The Agent-Readiness score

The score is a single number from 0 to 100. It is computed by arithmetic, not by a language model, and the same inputs always produce the same output.

The formula

The score is penalty-based. Every brand starts at 100 and loses points for specific, named failures.

total = clamp(100 − (invisible_queries × 15) − (missing_signals × 10), 0, 100)

That is the whole formula. There are exactly two penalty terms.

Penalty	Points	Triggered by
Invisible query	15 each	A shopper question that engines answered, without naming the brand
Missing signal	10 each	A structured-data signal the brand's archetype expects, verified absent

The total is not an average of the sub-scores

Visibility, recommendation rate, and technical readiness are independent diagnostics. None of them feeds total. There is no weighted rubric and no four-dimension model — documentation or tooling that implies one is describing something this system does not do.

What counts as invisible

A query is penalized when engines answered it and a majority did not name the brand. Concretely: of the mention rows for that query text, invisible / total >= 0.5.

A query with zero observations is unobserved. It is unverified, not failed, and carries no penalty — though it still sits in the visibility denominator.

The three sub-scores

Each is reported alongside the total as a diagnostic. Each is a percentage.

Visibility

round(visible_queries / queries_attempted × 100)

The denominator is queries attempted, including ones no engine answered. An unobserved query therefore lands in the denominator and in neither numerator — it drags visibility down without costing the total any points. This is deliberate: it signals thin evidence without punishing the brand for it.

Recommendation rate

round(recommended_mentions / all_mentions × 100)

The share of all mention rows where the brand was actively recommended rather than merely named. Reported under a "diagnostic" heading in the report because it is a percentage of a variable-size denominator, not a 0–100 subscore.

Technical readiness

round(present_signals / verifiable_checks × 100)

The denominator is the verifiable subset of the archetype-applicable checks. Signals that could not be verified are excluded from both halves.

The signal checks

Four checks per archetype, each worth 10 points when verified absent.

Check	Looks for
`product_schema`	`Product` / `ProductGroup` JSON-LD
`price_availability`	Price and availability in structured data
`review_markup`	Review / aggregate-rating markup
`llms_txt`	An authored `llms.txt`

Check	Looks for
`organization_schema`	`Organization` JSON-LD
`service_schema`	`Service` JSON-LD
`faq_schema`	`FAQPage` JSON-LD
`llms_txt`	An authored `llms.txt`

Check	Looks for
`organization_schema`	`Organization` JSON-LD
`software_schema`	`SoftwareApplication` / `WebApplication` JSON-LD
`faq_schema`	`FAQPage` JSON-LD
`llms_txt`	An authored `llms.txt`

Scored on the ecommerce check set. This is deliberate — it is the pre-archetype default, so an unclassifiable site is measured exactly as the system measured every site before archetypes existed.

`llms_txt` means authored, not served

A platform-generated llms.txt — the file some hosts emit by default — does not count. The check classifies the file and returns authored: false for a generated one.

review_markup is dropped from the applicable set unless the crawl found strong storefront evidence, so a brand with no cart is not penalized for missing review markup on products it does not sell.

Evidence gates

Before a score is issued at all, two coverage gates must pass.

Gate	Threshold	Meaning
`coverage`	≥ 0.60	Share of attempted engine calls that returned a usable, judgeable response
`site_coverage`	≥ 0.50	Share of archetype checks that could actually be verified

Failing either sets evidence_ok: false and populates blockers with plain-language reasons. Consumers must refuse to display the score when evidence_ok is false. The MCP tools enforce this themselves: withheld runs are split out of get_score_history and excluded from compare_brands rankings.

Versioning and comparability

score_version is stamped onto every score. Two runs are only comparable when the scoring method and the question set both match — see Measurement reliability for the guards and the noise floor.