How to Use This Tool

1. Enter your main topic. Use the phrase you want the page to be known for — “espresso machine,” not “coffee stuff.” The more specific and common the phrase, the richer the comparison.
2. Add your content. Paste the body text directly, or — if the optional URL fetch endpoint is installed — paste the page’s URL instead.
3. Click “Map my entity gaps.” The tool resolves your topic on Wikidata, pulls its known relationships, and checks each one against your text. This takes a few seconds.
4. Read your Topical Completeness Score. It’s the share of Wikidata’s expected entities that your content already mentions.
5. Work the gap list. Each missing entity is ranked by how central it is to the topic. Start at the top — those are the omissions most likely to make your content read as thin to both readers and AI systems.
6. Re-run after you edit. There’s no limit and nothing to install to check again — confirm the score actually moved before you consider a gap closed.

What Entities Are — and Why Coverage Beats Repetition

Entities are the concrete things a topic is made of: the parts, the related products, the adjacent processes, the people and organizations tied to it. Search engines and AI systems increasingly reason about content at this level, not at the level of word frequency — a page that mentions the right constellation of related entities reads as authoritative even before anyone evaluates its prose.

The core difference: keyword density measures repetition of a phrase you already chose. Entity coverage measures whether you covered the concepts an expert on the topic would expect to see — including ones you might not have thought to include.

Why Entity Coverage Matters for Both Search and AI Answers

Traditional search ranking has used entities for years — Google’s Knowledge Graph exists specifically to understand pages as networks of connected things, not bags of words. What’s changed is that generative AI systems build answers the same way: they assemble a response from the entities and relationships a page demonstrates it understands, then decide whether that page is worth citing.

A page that’s topically thin — correct, but narrow — gives an AI system little to work with beyond the exact sentence it’s already written. A page that surrounds its core answer with the entities a knowledgeable source would naturally mention gives the AI system more surface area to draw from, and more reasons to treat the page as a credible source rather than a single fact to extract and discard.

How to Interpret Your Topical Completeness Score

A low score is not a verdict on writing quality — a well-written page can still be topically narrow. It’s a map of what to add, not a grade on what’s already there.

The Priority Order for Closing Entity Gaps

Not every missing entity deserves the same attention. Work in this order:

Why We Use Wikidata Instead of an AI Model

Most tools in this space ask an AI model what a topic “should” cover, which means the answer is only as good as that model’s training data and is different every time you ask. We query Wikidata directly instead — a structured, versioned, publicly auditable dataset that already defines how concepts relate to each other.

• The comparison is reproducible. Ask the same question twice and get the same relationships back, because you’re querying a database, not generating a probabilistic answer.
• There’s no per-use cost and no account to manage. Wikidata’s public API and query service are free and open, which is also why this tool has no usage limits, no login, and nothing to configure before your first check.

What This Tool Doesn’t Measure

In the interest of being useful rather than impressive-sounding:

• It doesn’t check writing quality. A page can mention every expected entity and still be poorly written, or vice versa.
• It doesn’t measure domain authority, backlinks, or ranking history. This is a content-structure signal, not a full SEO or GEO audit.
• Matching is text-based, not semantic. If your content discusses a concept using only a synonym or different phrasing than Wikidata’s label, it may show as missing even though a human reader would recognize it as covered.
• Coverage depends on how well-documented the topic is on Wikidata. Extremely niche, brand-new, or hyper-local topics may return few related entities — the tool will tell you when that happens rather than fabricating a result.
• A high score doesn’t guarantee an AI citation on its own. Entity coverage is one input into how thorough and credible a page reads. It doesn’t account for competing content, domain trust, or how closely your phrasing matches a given query.

Using This Tool Alongside the Rest of Your Content Workflow

This checker answers one specific question: does this page cover the concepts the topic requires? It’s most useful as one step in a short sequence:

1. Draft the content around your core topic and keyword.
2. Run the Entity Gap Mapper to find concrete, missing related concepts.
3. Add the highest-priority gaps, with real explanatory context, not passing mentions.
4. Re-run the mapper to confirm the score improved.
5. Once entity coverage is solid, move on to checking whether individual paragraphs are structured so an AI system can actually retrieve and quote them using the AEO Readiness Checker — coverage and extractability are two different problems, and both matter.