JPKCom DB

SEO & GEO Analyzer — Examples

Hands-on walkthroughs with the SEO & GEO Analyzer: audit a page, read and fix a weak score, validate structured data, and check AI readiness.

Back to the overview: SEO & GEO Analyzer · Open the tool: www.jpkc.com/tools/seo/

The manual explains every tab and every score weight in detail. This page complements it with concrete workflows: typical tasks played out step by step, focused on how you read the result and what you do next. The tool's interface is in English, so tab and button names appear here exactly as you'll see them in the live tool.

Example 1: First full audit of a real page

The classic — you want to know where a page stands at all.

  1. Open the analyzer, paste an absolute URL into the input field (a product or article page, not just the bare domain) and click Analyze.
  2. You land in the Overview tab automatically. At the top you see the two gauges side by side: the SEO Score on the left, the GEO Score on the right, both on a 0–100 scale. Next to them sit the hard numbers: HTTP version, page size, TTFB, redirect count, and the key on-page values (title, description, H1, structured data).
  3. Read the colour first, then the number. The gauge turns green at 80, yellow at 50, red below — plus a letter grade from A to F. A value like SEO 67 / grade C means: solid foundation, clear room to improve. What matters isn't the single number but which checks are costing you points.
  4. For that, look at the Issues & Warnings block (also in Overview) — it collects the detected problems in one list. To prioritise, switch to the SEO Score tab: there the score is broken down by category, every individual check carrying a pass, partial or fail status and its point value.
  5. Work top-down by biggest lever. A missing Meta Description (70–160 chars) costs up to 8 points, a missing og:image 3 — sorting by points tells you what's worth doing first.

So a mid-range score isn't a verdict, it's a to-do list. You work through exactly that list in the next examples.

Example 2: Lifting a weak SEO score on purpose

Say the SEO Score sits at yellow and the SEO Score tab flags three checks as fail or partial. Take them in order:

  • Meta Description (70–160 chars) is fail. The page has no description; the Meta Tags tab shows the field empty. Write a 70–160 character description, ship it — and you earn the full 8 points. Any non-empty description outside the 70–160 character range earns 4 points (partial), but full marks only come inside the target range.
  • Single H1 Heading is partial. In the Meta Tags tab (Headings section) you see two <h1> elements. More than one H1 earns only 2 points instead of 5. Turn the second into an <h2> — that often clears the Heading Hierarchy check at the same time, if no level is skipped any more as a result.
  • Security Headers ≥ 4 is partial. The HTTP Headers tab tells you how many of the eight checked headers are set (e.g. "2/8"). The set is HSTS, CSP, X-Frame-Options, X-Content-Type-Options, Referrer-Policy, Permissions-Policy, Cross-Origin-Opener-Policy and Cross-Origin-Resource-Policy. Add at least four of them on the server and the check jumps from 1 to 3 points.

Then comes the most important step: analyze the page again. Paste the same URL once more and click Analyze — the gauge moves up, and you see in black and white that the fixes landed. (Example 6 shows how to compare before and after cleanly.)

Example 3: AI-readiness check — can an LLM cite the page?

The GEO Score answers whether generative systems (ChatGPT, Claude, Perplexity, Google AI) can capture and cite your page well. Here's how:

  1. After the run, switch to the GEO Score tab. It's organised into four categories: Structured Data, AI Discoverability, Content Structure and Machine Readability. Note: one check (Content Signals Declared) always shows as a pure info note with no effect on the score; a second one (Markdown Content Negotiation) appears only in Expert Mode.
  2. Look at AI Discoverability first. The AI Crawlers Allowed check matters here: it tests the robots.txt against nine named bots — GPTBot, ChatGPT-User, OAI-SearchBot, Google-Extended, Claude-Web, ClaudeBot, anthropic-ai, PerplexityBot and CCBot. Block them and no AI can read you. "Good" here means: 0 blocked → the full 3 points.
  3. Check llms.txt Present and llms.txt Valid Structure. No file means both are missing. "Good" looks like this: a reachable, non-empty and structurally sound llms.txt (5 points together). A present but malformed file earns only partial credit.
  4. Under Structured Data the checks are JSON-LD Present, Schema Variety, FAQ Schema and Author / Organization Schema. The last is your E-E-A-T signal: if your JSON-LD marks up a Person/Organization or an author field, the AI knows who to attribute the statement to.

What to do when it's red? That's exactly what the matching generators are for:

  • llms.txt Generator — produces a structurally valid llms.txt that turns both llms.txt checks green directly.
  • Meta-Tags Generator — delivers clean Open Graph and Twitter data that counts in both the SEO and GEO scores.

Run the page through the analyzer again afterwards and you can read the GEO-score jump directly.

Example 4: Validating structured data and rich-result readiness

You want to know whether your markup is good enough for rich results and AI citations:

  1. After the run, open the Structured Data tab. It lists every JSON-LD block found, with format, type and data.
  2. Check which @type values were detected. The analyzer collects types recursively. For the GEO check Schema Variety: ≥ 3 distinct types → the full 3 points, 2 → 2, 1 → 1. A page with only WebPage leaves potential on the table — add e.g. BreadcrumbList, Article and Organization and the value climbs.
  3. Watch for two signals in particular: a FAQPage block (the FAQ Schema check, 3 points — and a direct lever for FAQ rich results) and an author/organization schema (Author / Organization Schema, 3 points). Both appear in the tab as detected blocks when they're built in correctly.
  4. If the tab does not find types you expected, it's usually invalid JSON-LD. Look up the relevant spot in the Source Code tab (raw HTML) and fix it in the markup.

Example 5: Learning with the demo modes — no live page needed

You don't need a real URL to get to know the analyzer. The Export / Import tab holds three demo buttons that load ready-made example analyses (this tab works without a prior run):

  • Perfect (green) — a page where everything fits: HTTPS, full security headers including Content-Signal, valid SSL, rich meta/OG/Twitter/hreflang data, JSON-LD (WebPage + FAQPage), a well-formed llms.txt, a clean heading tree, 6 landmarks, readability 62.
  • Broken (red) — everything that can go wrong: HTTP only, a 3-hop redirect chain (301 → 302 → 200), title "Untitled", no description, duplicate H1 plus skipped levels, missing alt text, empty/spam links, robots.txt with Disallow: /, a malformed llms.txt, render-blocking resources.
  • Empty (grey) — a completely blank page, robots.txt and llms.txt both 404, all fields null/0.

This is how you learn most: load Perfect first, then Broken and compare the same tabs. In the SEO Score and GEO Score tabs you see, check by check, what makes the difference between green and red. The key point: the demo files contain only the raw data, no precomputed scores — scoring runs client-side on load, exactly like a live analysis. So you're not looking at a mock-up but at the real scoring logic on a known dataset.

Example 6: Comparing before and after cleanly

When you want to prove your fixes (to a client, say), you need a reproducible "before" state. The real mechanism for that is Export / Import:

  1. Capture the before. Analyze the page and export the result in the Export / Import tab as JSON. The format is { "version": "1.0", "tool": "jpkcom-seo-analyzer", "data": { … } } — only the raw data object is saved, never a frozen score.
  2. Apply the fixes. Work through the fail/partial checks (see examples 2 and 3) and deploy the changes.
  3. Analyze the after. Paste the same URL into the analyzer again and click Analyze.
  4. Compare. Re-import the old JSON file via Import any time — the scores are then recomputed from the stored raw data, exactly as on a live run. That gives you the old state and the new one side by side, so you can show: SEO from 67 to 88, GEO from 31 to 74.

Because export only ever stores raw data and never a precomputed score, the comparison stays fair — old and new files are scored by the same logic.

Going deeper: the overview for the big picture, the manual for every score weight in detail. You can try all of it directly in the tool.