any of them. To narrow down by sender within a connector, combine connector and tags. - Auto-generated tags (broker:, from:, to:, label:) are first-class filters. Validate them via bigdata_list_tags if you want to avoid empty result sets. - file_name is a case-insensitive partial match. - from_date is inclusive — documents created on or after the given ISO 8601 timestamp. Pagination Results are limited per page (page_size, default 50). Advance page to retrieve subsequent documents. A full page suggests more may exist. Behaviour guidelines - If the result set is large, summarise before listing (e.g. *"I found 47 documents on this page — here are the 5 most recent"*) and offer to narrow by tag, date, or file name. - When presenting documents, surface notable auto-tags from the tags field to help the user understand corpus organisation. - Once discovery is complete and the user wants to read or synthesise, route to bigdata_search or the bigdata_fetch_document tool — do not loop back to this tool. - When the user wants to manage their uploaded files (rename, delete, change sharing, or upload originals of any type/size), point them to https://app.bigdata.com/files — those actions are done there, not through this tool. Output An array of document summaries. Each carries: id (the document_id for the bigdata_fetch_document tool), file_name, request_origin, content_type, status, connector_id, tags (array of {id, name}), raw_size, shared_with_orgs, created_at, updated_at, published_at, error_code.