Bigdata.com

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_help

Create an owned watchlist from a name and rp entity ids. **Requirements:** - `items` must be rp entity ids from `find_securities` or prior tool output. Do not pass company names. - Provide at least one id and at most 100 unique ids. Duplicates are removed before create. - Do not set category, sharing, or global/thematic fields via this tool. **Limits:** - MCP create caps unique items at 100. Existing product watchlists may be larger. `bigdata_get_watchlist` by id can still return more than 100 items for those lists. **Routing / follow-ups:** - After create, offer both a structured portfolio view with `bigdata_portfolio_tearsheet` and a search across the full list with `bigdata_watchlist_search` using the returned watchlist `id`. - Use `bigdata_get_watchlist` without an id to list owned watchlists later. **Display rule.** This tool returns display-ready markdown. Relay it verbatim as the user-facing reply. Render id and entity id values as inline code.

bigdata_create_watchlist

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.

bigdata_etf_tearsheet

Returns the document by its ID.

fetch

**Routing (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**, **ETFs**, **mutual funds** and other **funds**. **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**, **ETF**, **MUTUAL_FUND** or **FUND** (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**, **ETFs**, **mutual funds** and other **funds**. Narrow to one kind with **`security_types`** (e.g. `["MUTUAL_FUND"]` for mutual funds alone). 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.

find_securities

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.

bigdata_company_tearsheet

**PREFERRED 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.

bigdata_sentiment_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. **Not this tool:** for a schedule of economic releases - what is coming this week, when a specific indicator is published, releases across several countries, or a list filtered by impact or category - call bigdata_events_calendar with calendar_type "economic_calendar" instead. This tool covers one country at a time, has no date range and no filters, and carries the calendar only as a snapshot section alongside indices, currencies and yields. "Recent economic releases" fits either tool, so pick on the shape of the answer: this one gives the latest readings grouped by sector, the economic calendar gives a dated list of the releases themselves. **When to Use:** Use this tool when users ask about: - The overall state of one country's economy, as a snapshot across sectors - 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_country_tearsheet

Returns a professionally formatted markdown calendar. Two different calendars are available and the calendar_type parameter picks between them. **Calendar routing (calendar_type is REQUIRED, always set it):** - "corporate_calendar" → corporate events: earnings announcements, conferences, IPO listings and company delistings. Use this when the question is about a company or a group of companies. - "economic_calendar" → macroeconomic releases: inflation prints, GDP, employment figures, interest rate decisions and central bank events, by country. Use this when the question is about an economy rather than a company. **Which one answers the question:** - "What are Apple's upcoming earnings?" → corporate_calendar - "Which US companies report earnings this week?" → corporate_calendar - "When is the next US CPI release?" → economic_calendar - "When are US average hourly earnings released?" → economic_calendar. "Earnings" is a word both calendars use: a company's earnings are corporate, wages paid across an economy are a "labor-market" release. - "What economic data comes out this week?" → economic_calendar - "Is there an ECB rate decision this month?" → economic_calendar with countries ["EMU"] - "What is on the calendar this week?" → corporate_calendar, unless the user is clearly asking about the economy There is no default. Pick one on every call, because which calendar answers the question is a decision only the caller can make. **Not this tool:** when the question is about the broader state of one country's economy rather than a schedule of releases - its market indices, currency, bond yields, or how it compares with its G7 peers - call bigdata_country_tearsheet instead. Use this tool when the answer wanted is a dated list of releases, when more than one country is involved, or when the releases need filtering by impact or category, none of which the tearsheet can do. The two calendars take different filters, and the filters object carries only the ones its calendar has. A filter belonging to the other calendar is rejected rather than ignored, so do not mix them in one call. Make two calls if both calendars are wanted. **Output Format (calendar_type "corporate_calendar"):** 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:** - A question that names a period - "this week", "in March", "next quarter" - is answered by passing start_date and end_date for exactly that period - No dates at all: the next 30 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 **Output Format (calendar_type "economic_calendar"):** A markdown document split into what has already been published and what is still to come. 1. **Header Section:** - Title: "# Economic Calendar" - Metadata recap: date range (From/To) and the applied filters 2. **Released Section:** Markdown table with columns: DATE | COUNTRY | EVENT | PERIOD | ACTUAL | CONSENSUS | PREVIOUS | IMPACT - ACTUAL is the figure that was published, CONSENSUS what the market expected, PREVIOUS the prior period - PERIOD is the month or quarter the figure describes, which is not when it was released: a January inflation print published in February describes January - Figures are shown with their magnitude and unit already applied, so 250K means 250,000 3. **Upcoming Section:** Same columns, for releases that have not happened yet. ACTUAL is empty for all of them, which is expected and not missing data. The economic calendar has no pagination. A window that returns too much is narrowed by date, and the document says how many releases it left out. **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_events_calendar

Resolves 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``, ``MUTUAL_FUND``, ``FUND`` or ``BOND``. The first four follow knowledge-graph ``company_etf_type``, which maps ``ETF`` to ``ETF``, ``OPEN-ENDED MUF`` and ``CLOSED-ENDED MUF`` to ``MUTUAL_FUND``, ``OPEN-ENDED FUND`` and ``CLOSED-ENDED FUND`` to ``FUND``, and ``BUSINESS`` to ``COMPANY``. ``BOND`` applies when the row has ``security_class`` ``FIXED_INCOME`` or the inferred id type is ``BISIN`` / ``BCUSIP``, and takes precedence over the others. Bonds nest the issuer under ``details.company``. Identifier cross-reference lists are on the flat non-bond rows only. **Data sources (LLM instruction):** When presenting results, identify data sources implied in the payload and add a "Data sources" section.

get_securities