company_ai_maturity
ChatGPTcompany_ai_maturity — AI maturity score / AI readiness / GenAI buying intent / cloud provider centricity for a batch of companies. Returns raw HG Insights AI-maturity signals: ai_maturity_score (0-100 composite), ai_maturity_rank (1 = highest), ai_maturity_6m_delta (6-month change), ai_product_use (AI product installed), genai_intent_score (GenAI buying intent — 0-100), data_maturity_level (LOW/MEDIUM/HIGH) and data_maturity_score (0-100), plus cloud_centricity (dominant provider) and cloud_intensity (per-provider aws/azure/gcp detection volume — UNBOUNDED, values in the thousands are normal, NOT 0-100 scores or dollar amounts; compare providers within a company, never across companies). These are raw scores as HG returns them — no derived stage labels. Accepts a batch: pass hg_ids and/or domains (up to 25); one entry per matched company under companies[]. IMPORTANT — a company not found or without AI-maturity coverage is omitted from companies[] and echoed in not_found[]. Do NOT use for AI spend in dollars (use company_ai_spend), derived AI-adoption stage labels or operating-signals rollup (use company_operating_signals), full AI/ML tech-stack detail (use company_technographic), or topic-level buying-intent evidence (use company_intent).
company_ai_spend
ChatGPTcompany_ai_spend — estimated annual AI spend / GenAI budget / how much a company spends on AI (USD), broken down by AI category and country, from HG Insights v2. Each row: spend (USD), category_name / category_id (e.g. "Total AI Spend", "AI Software", "AI Services", "AI Hardware"), and country_name / country_code. Accepts a batch: pass hg_ids OR domains (up to 25; mutually exclusive — hg_ids wins when both supplied). One entry per matched company under companies[] (unmatched omitted). Filter with category_ids or category_names. WARNING: category_names is case-insensitive substring match (max 10); non-matching name returns 0 rows — prefer category_ids or resolve names via the spend-categories catalog. Paginate with max_results (1–25) and offset. Credits: 2 per matched company returned; unmatched companies cost nothing. Do NOT use for total/overall IT spend across all categories — use company_spend. Do NOT use for cloud-vendor spend breakdowns (AWS/Azure/GCP) — use company_cloud_spend. Do NOT use for AI adoption/maturity scores (this returns dollar spend, not scores) — use company_ai_maturity.
company_cloud_spend
ChatGPTRetrieve cloud vendor and technology service data from HG Insights Cloud Dynamics (Intricately) for a specific company. Provide a company domain (e.g., "nike.com") or an HG Insights company ID (hg_id). If both are provided, hg_id takes precedence. Returns cloud vendors by service category with adoption dates and geographical traffic distribution for technology intelligence and sales opportunity analysis.
company_contracts
ChatGPTRetrieve contract intelligence for a company. Provide a company domain (e.g., "salesforce.com") or an HG Insights company ID (hg_id). If both are provided, hg_id takes precedence. Domain-based lookups automatically include contracts held by subsidiaries across the corporate family (matching HG app behavior). HG ID lookups return contracts for that specific entity only. IMPORTANT SCOPE: This tool returns ICT outsourced contracts (typically via Global System Integrators like Accenture, IBM, Cognizant) - NOT individual product contracts or vendor renewal dates. Data comes from publicly announced contracts and is not comprehensive. Best combined with firmographic, technographic, and IT spend data for full context. Returns vendor relationships, deal values, contract status, service line breakdowns, contract durations, and contractHolder (the entity holding each contract). Optionally include U.S. federal government contracts from USAspending.gov by setting includeFederalContracts=true (requires datagov integration). Federal data includes awarding agency, contract type, NAICS codes, set-aside information, and SAM.gov entity registration details.
company_enrich
ChatGPTcompany_enrich — enrich / full company profile / multi-signal lookup for a batch of up to 25 known companies in one call; returns { companies: [...] }. Select companies with hg_ids and/or domains (at least one required; both may be combined and are unioned). Unmatched companies are omitted — do not assume positional alignment. Choose sections via fields: firmographics · spend · ai_spend (opt-in) · technographics · contracts · ai_maturity · cloud_maturity · statistics · market_benchmarks. Defaults to firmographics + technographics + spend when fields is omitted. The same fields/filters/pagination apply to every company; credit cost scales with companies returned. contracts/statistics/market_benchmarks are entitlement-gated — if your org lacks access they are omitted and listed under unavailableSections rather than failing the call. Use this when you have a known set of companies and need 2+ data sections in one round-trip. Do NOT use when: you only need firmographics — use company_firmographic (faster, cheaper, smaller payload, accepts a batch too); you are discovering companies without identifiers — use search_companies; you need the full multi-level ownership tree — use company_hierarchy; you need exactly one section — prefer the dedicated tool (company_firmographic, company_technographic, company_spend). The interactive dashboard renders the first returned company (batches show a banner).
company_fai
ChatGPTRetrieve functional area intelligence about how specific products are used within company departments. PREFERRED: Pass productIds (numeric IDs from company_technographic productId field) for exact, deterministic lookup. FALLBACK: Pass product names for fuzzy matching. Provide a company domain (e.g., "cisco.com") or an HG Insights company ID (hg_id). If both are provided, hg_id takes precedence.
company_firmographic
ChatGPTRetrieve comprehensive business information about companies including industry, employee count, revenue, location, and technologies. Provide a company domain (e.g., "cisco.com") or an HG Insights company ID (hg_id). If both are provided, hg_id takes precedence.
company_gov_opportunities
ChatGPTFind open federal solicitations where a specific company is the incumbent contractor or a likely bidder based on their NAICS codes and existing contract portfolio. Resolves the company via SAM.gov, then searches for matching opportunities. Requires the SAM.gov (Data.gov) integration to be configured.
company_gov_relationships
ChatGPTShow prime/subcontractor relationships for a company from federal subaward data. Reveals which companies a prospect teams with on government deals, including contract counts and total subaward values. Data sourced from USAspending.gov subaward records. Requires the SAM.gov (Data.gov) integration to be configured.
company_hierarchy
ChatGPTcompany_hierarchy — corporate ownership tree / subsidiaries / parent company / who owns X / sister companies. Traverses the UCM tree for ONE company by domain or hg_id. company_domain IS LITERAL, not a brand alias: "alphabet.com" = a UK fleet subsidiary, NOT Google — a wrong-but-valid domain returns confident WRONG data with NO error. Holding domains: Alphabet="abc.xyz", Meta="meta.com". No known domain? Resolve via search_companies FIRST. USE WHEN: "who owns X?", subsidiaries, parent chain, or sister companies. Do NOT use for: firmographics only → company_firmographic; a full tech/intent/spend profile → company_enrich; a list of companies → search_companies. DEFAULTS: mode="children", depth=1 (direct children only). MODES: "children"=subtree at matched node · "full"=whole tree from GHQ · "parents"=ancestor chain up to GHQ. BEWARE: (1) Fortune-500 parents return 100–330+ nodes — depth is the size lever. (2) mode:"full" returns the WHOLE parent family; check company_level. (3) depth applies BEFORE filters — pair country_codes/naics/industry filters with depth:5+. (4) UCM may return DUPLICATE nodes; dedupe by id. RECIPES: "who owns X?"→mode:"parents" · all subs→depth:5 · EU entities→country_codes:["DE","FR"],depth:5. Trim returned columns with selected_fields. Matched node always kept even if it fails a filter. hierarchy:null → read no_match_reason. mode:"parents"+already_at_ghq:true → company IS the GHQ. Credit: 0.1/node.
company_install_time_series
ChatGPTGet installation intensity time series data for a company's technology usage. Returns monthly intensity values (0-100 scale) for each product over time. Use to answer trend questions like 'How has Cisco's usage of Snowflake changed over the past 2 years?' Filter by products, vendors, or categories. Each product includes current_intensity, intensity_momentum (positive = growing), and is_active status. Products are ordered by current intensity (descending) when no filters are applied. Empty results mean no matching products were found for the specified filters.
company_intent
ChatGPTGet a broad overview of intent signals for a specific company. Provide a company domain (e.g., "cisco.com") or an HG Insights company ID (hg_id). Returns the top intent topics by score with merged HG proprietary + TrustRadius buyer intent data including: topic scores, intent levels, buyer journey stages, context dispositions for competitive intelligence (e.g., displacement signals), and TrustRadius buyer activities. Use vendor_name for competitive intelligence (e.g., "Is Prudential looking at Snowflake competitors?"). NOTE: For topic-specific queries (e.g., "Does Caterpillar show intent for Siemens Teamcenter?"), use intent_category with the topic_name instead — company_intent returns the top topics by score and may miss specific topics for companies with many active signals.
company_operating_signals
ChatGPTRetrieve company operating signals by combining mentions-derived attributes (work_model, cloud_posture, esg_commitment, iot_posture, network_modernization, automation_stage) and GenAI maturity attributes (ai_trajectory, cloud_depth, genai_readiness, intent_adoption_gap). Provide a company domain (e.g., "cisco.com") or an HG Insights company ID (hg_id).
company_research
ChatGPTComprehensive company research that aggregates firmographic, technographic, IT spend, cloud spend, intent signals, contracts, and operating signals into a single interactive dashboard. Provide a company domain (e.g., "cisco.com") or an HG Insights company ID (hg_id). Requires fewer API calls than querying each tool individually.
company_spend
ChatGPTRetrieve IT spend data from HGInsights for a specific company. Provide a company domain (e.g., "cisco.com") or an HG Insights company ID (hg_id, e.g., "0FF69D9F596504A1FF4FF5B16FF"). If both are provided, hg_id takes precedence. Returns detailed spend amounts by category and geography, with currency formatting and category hierarchies for sales intelligence and market analysis.
company_technographic
ChatGPTREQUIRED WORKFLOW — follow these steps before calling this tool: 1. To check a SPECIFIC vendor/product: call get_vendor_information first → pass the returned vendorIds/productIds here. 2. To filter by category: call list_product_categories first → pass the exact category name here. 3. For a full unfiltered tech stack: call this tool directly with no filters. Skipping step 1 or 2 causes missed results (e.g., Snowflake is under 'Database Management', not 'Data Warehousing'). Get technographic data (technology usage) for a company by domain. Returns installed technologies with productId (numeric), vendor, usage intensity, and verification dates. Use the productId values from results when calling company_fai for precise lookups. Filter by 'categories' for category-based filtering, or by 'vendorIds'/'productIds' for exact vendor/product filtering (preferred for specific vendor lookups). Large unfiltered requests may be truncated. Default limit: 50. Empty results mean no data was found for the specified filters — do NOT say the tool lacks functionality. Provide a company domain (e.g., "cisco.com") or an HG Insights company ID (hg_id). If both are provided, hg_id takes precedence.
contact_enrich
ChatGPTEnrich contacts with full details: email, phone, social profiles, employment history. Supports Apollo and ZoomInfo providers (auto-selected based on org configuration). USES CREDITS. Use contact_search first to find contacts (free), then enrich the best matches. Accepts contact ID (most accurate), name + company, or up to 25 contacts for bulk enrichment. Specify provider to target the same provider used in search.
contact_search
ChatGPTSearch for contacts at a company by title, seniority, and location. Supports Apollo and ZoomInfo providers (auto-selected based on org configuration). Returns basic contact info (name, title, LinkedIn). FREE - does not use credits. IMPORTANT RULES: 1) Combine ALL title variations in ONE call using arrays (e.g., personTitles: ['VP Marketing', 'CMO', 'Head of Marketing']). 2) Do NOT make separate calls for each title - use ONE search with all titles combined. 3) If a search returns 0 results, STOP - do not retry with different title variations. Report 'no matches found' to the user. 4) Maximum 2 searches per user request (initial search + optional pagination). Use contact_enrich to get email/phone for the best matches.
get_product_attribute
ChatGPTget_product_attribute — resolve / search capability-theme attribute IDs from the HG Insights taxonomy. Attributes are cross-cutting capability tags (e.g. 'Cloud Computing', 'Security', 'SaaS', 'Open Source') — the semantic layer above individual products. This is a taxonomy lookup, NOT a per-product attribute reader: no product_id input; returns global taxonomy rows, not attributes attached to one product, and never returns which companies carry an attribute. Free — no credits consumed. Search by attributeName (case-insensitive substring, relevance-ranked) or pass known attributeIds to fetch specific rows. Provide at least one. Returns rows: attribute_id, attribute_name, attribute_description, attribute_parent_id (0 = root theme), attribute_level (1 = root, 2 = sub), product_count (breadth signal); plus top-level count. Taxonomy is hierarchical: 'Cloud' → root 'Cloud Computing' (level 1) plus children like 'Cloud Workloads' (level 2). Use when you need attribute_id(s) to filter product_search_and_enrich or company_technographic. Browse by searching a broad attributeName — do NOT enumerate IDs sequentially. Do NOT use to resolve a product category — use get_product_category. Do NOT use to resolve a vendor/company — use get_vendor_information.
get_product_category
ChatGPTget_product_category — resolve / look up / search taxonomy category names and IDs before an install query. Free — no credits consumed. Matching: categoryName and treeContains use case-insensitive LIKE substring matching — NOT fuzzy or semantic, so misspellings return zero rows with no warning. Use common partial terms rather than guessing full names. treeContains scans the full root → leaf path to scope to a whole branch. categoryCode / categoryId are exact lookups. Returns category rows (category_id, category_code, category_name, category_name_tree, has_category_installs, product_count) plus a top-level count. product_count covers direct products only, not the subtree. Prefer deeper leaf categories (longer category_name_tree) for precise filtering. Use this when: - You need the exact category name/id to pass to company_technographic (set hasInstalls: true to limit to categories with real install data). - You want to explore the category taxonomy by keyword. Do NOT use this when: - You want vendor details or a vendor_id — use get_vendor_information. - You want product attribute data — use get_product_attribute. - You want warehouse table schemas for SQL query planning — use hg_catalog (not product taxonomy). Requires at least one filter. When both categoryId and categoryCode are given they must match the same record (AND logic); if in doubt provide only categoryId.
get_product_information
ChatGPTGet comprehensive information about a software product including overview, pricing, competitors, and integrations from TrustRadius.
get_product_reviews
ChatGPTGet customer reviews and feedback for a software product with flexible filtering by rating and date.
get_vendor_information
ChatGPTLook up technology vendors from the HG Insights catalog. When vendorName is provided, performs fuzzy search and returns matched vendors with their product IDs — use these IDs with company_technographic's productIds or vendorIds parameters to check if a company uses that vendor's technologies. When vendorName is omitted, returns the full vendor catalog for discovery. TIP: To check if a company uses a specific vendor, call this tool first to get the vendor ID, then call company_technographic with the vendorIds parameter.
hg_catalog
ChatGPTReturns the HG Insights data warehouse schema — table names, descriptions, approximate row counts, and column definitions (name, type, description). Call this tool before writing queries with hg_data_query to discover available tables and columns. Pass an optional table_name to filter results to a single table.
hg_data_query
ChatGPTExecute read-only SQL SELECT queries against the HG Insights data warehouse. Call the hg_catalog tool first to discover available tables and columns before writing queries. Queries must be SELECT-only (no INSERT, UPDATE, DELETE, DROP, etc.). Returns rows, column names, row count, and credits consumed. Credit cost is dynamic based on the query (reported by the upstream API).
intent_category
ChatGPTFind companies showing intent for a topic, vendor, or product. Returns merged HG proprietary + TrustRadius buyer intent signals with signal strength (0-100), intent levels, buyer journey stages, and context types. Use list_intent_topics to discover available topics first.
list_fai_departments
ChatGPTlist_fai_departments — resolve FAI department name to hex ID / browse department taxonomy / look up department and role IDs before company_fai. Resolver for the Functional Area Intelligence (FAI) taxonomy: lists valid FAI department and role names (with their hex-encoded IDs) from the official HG Insights catalog. Returns each department's hex ID and name plus its roles (role hex ID and name). The catalog is company-independent — this tool does NOT return any company's technology usage. Use this when you need to discover or confirm the canonical name/ID of a department or role before querying departmental data — for example to resolve a valid department_ids value for company_fai, or to map the departmentId/roleId fields returned by company_fai back to human-readable names. Always look up department and role IDs here rather than guessing or fabricating them. Do NOT use this when you want how a company actually uses products across its departments — call company_fai (actual departmental tech usage) with a company_domain or hg_id instead. Optionally filter by department name (case-insensitive partial match) and page with limit/offset.
list_intent_topics
ChatGPTSearch or list intent topics from the official HG Insights catalog. Intent topics represent buying signals and research areas that indicate what technologies companies are actively investigating or planning to purchase. Use the 'query' parameter to fuzzy-search topics by name (recommended to avoid overwhelming results). Use 'category' to filter by topic category. IMPORTANT: When recommending intent topics, always use this tool to look up real topic IDs and names — never guess or fabricate them.
list_product_attributes
ChatGPTSearch or list product attributes from the official HG Insights catalog. Product attributes describe technology characteristics like deployment model (Cloud-Based, On-Premise), licensing (Open Source, Enterprise), and other features. Use the 'query' parameter to fuzzy-search attributes by name (recommended). IMPORTANT: When filtering by product attributes, always use this tool to look up the exact attribute name — never guess or approximate.
list_product_categories
ChatGPTSearch or list product categories from the official HG Insights catalog. Returns hierarchical category structure with IDs, names, and category trees. Use the 'query' parameter to fuzzy-search categories by name (recommended). Use 'parentId' to navigate the category hierarchy. IMPORTANT: When filtering company technographics by category, always use this tool to look up the exact category name — never guess or approximate.
phoenix_get_artifact
ChatGPTphoenix_get_artifact — fetch/read/retrieve ONE Phoenix artifact's content or URL by id or run id. Pass either a synthetic artifact_id ({runId}-html, {runId}-pdf, …) OR a bare run_id (UUID) — not both needed. Returns { found: true } with the artifact type, an absolute webapp URL to open it, and the brief's HTML body when it's small enough to inline (large or non-HTML deliverables return the descriptor + URL only, no inlined content). If the run has no artifact (queued, failed, unknown, or an id that doesn't match the run's real type), returns { found: false } rather than erroring. Use this when you already have a specific artifact/run id and want its content or link. Do NOT use it to discover which artifacts exist — use phoenix_list_artifacts; to check a still-running job use phoenix_get_run_status.
phoenix_get_run_status
ChatGPTGet the status and details of a Phoenix agent run. Returns the current status, any generated artifacts, and cost information.
phoenix_invoke_agent
ChatGPTExecute a Phoenix AI agent with the specified inputs. Creates an agent run that will be processed asynchronously. Returns the run ID for tracking status and retrieving artifacts.
phoenix_list_agents
ChatGPTList all available Phoenix AI agents for this organization. Returns agents that have been published and are ready for invocation. Each agent has specific capabilities defined by its allowed tools and input schema.
phoenix_list_artifacts
ChatGPTphoenix_list_artifacts — list/browse/enumerate generated deliverables & artifacts already produced in this Phoenix org. Returns one row per run with its synthetic id, artifact type, source (agent vs uploaded), created/expiry dates, and an absolute webapp URL to open it. Narrow with artifact_type, source, or a specific run_id, and page with limit/offset. Filters are structured only — there is NO free-text or content search, so you cannot search by company name or brief text. Use this to enumerate or find recent deliverables across the org. Do NOT use it to fetch one artifact's HTML body — use phoenix_get_artifact; to check a run that may still be in progress use phoenix_get_run_status; to start a new deliverable use phoenix_invoke_agent.
phoenix_onboarding
ChatGPTphoenix_onboarding — orient/onboard/get started with Phoenix / "what can Phoenix do" / "where do I start". Renders a branded, personalized getting-started widget recommending the best GTM workflows to run first, with a text fallback for clients that cannot render MCP-app widgets. Use this when the user is new to Phoenix or asks how to begin (e.g. "I'm getting started", "what can Phoenix do", "where do I start") and needs orientation on Phoenix's tools and capabilities. Do NOT use it when you already know which specific data tool to call (e.g. a company's firmographics, technographics, or intent) — call that tool directly instead. On that intent you MUST ask EXACTLY these two questions and WAIT for the answers before doing anything else. Ask the role question as a NUMBERED choice list (so the user can reply with a number), then the company question on its own line — formatted exactly: "First, what's your role? Reply with the number: 1. Sales 2. Marketing 3. Customer Success 4. Exec / Strategy 5. Other" and "And what company or product do you represent?". Ask ONLY those two — do NOT ask open-ended questions like "what are you hoping to do with Phoenix", and do NOT present role as a free-text question. Do not skip, improvise, or guess the answers. After you have BOTH answers, call this tool with role, company, and 1–3 recommended_prompts slugs. See each parameter for how to pick and when to omit.
phoenix_upload_artifact
ChatGPTphoenix_upload_artifact — file/ingest/register an externally-produced PDF or HTML deliverable into Phoenix. Provide a publicly-fetchable https URL; Phoenix server-side fetches it (SSRF-guarded), stores it in S3, and it appears in the org's Artifacts tab tagged "Uploaded" — indistinguishable from an agent-generated deliverable. Returns the upload run id and artifact descriptor. Only PDF (application/pdf) and HTML (text/html) up to 25 MB; URL must be https and reachable without auth. Use this when you already have a finished deliverable hosted somewhere and want it filed in Phoenix. Do NOT use this to generate a deliverable from scratch — use phoenix_invoke_agent; do NOT use it to read back an existing artifact — use phoenix_get_artifact or phoenix_list_artifacts.
product_search_and_enrich
ChatGPTproduct_search_and_enrich — search / discover / hydrate products & technologies from the HG Insights catalog. One tool, two actions. action='search' (free) returns a slim, paginated hit list of product_ids matching name/vendor/category/attribute filters — use it to disambiguate a fuzzy product name or browse products under a vendor/category. action='enrich' (1 credit per successful match) hydrates 1-50 known product_ids into full catalog records: product_details, category_info, vendor_info. Typical flow: search to find the id, then enrich the chosen id(s). Unmatched enrich ids are silently omitted and consume no credits. When filtering by category, prefer resolving the exact category first via get_product_category and passing category_id — category_name does a substring match that can silently pick the wrong category (e.g. 'CRM' can match a BPO/outsourcing category). Same guidance applies to attributes via get_product_attribute (use attribute_ids) and vendors via get_vendor_information (use vendor_id). Do NOT use for pricing, competitor narrative, or user reviews. This tool returns HG catalog taxonomy (category/vendor/attributes/install signals) only.
search_companies
ChatGPTSearch for companies in the HG Insights data warehouse. PREFERRED: Use structured parameters (technologies, industries, countries, states, revenueMin/Max, employeesMin/Max, companyName, trends, excludeTechnologies, technologyMode) for reliable, deterministic SQL generation. ALTERNATIVE: Pass a natural language searchCriteria string (less reliable). Read the company_search_guide prompt first for product aliases and usage examples. If you provide hg_id or hg_ids, other filters are optional. Credit cost is dynamic based on the query (reported by the upstream API).
search_federal_contracts
ChatGPTSearch USAspending.gov for U.S. federal contract awards. Filter by awarding agency, NAICS code, PSC code, keywords, contract value range, date range, and small business set-aside type. Returns contract details including recipient, obligation amount, and performance location. Requires the SAM.gov (Data.gov) integration to be configured.
search_gov_opportunities
ChatGPTSearch SAM.gov for open U.S. federal solicitations (RFPs, RFQs, presolicitations, sources sought). Filter by keywords, NAICS code, PSC code, agency, set-aside type, posting date, and response deadline. Returns opportunity details including title, agency, deadline, and direct SAM.gov link. Requires the SAM.gov (Data.gov) integration to be configured.
search_industries_naics_sic
ChatGPTsearch_industries_naics_sic — resolve / translate industry names and codes (NAICS, SIC, HG industry_id) to feed into search_companies (industry_ids, naics_codes, sic_codes). Searches across HG industry (23 buckets), NAICS 2012 (~2,200 codes), and SIC 1987 (~1,500 codes) in one call. Use when you have an industry NAME or colloquial term ("fintech", "software publishers") and need its code(s) before an industry-scoped company search — resolve here first. Do NOT use to find companies — that is search_companies (pass the codes you resolve). Do NOT use to find what industry a specific company belongs to — call company_firmographic; this searches taxonomy definitions, not company records. Do NOT use for technology/product categories ("IaaS","CRM","cloud infrastructure") — use get_product_category. Use cases: q=name fragment · q=code crosswalk · q=numeric prefix→sector+descendants · q="software,saas" multi-term OR · taxonomy=naics|sic|industry for deduped rows · naics_leaf_only=true for 6-digit leaves. Colloquial terms (fintech, saas, etc.) expanded server-side — alias_expansions shows what ran. Zero-result: up to 5 suggestions for near-miss q. NAICS: hierarchy_level + is_leaf — only leaves safe for downstream filters. Downstream: use sic.sic_standard_code ("7372") not sic.sic_code ("I7372"). HG quirk: no Software bucket — 511210/7372→Computer Mfg; 541511/518210→Professional Services. Use NAICS/SIC for tech. Paging: offset_exceeds_total flags paging-past-end. Max limit 500. Free.
sec_filing_section
ChatGPTExtract specific sections from SEC 10-K, 10-Q, or 8-K filings. Returns cleaned text optimized for analysis. SECTION MAPPING GUIDE - For questions about: 10-K ANNUAL REPORTS: - Company overview, products, markets, strategy → "1" (Business) - Risks, challenges, uncertainties, threats → "1A" (Risk Factors) - Financial performance discussion, trends → "7" (MD&A) - Executive pay, stock options, bonuses → "11" (Executive Compensation) - Board members, leadership team → "10" (Directors & Officers) - Legal issues, lawsuits, litigation → "3" (Legal Proceedings) - Properties, facilities, real estate → "2" (Properties) 10-Q QUARTERLY REPORTS: - Quarterly performance analysis → "part1item2" (MD&A) - Updated risk factors → "part2item1a" (Risk Factors) - Quarterly financial statements → "part1item1" (Financial Statements) 8-K CURRENT EVENTS: - CEO/CFO changes, executive departures → "5.02" (Officer Changes) - Earnings announcements, quarterly results → "2.02" (Results of Operations) - M&A activity, acquisitions, divestitures → "2.01" (Acquisition/Disposition) - Cybersecurity breaches, incidents → "1.05" (Cybersecurity Incident) - New contracts, major partnerships → "1.01" (Material Agreement) - Contract terminations → "1.02" (Termination of Agreement) - Bankruptcy filings → "1.03" (Bankruptcy) - Auditor changes → "4.01" (Accountant Changes) - Stock delisting notices → "3.01" (Delisting Notice)
sec_full_text_search
ChatGPTSearch within SEC filing content for specific terms or phrases. Supports boolean operators (AND, OR, NOT), wildcards (), and exact phrase matching ("quoted"). Use to find filings mentioning specific topics, risks, or events. QUERY EXAMPLES: - "material weakness" - Find exact phrase - cybersecurity AND breach - Both terms required - layoff - Wildcard matching (layoff, layoffs, etc.) - "going concern" OR "substantial doubt" - Either phrase - acquisition NOT merger - Exclude term COMMON USE CASES: - Risk monitoring: "material weakness", "going concern" - Cyber incidents: "cybersecurity incident", "data breach" - M&A activity: acquisition, merger, "change of control" - Restructuring: layoff*, "workforce reduction", restructuring - Leadership changes: "chief executive", "board of directors"
web_search
ChatGPTweb_search — live web search / browse the internet / find recent news or public facts not in HG Insights' data. Runs a live Tavily search and returns results (title, URL, snippet) plus an optional AI answer summary. Cost: 0.05 credits (basic) or 0.10 credits (advanced, higher relevance). Use when: you need current/breaking news, background on a person or topic, or any fact on the open web rather than HG's structured data. Do NOT use when a purpose-built HG tool covers the request — reach for company_enrich/company_firmographic (profile/size/HQ/industry), company_technographic (installed tech), company_intent (buying signals), or search_companies instead, since those return richer structured HG data. Do NOT use to search SEC filing text — use sec_full_text_search. Do NOT use for general knowledge you already know. OPERATORS: boolean exclusion (-term) is NOT honored — filter unwanted results yourself. VERBOSITY: includeRawContent=true returns the full cleaned page body (no extra cost) but can add ~10KB+ per result — cap with maxContentLength, keep max_results low, and restrict sources with allowedDomains. searchDepth='advanced' improves relevance and snippet quality (not raw-page extraction) at 2x cost.
