Before a dollar of spend, GooseCabbage crawls your whole site — and every known competitor's — and grades each page on the job it's actually there to do: a homepage sells positioning, a pricing page makes cost legible, a sign-up page removes every reason not to convert.
Then it does something no other tool does: it scores how ready each page is for the AI agents that now browse and act on the web — structured data, semantic markup, and WebMCP tools included.
Click through a real audit: per-page grades with the exact positives, negatives, and CTAs the agent surfaces, a head-to-head matrix against the field, and a live AI-readiness score you can watch climb.
Publish a feature-by-plan matrix and collapse to one primary CTA — Rival A already does both.
Ad platforms grade your landing pages and quietly tax weak ones with higher costs. Meanwhile your competitors' pages are public — and a goldmine — but nobody has time to read them all. And a new audience has arrived: AI agents that browse, compare, and buy, and skip pages they can't parse.
Quality Score and ad rank fold in landing-page experience. A slow, vague, or untrusted page means you bid more for the same click — forever.
Your rivals tell you their positioning, offers, and CTAs on every page. Reading all of them by hand is impossible, so you fly blind into the same auction.
AI shopping and research agents skip pages without structured data, semantic markup, or actionable tools. Invisible to agents means invisible to a fast-growing slice of demand.
Each page is graded only on the dimensions that matter for its type — then on AI-friendliness, every time.
Message clarity, CTA strength, form simplicity, distraction-free flows, cost legibility — scored against what the page type is for, 1–10, with concrete positives and negatives.
Social proof, trust microcopy, guarantees, and risk flags that scare off high-intent buyers. The agent quotes the actual phrases on the page, not generic advice.
JSON-LD types, OpenGraph, meta descriptions, a sane heading outline, and semantic landmarks — the machine-readable layer that search engines and AI crawlers depend on.
A deterministic 0–100 readiness score: WebMCP tool registrations, labelled actionable forms, alt-text coverage, and HTTPS — how easily an AI agent can understand and act on the page.
WebMCP (Web Model Context Protocol — Google/Microsoft, W3C draft) lets a page expose tools an AI agent can call. We detect both imperative registrations and declarative form annotations, then score readiness deterministically — no LLM guesswork, no tokens burned, accurate today and as adoption grows.
We scan for navigator.modelContext.registerTool() and for <form toolname="…"> annotations, surfacing the actual tool names an agent could call on the page.
Readiness is computed from extracted HTML signals, weighted toward WebMCP but crediting the agent-readiness fundamentals it's built on — so the badge is meaningful before WebMCP adoption is universal.
JSON-LD, semantic landmarks, a single clean H1, labelled forms, alt-text, HTTPS — the same signals lift you with search engines and with agents at once.
Every page lands at none, partial, or full readiness with the exact detected signals listed — so you know precisely what to ship next.