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Data quality scorecard

data_quality_scorecard
Read-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), 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. The all-states form evaluates the full book
        (~800k records, ~40s) — when you only need one state, pass it:
        per-state responses return in seconds.
    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).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stateNo
sample_limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, and the description is fully consistent with these. It adds substantial behavioral detail: coverage deliberately does not affect the headline score, paid enrichment stages are reported but unscored, unmeasured stages are listed with reasons, and check results include browse_filters for surgical repair runs. This goes well beyond what annotations provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but earns its length given the tool's subtle coverage semantics and non-obvious behavior around paid vs free stages. It front-loads the core purpose, then organizes details into Args and Returns sections. It could be tightened with bullets, but every sentence carries meaningful operational information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with only two parameters and an output schema, the description is complete. It explains the return shapes for single-state vs all-states calls, the _meta provenance block, per-check sample offenders, browse_filters usage, and the reasoning behind coverage not moving the score. An agent has everything needed to invoke the tool correctly and interpret its results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description carries the full burden. It fully documents both parameters: state is a two-letter code with the omit-to-get-all behavior, explicit performance implications, and default behavior (worst score first); sample_limit is given with its range (0–50) and default (8). This is excellent compensation for the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool's job: grading data quality for a state with a 0–100 score and per-dimension breakdown. It names the specific resource (records a buyer would receive) and the verb ('grades'), and provides the intended question it answers. However, it does not explicitly differentiate itself from siblings like browse_leads or list_filterable_fields, though the unique purpose is largely self-evident.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives practical usage guidance: use it to answer 'how clean is the data we're selling in {state}?' and to track quality work. It also clearly instructs when to pass a state versus omit it, including performance tradeoffs (~800k records/40s vs seconds). It does not explicitly mention when not to use this tool or name alternative tools, but the context is clear enough for an agent to select it correctly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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