GoodLeads

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• tools: 13 tools scanned
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Tools (13)

• find_lead_by_glid

  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). Includes `history`: every event the state has published about this business, newest first, each with `published_date`, `event` (plain words) and `effective_date` when it differs; for a business that changed form, `prior_entity_ref` and `prior_entity_formation_date` name the record it came from. The `entity` carries `filin

• data_quality_scorecard

  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; the address's own state agrees with its ZIP), 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 selling in {state}?" and to track data-quality work the way classification is tracked. The payload also carries a fifth, record-centric **covera

• browse_leads

  Browse leads — rows for a shape or a saved list, or (`summary=True`) its counts, facets and price. **Type it the way your buyer says it.** Filter values are understood, not matched literally: "residential", "cell", "CST", "Texas", "on fire", "Denver metro" and "working from home" all resolve (case, spacing, plurals and reviewed words), and every response echoes what was understood in `interpreted_values` (typed → stored). A value that matched nothing comes back with `value_hints` — the closest real values and, for a closed list, every allowed value. Read both before telling a human a count is zero or a field is missing. 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` (seve

• interpret_list

  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", "SIC 1711", "MCC 5812", "google category plumber") 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`, `email_primary`, `phone_primary`). Every classification system is an entry point — a seller who thinks in MCC, SIC, NAICS or Google ca

• quote_list

  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 zero by design the payload adds `zero_reasons` — a state that never publishes the value, or a channel asked of records too new to carry one yet: the morning after the state posts a filing

• list_starters

  Ready-made lists to start from: every live state × business type, with live counts and a starting price. Returns one document: `{"count": N, "starters": [...]}` — one entry per (state, business type): the display `label`, the exact `filters` the card opens with, the graded counts (`matching`, `sellable`, `verified_one`, `verified_both`) and `price_from_cents` (the name-and-address grade — the floor, not a flat price; the full price ladder comes from `quote_list`). Show these to a buyer who has not said what they want yet, then narrow with `interpret_list` or your own filters and price the result with `quote_list`. Counts come from live inventory, cached server-side for a few hours — never a stale copy from a marketing page.

• checkout_list

  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. Pass a saved list (`list_id`, `#browse?list=<id>`) or the inline shape `filters` + `states` (omit `states` for every live state: CO, CT, FL, NY, TX, VA), a `lane` (`all` / `best` / `conta

• list_filterable_fields

  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. Each field lists the `allowed_values` when its vocabulary is closed and its `accepted_words` — the buyer's own words that mean a stored value ("cell" → mobile, "CST" → the Central zones) — so you never have to learn our spellings; typed values are understood anyway. Args: section: `fields` (default) — every one of the 85 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 76 fields `sort` accepts, `masked` flags `contact_name`, `email_primary`, `phone_primary` (redacted for ke

• explain_concept

  Translate a classification code, or map YOUR word for a concept to this surface's fields — ask before concluding absence. **Codes — the crosswalk.** Every record carries NAICS, SIC, the Google Business category and the payment MCC, tied together by our own taxonomy. Give ANY one — `term` "MCC 5812", "SIC 1711", "NAICS 238220", "google category plumber" (or `system` = naics / sic / mcc / google_category plus `code`) — and get the other three back: the equivalents in each system, our industry, the ready-to-use `filters` for each system, and a live count (`total` is the whole crosswalk neighborhood; `total_exact` only industries that carry the code itself; each industry is marked `exact` or `related`). A code we do not carry directly WIDENS — to its parent group, then to the industries the crosswalk ties it to — and `widened` says which step it took; an empty answer means nothing relates to it, and names where to go next. A trade word works

• list_live_states

  Where we are live right now — the state codes, read from production, never a cached page. Call it before promising a buyer a state: a state not in this list is not live yet. Returns `[{"state": "CO"}, ...]` — codes only, no counts. For how many records a state holds, `quote_list` (or `browse_leads(summary=True)`) on that state returns the live graded counts.

• describe_surface

  What GoodLeads is, who buys it and how every record is built — call this to explain or vet us; to price a list, start with interpret_list. It is for anyone who wins by reaching a business owner first: to sell what a new owner needs now, to be the name they already know a year from now, to spot their own customer starting a business, or to build a product on every new business. Never rule your owner out from this description — pass what they sell to `interpret_list` and read the free count from `quote_list`. Leads with what the buyer gets and how to act on it, then the mechanics: which database this surface reads (production, or an explicitly opted-in local surface — provenance you can trust), the contract it upholds, and the tools available. The surface never silently answers from local data.

• list_products

  The shelf — ready-made business type × state lists, with live counts. Returns the same live document `list_starters` returns — `{"count": N, "starters": [...]}`, one entry per (state, business type) with its display `label`, the exact `filters` it opens with, the graded counts (`matching`, `sellable`, `verified_one`, `verified_both`) and `price_from_cents` (the name-and-address grade — the floor, not a flat price) — plus a `note`. The shelf is the starter lists: every count here is live, the price is quoted per record by `quote_list`, and the payment link comes from `checkout_list`. Every purchase is one-time; a buyer who wants new filings to keep coming sets up a standing order from a paid order's receipt, billed monthly for the records actually delivered. There are no fixed-price products and nothing here carries a `product_id`: pass a starter's `filters` to `quote_list` for the exact count and price, then to `checkout_list` for the link. name and

• create_checkout

  Mint a hosted Stripe Checkout link for a list you shaped — the same code as `checkout_list`. 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. For new work, use the dedicated buying journey: `interpret_list` (the buyer's words → a shape) or `list_starters` (ready-made lists with live counts) → `quote_list` (graded counts + the price) → `checkout_list` (this link). This tool keeps accepting a list for compatibility — the list path is the same code as `checkout_list`. What you can buy: a list — `list_id` (a saved list, `#browse?list=<id>`) or the inline shape `filters` + `states` (omit `states` for every live state: CO, CT, FL, NY, TX, VA), with a `lane` (`all` / `best` / `contact`) and an optional `cap` (`{"type": "count|budget