Analyze keyword density and frequency distribution in text. Part of the DevTools Surf developer suite. Browse more tools in the Web / Frontend collection.
Use Cases
Audit a blog post or landing page for keyword density before publishing.
Compare keyword distribution between two competing pages to identify coverage gaps.
Identify overused terms that should be replaced with synonyms to avoid over-optimization.
Check that a translated or paraphrased article maintains the same keyword profile as the original.
Tips
Aim for 1-2% keyword density for primary keywords — higher rates risk being flagged as keyword stuffing by search engines.
Analyze both exact-match and semantic variations (LSI keywords) — modern search algorithms weight topic coverage more than repetition of a single phrase.
Use the distribution chart to ensure keywords appear throughout the content, not just in the introduction.
Fun Facts
Google's Panda algorithm update (2011) was the first major algorithm change to demote pages with keyword stuffing. Pages went from #1 rankings to page 10 overnight, affecting 12% of US search queries.
The term 'keyword stuffing' was coined in SEO circles around 2003-2004, describing the practice of hiding dozens of keywords in white text on a white background to manipulate early search engines.
Moz research (2022) found that pages ranking in the top 3 positions for competitive keywords contain their target keyword in the title, H1, and first 100 words in 87% of cases — placement matters as much as density.
FAQ
What's a good keyword density?
1-2% for primary keywords is the commonly cited target. More important is natural language variation — search engines evaluate topical relevance, not just density.
Does it analyze meta descriptions and titles too?
Yes — paste the full page including title and meta description, or analyze the body copy separately. The tool reports keyword frequency across each section.
Does it detect LSI keywords?
It identifies semantically related terms that appear alongside your primary keyword, highlighting co-occurrence patterns that contribute to topical authority signals.
