account_status
ChatGPTReport the current user's account details and credit balance. Free. No arguments — identity is taken from the authenticated API key. Call this when the user asks about their plan, credits, or limits, and consult it before attempting tools pay-as-you-go can't use (headcount_trends, website_traffic, employee_reviews, product_reviews, job_posts, social_posts) or the Funding Detail / M&A and Investment enrichment sections (not on pay-as-you-go) to pre-empt a 403. The tier also sets the news_signals lookback limit (pay-as-you-go 6 months, subscription 1 year, agent and enterprise unlimited) and the generate_company_list window (pay-as-you-go 20, every other plan 500). Returns: is_enterprise (bool — true only on enterprise, not agent; do not use it to decide funding/M&A access, which subscription and agent have too), package_type (top_up = pay-as-you-go | subscription | agent | enterprise), credit_balance (remaining credits), currency.
company_data
ChatGPTEnrich a company with the requested sections, returned as Markdown. akta-pro renders the (large, deeply-nested) company data into clean Markdown server-side. Sections (credits): firmographic 2, business_model 2, company_assessment 2, trust_signal 0.5, company_hierarchy 0.5, digital_presence 0.5, financial_estimate 0.5, location 0.5, management_profile 1, product_offering 2, strategic_signal 1.5, customer_profile 1, industry 0.5, technology 2, funding_detail 3, mna_and_investment 5. funding_detail and mna_and_investment are included on Subscription, Agent and Enterprise plans. Subscription and Agent get a trimmed view (first 3 rounds/investors/deals, month-year dates, with a note listing what is withheld); Enterprise gets the full data. On pay-as-you-go they are skipped (not an error); the returned Markdown notes any that were dropped. funding_detail and mna_and_investment are included on Subscription, Agent and Enterprise plans. Subscription and Agent get a trimmed view (first 3 rounds/investors/deals, month-year dates, with a note listing what is withheld); Enterprise gets the full data. On pay-as-you-go they are skipped (not an error); the returned Markdown notes any that were dropped. Prerequisite: company must be an akta-pro uuid from company_search (free). Names and websites are rejected. API: GET /company/enrichment/markdown — the Markdown variant of akta-pro's Company Data API, documented at https://docs.akta.pro/api-reference/company-data; the fields each section carries are listed at https://docs.akta.pro/company-data-dictionary.
company_data_concise
ChatGPTReturn a condensed company overview (no section selection). akta-pro returns a slimmed JSON with the fluff redacted — much smaller than full enrichment. Flat cost 3 credits. This tool never returns funding or M&A data. Use company_data with funding_detail / mna_and_investment (Subscription, Agent and Enterprise plans) to get them. Prerequisite: company must be an akta-pro uuid from company_search (free). Names and websites are rejected. API: GET /company/enrichment/concise (akta-pro Company Data API — https://docs.akta.pro/api-reference/company-data).
company_search
ChatGPTResolve a company by name or website to its akta-pro identifiers. ALWAYS call this first. Every other company tool accepts ONLY the uuid returned here — names and websites are rejected — so this is also the only way to check whether a company exists in akta-pro at all. Free (0 credits). Returns candidates with uuid, name, website, product_category, and public/private status. A child company also carries parent_company ({name, uuid}) pointing at its parent. By default only top-level companies are returned; pass include_child_companies=True to also match subsidiaries and divisions. Searching by a child company's own uuid finds it either way. Results are ranked but not disambiguated: one website can map to several distinct entities (stripe.com → Stripe, Stripe Capital, Stripe Atlas). Pick the candidate that matches the user's intent, and if none does, say the company isn't covered rather than passing a guessed uuid.
employee_reviews
ChatGPTEmployee reviews: overall + dimension-level ratings and individual reviews (Glassdoor et al.). 1.5 credits per 50 reviews. Prerequisite: company must be an akta-pro uuid from company_search (free). Names and websites are rejected.
generate_company_list
ChatGPTRun a company list: the companies matching your filters. Cost: 1.5 credits per call that returns companies + 0.2 per company, so 20 companies is 5.5 credits. A query matching nothing is FREE. The cost does not depend on the fields: this tool always returns the same identifying fields per company and never full enrichment, so screening a list is cheap and predictable. For depth, call company_data on the few uuids that matter — that is billed per section, on those companies only. Prerequisites, in order: list_filters() to see what exists, then get_filter_values() for every filter that has a dropdown_type. Use those values VERBATIM and never write one from memory — these are exact strings from akta-pro's data, so an invented value matches zero companies instead of erroring. Key this call by each entry's filter — the one name that field has everywhere. Send at least one selective filter. A very broad or unfiltered query is rejected, not slow — it exhausts the backend's query timeout. Always read warnings in the result. A value that was dropped there means the list is BROADER than what the user asked for, which otherwise looks exactly like a good answer; a coerced entry means a value was normalised to its canonical form (harmless, but worth trusting over your own spelling next time). A filter key that isn't in list_filters() is an error, not a warning — the call fails with the offending key named and costs nothing, so fix the key and retry rather than guessing. Each company comes back with uuid, name, website, product_category, company_type and hq_country — that is the whole row; there is no option to return more per company. Feed a uuid straight into company_data / news_signals for depth on one of them. total_count saturates at 500, meaning "500 or more" — it is also the deepest page reachable, so narrow the filters rather than paging past it. API: POST /list/generate/companies/ — akta-pro's List Generation API, documented at https://docs.akta.pro/api-reference/listgen-api (filter keys, value shapes, sort fields, response envelope).
get_filter_values
ChatGPTGet the allowed values for one or more filters. Free (0 credits). Two kinds of result: A fixed list -> [{label, value}]. Use `value` verbatim; `label` is just the human-readable form. `query` substring-filters the list. A searched taxonomy (industry.industry, industry.naics, industry.sic; investors resolve to companies) -> ranked matches with a code and a similarity score. query is REQUIRED, and you use the code. For industry, level picks how deep in the taxonomy to search — leave it at the ['l4'] default for a specific niche, and pass ['all'] for a broad sector, since a broader code matches every company beneath it in the hierarchy. NEVER write a value from memory. These are exact strings from akta-pro's data, and a value that merely looks plausible matches zero companies instead of failing — so a made-up value silently ruins the whole query. If nothing returned clearly means what the user asked for, leave that filter out rather than forcing a weak match. Check truncated / total_available: if what you need isn't in the list, re-query with a narrower query. API: POST /filters/options/ (akta-pro) — the value lookups behind the filters documented at https://docs.akta.pro/api-reference/listgen-api.
headcount_trends
ChatGPTLinkedIn-sourced headcount: total employees, historical growth, and breakdown by function. 2.5 credits. Prerequisite: company must be an akta-pro uuid from company_search (free). Names and websites are rejected.
industry_search
ChatGPTResolve a free-text industry to akta-pro industry codes. Free (0 credits). Returns code, industry_name, and similarity score. The codes serve two purposes: news_signals — pass them as the `industry` filter. company lists — use them as industry.industry filter values in generate_company_list (the same lookup as get_filter_values with dropdown_type 'industry.industry'). A broader code matches every company beneath it in the hierarchy. level picks the taxonomy depth(s); several can be combined.
job_posts
ChatGPTLive LinkedIn/Indeed job posts: title, location, description, compensation, experience level, and key skills. 4 credits. Prerequisite: company must be an akta-pro uuid from company_search (free). Names and websites are rejected.
list_filters
ChatGPTList the filters available for building a company list. Free. Call this FIRST whenever the request is for a SET of companies rather than one named company. Each entry gives: filter the field's enrichment path — the key to use in your output, and the only name it has description what it means / when to use it value_shape the JSON shape to emit how_to_get_values either "look up with /list/options/" or "supply the value directly" dropdown_type present only when a lookup is available; always the same string as filter same_element_group present only for a repeated sub-object; every filter sharing a group must match the SAME element Next step: for every filter you picked that has a dropdown_type, call get_filter_values() and use the returned values verbatim. Filters without one take their value directly — ranges as {"gte": …, "lte": …} (inclusive, either bound optional), booleans as true/false, name filters as a string (case-insensitive substring, so a surname or partial name works). API: GET /filters/list/ (akta-pro). The same keys are documented publicly under "Available Filters" at https://docs.akta.pro/api-reference/listgen-api — read that when a catalogue entry's description isn't enough to decide on a key.
news_detail
ChatGPTFetch the full article body for specific news id(s) from news_signals. Use this after news_signals when you need the complete text of particular articles — pass their ids here. Returns up to 10 articles with the same fields as full_enrichment plus full_text. Cost: 0.1 credits per call + 0.01 per article. API: GET /news/by-id/ (akta-pro News Signals API — https://docs.akta.pro/api-reference/news-signals).
news_signals
ChatGPTFetch a list of news signals by company, industry, topic, or title (JSON). Typically provide one of company, primary_company, industry, query, or title to anchor the search. Cost: 0.1 credits per call + 0.01 per article. company matches any article mentioning the company; primary_company keeps only the articles the company is actually the subject of. Prefer primary_company for "news about X" / company-monitoring requests, and company when broad mention coverage matters. They are mutually exclusive — passing both is an error. To filter by news type/topic (e.g. "only product-related news"), call news_types() first to find the matching tag code(s), then pass them as type_codes. To filter by industry/sector, resolve codes with industry_search and pass them as industry — don't fall back to query for company or industry topics. Default to the compact list (full_enrichment=False) — it's enough to read and triage results. Don't set full_enrichment=True unless the user explicitly needs every field on every article. This list never includes full article text (kept compact). Every result carries an id; pass those ids to news_detail to retrieve the complete article body for the ones you care about. API: GET /news — akta-pro's News Signals API, documented at https://docs.akta.pro/api-reference/news-signals (every filter, the article schema, and the published code lists for the NAICS / SIC / IPTC / IAB / country / news-type filters above).
news_types
ChatGPTList the news-type taxonomy (tag codes) for filtering news_signals. Call this FIRST whenever the user wants a specific kind of news — e.g. "product-related", "M&A", "layoffs", "funding". Map their intent to the matching code(s) below, then pass them to news_signals(type_codes=[…]). Free, no credits. Codes are grouped by category (e.g. product launches = SD01, M&A = CM03, layoffs = WT02).
product_reviews
ChatGPTProduct catalog and per-product reviews (G2 et al.). Call once without products to get the catalog + product ids, then again with those ids to fetch reviews. 1.5 credits per 50 reviews. Prerequisite: company must be an akta-pro uuid from company_search (free). Names and websites are rejected.
social_posts
ChatGPTCompany social media posts: content type, text, publish date, paid/repost flags, an AI classification, and engagement metrics. 1 credits. Prerequisite: company must be an akta-pro uuid from company_search (free). Names and websites are rejected.
website_traffic
ChatGPTWebsite traffic: engagement metrics, monthly visit estimates, and traffic by acquisition channel. 1.5 credits. Prerequisite: company must be an akta-pro uuid from company_search (free). Names and websites are rejected.
