Landing Page Review
Analyze a landing page for conversion optimization — messaging, layout, trust signals, and CTA effectiveness.
Usage
/landing-page-review example.com/pricing — review the given page
Works with
The review runs on the public page with zero accounts. Connected analytics turn opinions into measured problems.
| Job | Primary | Also answers it | No account |
|---|---|---|---|
| Conversion rate of this page, by device and source | GA4 (tool_run_report) | Plausible (breakdown, query) | — (score from heuristics) |
| Where visitors get stuck (scroll, rage/dead clicks, quickbacks) | Microsoft Clarity (get_live_insights) | — | — |
| What was already A/B tested here | GrowthBook (list_experiments, get_experiment_results) | — | Ask the user |
If GA4 and Plausible are both connected, show both rates side by side; they count sessions and visitors differently.
Steps
1. Fetch and analyze the page
Use WebFetch to load the URL. Extract:
- Headline and subheadline text
- CTA buttons (text, placement, count)
- Value proposition clarity
- Above-the-fold content
- Page structure (sections, flow)
2. Evaluate messaging
Score each element:
- Headline clarity (1-10): Does it immediately communicate what you get?
- Value prop specificity (1-10): Numbers > vague claims
- CTA strength (1-10): Action-oriented, specific, visible
- Social proof (1-10): Testimonials, logos, stats
- Urgency/scarcity (1-10): Reason to act now
3. Check trust signals
Look for:
- Customer logos or testimonials
- Security badges / certifications
- Pricing transparency
- Contact information
- Privacy policy / terms links
- Case studies or results
4. Analyze form/CTA friction
If there's a form:
- Number of fields (fewer = higher conversion)
- Field labels and placeholder text
- Submit button text (specific > generic)
- Error handling
- Privacy reassurance near form
5. Mobile assessment
From the fetched HTML you can check:
- Mobile viewport meta tag (
curl -sL <url> | grep -i 'name="viewport"'; WebFetch can't see<head>) - Content readability on small screens (font sizes and line lengths in the CSS, if inline)
Touch target sizes and whether the CTA sits above the fold need a rendered page. WebFetch and curl can't measure them. Run the URL through PageSpeed Insights (https://pagespeed.web.dev, Lighthouse flags "Touch targets" / "tap targets"), or open it in a browser's device mode at 375px wide. If neither is possible, mark both "not measured (needs a browser)" instead of scoring them.
5a. Measure load time
Step 7 scores load speed, so measure it rather than guess:
bashcurl -sL -o /dev/null -w "ttfb=%{time_starttransfer}s total=%{time_total}s html=%{size_download}B redirects=%{num_redirects}\n" "<url>" # Rough page weight: HTML + the scripts, styles and images it references curl -sL "<url>" | grep -oE '(src|href)="[^"]+\.(js|css|png|jpe?g|webp|avif|svg)[^"]*"' | head -40
Fetch the largest few assets with the same -w "%{size_download}" call and sum them. Report "TTFB X s, HTML Y KB, ~Z KB total across N requests (curl, no JS execution)". Over 1 s TTFB or ~3 MB total weight costs load-speed points. For field data (real users), use /cwv-audit.
5b. Measured behavior (enhanced when connected)
If Cogny isn't connected, skip this and note what connecting adds: real conversion rates by device and source (GA4/Plausible), where visitors get stuck (Clarity), and what was already tested (GrowthBook).
Call mcp__cogny__list_integrations once and skip whatever isn't connected.
- GA4:
mcp__cogny__mcp__Google_Analytics_4__tool_run_reportwithproperty_id,date_ranges(last 28 days),dimensions: ["landingPagePlusQueryString", "deviceCategory", "sessionSourceMedium"],metrics: ["sessions", "engagementRate", "keyEvents", "sessionKeyEventRate"], filtered to the page path. Checktool_list_conversion_eventsso you know which key events count. - Plausible:
mcp__cogny__mcp__Plausible__breakdown(site_id,dimension: "visit:entry_page",metrics: ["visitors", "bounce_rate", "conversion_rate"],date_range: "30d"). Conversion rate needs a goal filter, e.g.filters: [["is", "event:goal", ["Signup"]]]. - Clarity: call
mcp__cogny__mcp__Microsoft_Clarity__get_api_quotafirst (10 requests/day), thenget_live_insightswithnum_of_days: 3,dimensions: ["URL", "Device"],metrics: ["Scroll Depth", "Rage Click Count", "Dead Click Count", "Quickback Click"]. Dead clicks on a non-link element or a scroll depth that ends above the CTA are direct evidence for a top-5 change. Heatmaps and recordings aren't in the API; tell the user where to look in Clarity. - GrowthBook:
mcp__cogny__mcp__GrowthBook__list_experiments, thenget_experiment_resultsfor any experiment on this page. Don't recommend a variant that already lost.
Mobile vs desktop gaps from this data take priority over heuristic scores in step 7.
6. Competitive comparison
WebSearch for 2-3 competitors and briefly compare:
- How do their landing pages differ?
- What trust signals do they use?
- How is their CTA different?
7. Report
Landing Page Review: [URL] Conversion Score: X/100 Messaging: X/30 - Headline: [score] — [specific feedback] - Value prop: [score] — [specific feedback] - CTA: [score] — [specific feedback] Trust: X/30 - Social proof: [score] - Credibility signals: [score] - Transparency: [score] UX: X/20 - Form friction: [score] - Mobile: [score] - Load speed: [score] — TTFB [X]s, ~[Y] KB, [N] requests (curl) Differentiation: X/20 - vs [Competitor 1]: [comparison] - vs [Competitor 2]: [comparison] Measured (if connected; name the source on every number): [If nothing is connected: "Measured: not connected. Connecting GA4/Plausible, Clarity or GrowthBook adds real conversion rates, friction data and past test results."] - Conversion rate: [X]% desktop / [Y]% mobile (GA4 or Plausible) - Clarity: [N] rage clicks, [N] dead clicks on [element], avg scroll depth [X]% - Already tested: [experiment, result] (GrowthBook) Top 5 Changes (in priority order): 1. [Specific change with expected impact] 2. ... 3. ... 4. ... 5. ...
