What term-based optimizers get right
They catch genuine omissions. If every ranking page for a query discusses pricing, warranty and installation and your draft covers none of them, you have a coverage gap, and the tool found it faster than you would have.
Where the score misleads
Hitting term counts produces text that reads as assembled rather than written, and rewards padding: word-count targets derived from the average of ranking pages push you to inflate a page that would answer better in 800 words. Google's helpful-content systems specifically target that pattern.
What matters more in the AI era
Answer engines quote passages. A self-contained 40-60 word answer under a question-shaped heading, with one concrete number, gets cited. No term-density score measures that, which is why content graders built for AI answer inclusion score pages differently.
The alternatives by need
For pure editing help, a term-gap check inside your CMS is enough. For strategy, you want intent classification, SERP-format matching and internal-link recommendations. For AI visibility, you want citation tracking and entity-coverage analysis - a different measurement entirely.
What SEO Smart Engine does instead
It grades an existing page against the query it targets, generates a brief with the outline, required schema types, entity suggestions and citation targets, and recommends specific internal links with anchor text and a reason for each - then tracks whether AI engines start citing the page.