GoodLeads

One record in full, by its Lead ID (e.g. `GL-CO-00042`). Use this when you already hold a Lead ID — from a file, a CRM, a receipt — and want everything we know about that business and its owner: the business, the primary contact, both scores, every attribute, and where each field came from. Args: glid: The Lead ID, e.g. `GL-CO-00042` — the `lead_ref` field on every record. Case-insensitive. Returns: The full lead detail dict, including a `_meta` provenance block (schema_version, freshness incl. this record's last update, source, score_versions, access_level). Raises ValueError if the id is not shaped like a Lead ID or no record matches.

How clean is the data a buyer would receive in a state — numbers, not adjectives. The scorecard grades the records a buyer would receive on mechanical conformance across four dimensions — format (state/phone/email/zip), completeness (a name for who filed it, an address present), consistency (names in CRM-ready Title Case, not ALL-CAPS), and standardization (how much of the state's raw status / entity-type vocabulary is mapped into the canonical cross-state values that `status` / `entity_type` filters match on — an unmapped row is one a canonical filter silently misses). It returns an overall 0–100 score, the per-dimension breakdown, and per-check pass rates with sample offenders you can click through. Use it to answer "how clean is the data we're se

Browse leads — rows for a shape or a saved list, or (`summary=True`) its counts, facets and price. Two ways to say which records, one contract underneath: * a saved list — `list_id`, the 8-char id in `#browse?list=<id>`. Its states, filters, sort and inactive-or-holding toggle are read from the list; pass nothing else about the shape. * an inline shape — `filters` plus `state` (one state) or `states` (several); omit both for every live state (CO, CT, FL, NY, TX, VA). `summary=True` returns the summary contract instead of rows — the same numbers the buying surface shows, from the same code path: `matching`, `sellable`, `verified_one`, `verified_both`, `no_channel`, `unnamed`, `facets`, `prices`, `quote` (present when `lane` or `cap` is g

Start here: the buyer's own words become a list we can count, price and sell. Give it what the buyer would type ("cleaning companies in Texas", "denver plumbers formed last 30 days with a phone", "NAICS 238220") and you get back a list shape in the one filter contract — `states`, `filters`, `sort`, `lane`, `cap` — with a one-sentence `readback` to show the buyer, `assumed` (every default and substitution, named), `unresolved` (the words it could not place) and up to three `alternatives`. It is the same interpreter behind the buy page's search box, so a person and an agent get the same list from the same words. It never answers in prose, never asks a question back, never looks a person up, and never emits a predicate on a masked field (`contact_name`

What this list costs before anyone pays: how many records name a person, and the price by grade. Pass a saved list (`list_id`, the 8-char id in `#browse?list=<id>`) or an inline shape (`filters` + `states`; omit `states` for every live state: CO, CT, FL, NY, TX, VA). You get the same numbers the buy page shows a person: `matching`, `sellable`, `verified_one`, `verified_both`, `no_channel`, `unnamed`, `facets`, `prices` (the live graded price rule + `price_rule_version`), `quote` (present when `lane` or `cap` is given), `exact`, `computed_at`, `quote_valid_until` (counts refresh tomorrow morning; the quote holds until then), `per_state`, and the `_meta` provenance block every read carries (schema_version, freshness, source, access_level). When a count is

Turn a quoted list into a payment link a person completes — the buyer gets the file within a minute of paying. Creating the link costs nothing and charges nobody — payment only happens if a human opens the returned `checkout_url` and completes it on Stripe's hosted page. Hand the URL to your human; do not represent the purchase as complete until they confirm payment. Nothing is charged until a person completes checkout; the file arrives about a minute after they pay; if we find a phone or email on the records after that, the updated file replaces it on the order's receipt page within a few hours and the receipt shows what was found and billed. A hard bounce, a disconnected phone or the wrong person is replaced within 30 days; what you buy is yours to re-download any time.

The filter contract, from the schema endpoint (`GET /api/v1/schema/attributes?include=grammar`): fields, grammar, or recipes. Call this before building `browse_leads` filters you haven't used before. Args: section: `fields` (default) — every one of the 77 filterable fields as `{"field", "label", "type", "operators", "sortable", "masked", "allowed_values"?, "description", "job", "absence", "synonyms"}`: `operators` is the ENFORCED set for that field (its type's row, or a narrower pseudo-field override), `sortable` flags the 71 fields `sort` accepts, `masked` flags `contact_name`, `email_primary`, `phone_primary` (redacted for keyless callers, who may not filter the summary on them), `allowe

Map YOUR word for a concept to this surface's fields — ask before concluding absence. Call this whenever a term you or your buyer uses ("vertical", "direct dial", "sole proprietor", "operating address", "decision maker") doesn't obviously match a field name. It answers in three shapes: `carried` names the exact fields and how to use them; `partially_carried` adds per-state availability with the reason a state is zero (zero by state design is not a data gap); `not_carried` explains why and names the nearest signal we do hold. Ambiguous terms return a clarifying question instead of a guess. Never conclude "this data is missing" from an empty filter result or an unmatched field name without calling this first — several concepts are carried under a dif