Free Meta Description Generator

How this tool computes its result

Same underlying generation function as the description generator: assembles one description from title, keyword, optional content snippet, and a CTA toggle, then scores it 0–100 based on length bands (120–160 optimal) and keyword presence. The content field is truncated with a hard `content.substring(0, 100)` cut — no word-boundary logic is applied to that snippet, unlike the final 155-character truncation of the assembled description, which does look for a trailing space.

Worked example

Title "AI Search Visibility Monitoring for SaaS Brands", keyword "AI search visibility" (three words), content "Track how ChatGPT, Gemini, and Perplexity mention your brand across thousands of real prompts every week.", CTA on (picks "Get started"). Because `titleWords.includes(keywordLower)` compares the whole 3-word keyword string against individual title tokens, it never matches, so the description opens with the raw keyword instead of the title: parts = ["AI search visibility", "Track how ChatGPT, Gemini, and Perplexity mention your brand across thousands of real prompts eve" (a hard 100-character cut, landing mid-word), "Get started today."] → joined description is roughly 144 characters, which falls in the 120–160 optimal band, so score = 100 with suggestions `["Perfect length for search results", "Primary keyword included", "Includes engaging action words"]` and no issues.

When not to use this tool

Produces exactly one description per click — for a batch of several distinct alternatives to compare, it must be re-run manually, and only the CTA sentence varies between runs (via `Math.random()` over 5 fixed phrases); the rest of the output is fully deterministic given the same inputs.

Common mistakes

• - Pasting a long content snippet expecting it to be summarized or trimmed at a sentence boundary — the code does a hard `substring(0, 100)` cut with only `.trim()` afterward, so the snippet frequently ends mid-word.
• - Expecting a multi-word keyword phrase to be woven into the title-based opening sentence — the code's `includes()` check on `titleWords` only succeeds for single-word keywords that appear as a standalone token, so multi-word keywords always fall back to using the raw keyword string as the opening sentence instead of the title.
• - Treating outputs as reproducible for QA diffing — identical title/keyword/content inputs can still yield a different final sentence between runs because of the random CTA pick.

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