Structure Harvester

Structure Harvester analyzes a webpage's information architecture by extracting structural elements such as headings (H1–H6), sections (section, article, aside), and landmarks. It returns a machine-readable representation of the page's organization that supports SEO, content strategy, IA, and developer workflows. Given a URL, it fetches the HTML, parses the DOM, and identifies hierarchical relationships between blocks, lines, and content groups. The output highlights the heading sequence, section depth, and distribution of main versus complementary regions; this enables audits, content planning, and accessibility reviews.
Conceptually, the tool normalizes tag names, builds a tree from the document outline, assigns a path for each node, and computes depth. It counts tag occurrences, estimates the main content region via heading order heuristics, and can map links to anchors or detect embedded metadata (JSON-LD, microdata). Outputs are structured for machine consumption and can be correlated with on-page keywords to assess semantic coverage. It is designed for repeatable analysis across sites or campaigns.
Benefits include faster content audits, improved information architecture decisions, and more accurate keyword mapping. Content teams, SEOs, and developers gain a repeatable method to identify orphaned sections, quantify structure quality, and compare pages. Unique value lies in producing a compact, render-light structural model suitable for downstream tooling. Core features include a hierarchical tree, tag counts, main-content heuristics, and export-ready data. Typical use cases are site-wide structure audits, campaign-page planning, and accessibility reviews.