Scorecard methodology
The scanner loads your website the same way an AI agent would and discovers every WebMCP tool it exposes. It then evaluates the implementation across two dimensions:
The overall grade is a weighted blend of three scoring parameters.
Final grade weighting
Hover a parameter to see how it's scored
An agent reviews your tool surface, judging whether the tools are useful, clearly named, well described, and easy to call. Scored on a strict 1–5 scale.
How much of your site is exposed to agents. Great implementations enable complete user journeys, not just the homepage.
The mechanical hygiene of each tool — the things that make a tool callable without guesswork. Averaged across every tool we find.
snake_case nameEach score maps to a letter grade.
| Grade | Tier | Stars | What it means |
|---|---|---|---|
| A+ | Exceptional | ★★★★★ | Best-in-class agent surface |
| A | Excellent | ★★★★★ | Strong, minor gaps only |
| A− | Very good | ★★★★☆ | Solid surface, a few fixes away |
| B+ | Good | ★★★★☆ | Usable, real room to improve |
| B | Solid | ★★★☆☆ | Works, but gaps an agent will feel |
| B− | Needs work | ★★★☆☆ | Thin or loosely specified |
| C | Early | ★★☆☆☆ | WebMCP detected — the starting line |
A blank grade means one of these results:
document.modelContext, navigator.modelContext, and declarative tool elements.