Is CherryPy ready for AI agents?

Lighthouse builds the accessibility tree an agent would traverse and scores the page pass or fail. The denominator is only the pages the audit could evaluate: a notApplicable or an error is excluded rather than counted as a failure, and the fraction that could be evaluated is published beside it as the coverage.

n=1 · coverage 100.0% · margin ±53.00 pp.

The llms-txt audit passing: a file is served AND Lighthouse considers it valid. The denominator is every sampled page, so this is a fraction of the whole web and not of the sites that have a file. This is the figure the index uses, because a broken file does not guide an agent.

n=1 · coverage 100.0% · margin ±53.00 pp.

A count, not a percentage: how many sampled sites register WebMCP tools that an agent can call. It is the audit that defines the Act dimension — the other two WebMCP audits measure how good those tools are, not whether they exist.

n=1 · coverage 100.0% · margin ±53.00 pp.

Read from the robots.txt of the sampled sites: of the AI agents we track, how many are NOT disallowed. Gradual and not a yes or no — turning away one agent out of seventeen is not the same as turning away all of them. A site with no robots.txt blocks nobody, so it counts as fully reachable: that is a measurement, not a gap. This is the only figure that MULTIPLIES the index instead of adding to it, because it is a precondition: if an agent cannot fetch the page, nothing else about the page matters. One caveat we would rather state than hide: a blanket Disallow under User-agent: * is not counted, because the corpus-wide measurement cannot see it either, and we would rather be consistent than clever.

n=1 · coverage 100.0% · margin ±53.00 pp.