Orientation call: how many companies, split by status/era/confidence/sector. Cheap. Use this first to understand what the dataset covers before querying.

Return the valid values for every filter accepted by find_channels (sector groups, business models, eras, statuses, trust/virality/CAC/category levels, stages, and the 24 canonical channels). Call this if unsure what to pass.

PRIMARY TOOL. Given a marketing situation, return the channels comparable Indian startups actually used — ranked by prevalence and split across growth stages (0→1, 1→10, scale), with named example companies as evidence.

Pass any subset of: sector (a group like 'Fintech' or a raw sector), business_model (B2C/B2B/D2C/SaaS/marketplace/B2B2C), era ('2010-2016', '2016-2019', '2020-2021', '2022-2025'), status, trust_burden (low/medium/high), virality (low/medium/high), cac_ceiling (low/medium/high/enterprise), category_play (creation/capture). Optionally set stage to rank by that stage.

Always report the returned cohort_size and honor any 'warning' about small cohorts.

Full channel profile for one company (fuzzy name match): channels by stage, what worked, what failed, economics, sponsorships, variables, confidence, sources.

Keyword search across the narrative evidence (what worked / what failed / signature moves / ignition). Use for 'when did X fail', 'who used Y for Z'. Returns short snippets with company names — not full rows.

Inverse view for one channel: how common it is, its stage split, which sectors use it most, and the companies behind it. Accepts a partial channel name.