Multiples.vc

Compute distributional stats (count, p25, median, p75, mean, min, max) over a filtered set of deals. USE THIS for any "median / typical / average / p25 / p75 X" question about deals - NEVER compute medians from a list_tool sample, the answer will be wrong. - metric: "ev_revenue" / "ev_ebitda" (multiples) | "valuation" (raw post-money, USD M) | "raised" (round size, USD M). - deal_subtypes: filter by round stage (Series A, Seed, Strategic M&A, PE buyout, …) - exact match. - participant_names: filter to deals where a named buyer/investor participated ("Salesforce acquisitions", "rounds with a16z"). Use lead_only=true for "X-led" rounds. - participant_verticals / participant_themes / participant_geographies: filter by the buyer/investor company's own profile ("deals bought by European PE firms"). - bucket_by: "year" or "quarter" for evolution/trend questions ("how have Series A valuations moved", "SaaS M&A multiples 2022 vs 2024"). Server caps at the last 12 buckets - always pair with a sensible deal_date_from when bucketing. Returns one row per bucket (or one row total when not bucketed). NEVER returns a list of individual deals.

multiples_compute_deal_stats

Cross-sectional p25 / median / p75 / count for one metric across a peer group of 3-50 public companies, computed at each qualifying month then optionally thinned. The 'resolution' input controls density: - 'auto' (default): up to 12 evenly-spaced dated rows. Cheap. Use for first-pass answers. - 'monthly': every qualifying month within the window, no gaps. Use ONLY when the user explicitly asks for month-by-month / no skips / the full series. The result envelope tells you which mode ran (`series_mode`) and how to escalate (`escalation_hint`). Never invent explanations like "reporting periods" for sampled gaps - escalate to 'monthly' instead. For a single company's trajectory use get_time_series_stats; for one date use get_time_series_snapshot.

multiples_get_time_series_aggregate

Read up to 6 metrics for up to 25 companies at one specific date. Use for "what was Microsoft's EV/Revenue in Q4 2023", "cyber multiples as of Dec 2022 vs today" (two calls), "how did Snowflake look at IPO". For trajectory questions use get_time_series_stats; for peer-group medians over time use get_time_series_aggregate.

multiples_get_time_series_snapshot

Trajectory of one metric for specific public companies over a date window. Returns per-company scalars (start, latest, change %, min, max, median, observation count) plus a 'series' array. The 'resolution' input controls the series: - 'auto' (default): up to 12 evenly-spaced sampled points per company. Cheap. Use for first-pass answers where prose + scalars suffice. - 'monthly': every month within the window, no gaps. Use ONLY when the user explicitly asks for month-by-month / every month / no skips / the full series. Max 5 companies in this mode. The result envelope tells you which mode ran (`series_mode`) and how to escalate (`escalation_hint`). Never invent explanations like "reporting periods" for sampled gaps - escalate to 'monthly' instead. For peer-group medians use get_time_series_aggregate; for a single date use get_time_series_snapshot.

multiples_get_time_series_stats

Returns canonical filter values across taxonomies (verticals/themes/geographies/deal_subtypes/client_focus/revenue_model/company_type). Set filters in search tools are EXACT MATCH against these values. Returns hierarchical structure: rows have {taxonomy, value, kind: parent|leaf, parent?, children?}. When kind=parent, the underlying data is tagged at leaf level - pass parent + children together as filter values to match all child-tagged rows. OMIT `type` for ambiguous concepts to search across all taxonomies at once.

multiples_list_taxonomy_values

Ranks M&A acquirers by deal count over the FULL filtered set. USE THIS for "most acquisitive buyers in <X>", "top strategic acquirers", "most active PE buyers", "who has bought the most <X> companies" questions. ALWAYS set deal_subtypes when the user implies a buyer type: "strategic" / "corporates" → ["Strategic M&A"]; "PE" / "financial sponsors" / "sponsors" → ["PE buyout"]. Omitting deal_subtypes mixes strategics and PE funds together and is rarely what the user wants. Returns one row per buyer with deal_count, total disclosed transaction value, latest deal date, and buyer profile fields.

multiples_search_buyer_rankings

Look up companies by name (ilike contains). Returns id, name, short_name, plus `listed_in_public_dataset` + `public_company_name` + `ticker` + `exchange` when the company is in the public-comps dataset. ALWAYS call this FIRST for "tell me about <company>" or "info on <company>" questions - if `listed_in_public_dataset` is true, follow up with search_public_companies passing `companies:[id]` (the `id` from this result) to pull live multiples. Pass the id, not the name: names are not unique (two listed companies are both called "Zoom") and a name pulls every match.

multiples_search_companies_by_name

Search funding rounds (VC, growth, IPO, secondary - anything that isn't M&A). Numeric monetary inputs are USD millions. Returns curated rows: deal_id, deal_date, target_name, deal_subtype, deal_raised, deal_valuation, EV multiples, investors, lead_investors, target country/verticals.

multiples_search_funding_rounds

Ranks investors by funding-round participation count over the FULL filtered set. USE THIS for "most active VCs in X", "top investors in X", "biggest backers of X", "who has invested the most in X", "top lead investors in X" - any question that asks to rank investors. Set lead_only=true ONLY when the user explicitly says "lead investors" / "rounds led by" / "who led". Returns one row per investor with deal_count, lead_count (how many were leads), distinct_companies, total disclosed raise, latest deal date.

multiples_search_investor_rankings

Search M&A transactions. Use deal_subtypes:["Strategic M&A"] for strategic / corporate acquisitions, deal_subtypes:["PE buyout"] for PE buyouts / LBOs. Filters AND together. Numeric monetary inputs are USD millions. Returns curated rows: deal_id, deal_date, target/buyer names + countries + verticals, deal_valuation/revenue/ebitda, EV multiples. The buyers array lists ALL acquirers with id, name, country (ISO code), and current ownership. Use it when identifying buyers; buyer_name and other scalar buyer fields describe only the primary buyer.

multiples_search_ma_deals