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.

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:

curl -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_report with property_id, date_ranges (last 28 days), dimensions: ["landingPagePlusQueryString", "deviceCategory", "sessionSourceMedium"], metrics: ["sessions", "engagementRate", "keyEvents", "sessionKeyEventRate"], filtered to the page path. Check tool_list_conversion_events so 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_quota first (10 requests/day), then get_live_insights with num_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, then get_experiment_results for 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. ...