Explain article score
article__scoreRead-onlyIdempotent
Explain an article's content score: the per-slot breakdown, word/heading/paragraph counts against their competitor-derived targets, and every NLP term with its current vs. required usage. This is the tool that says WHY a generated article scores badly.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| article_id | Yes |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| score | Yes | computed = the SEO score of the current HTML: the sum of the six v7 components, rounded up, out of 100; overall = SEO and AI blended, which is what stored (articles.content_score) holds; model = the formula behind computed. | |
| terms | No | Each NLP term: missing (0 uses), low (under target), overuse (past the competitors' range) or ok. | |
| counts | Yes | Actual document counts against the competitor-derived targets. | |
| breakdown | Yes | Per-slot points earned vs. available, with the hint explaining the gap. | |
| article_id | Yes | ||
| terms_missing | Yes | ||
| target_keyword | No |