Data quality scorecard
data_quality_scorecardRead-onlyIdempotent
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 **coverage** dimension
(the `coverage` block + `dimensions.coverage`): per pipeline stage, how
many records that brain has NEVER stamped (`gap`), plus a stale-version
count where the brain persists one. Free-chain checks are scored; paid
stages (skip trace / gap-fill / validation / LLM passes) are reported but
unscored — enrichment is spent per order, so an un-enriched set of records
is posture, not a defect. Coverage deliberately does not move the headline
`score`. Each check includes `browse_filters` (a `missing_stage` filter):
the exact set of records works on `browse_leads` and scopes a surgical repair run
on the pipeline trigger. Stages whose brains leave no per-record mark are
listed under `coverage.unmeasured` with reasons rather than pretended into
numbers.
Args:
state: Two-letter state code (e.g. `FL`, `CO`). Omit to get every state,
worst score first.
sample_limit: Max sample offenders to return per check (0–50, default 8).
Returns:
A scorecard dict for one state, or `{"states": [...]}` for all states.
Either shape carries a `_meta` provenance block (schema_version,
freshness, source, score_versions, access_level). Served from a cache
refreshed in the background: `computed_at` / `age_seconds` date it,
`stale: true` means the refresh is behind (report the numbers with their
age), `status: "computing"` means none exists yet. Never call it again for a fresher one.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| state | No | ||
| sample_limit | No |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||