PageLens AI — MCP Server

AI agents can run senior-level website audits in seconds.

An MCP (Model Context Protocol) server that gives AI agents direct access to PageLens AI — automated website reviews covering UX, SEO, Performance, Accessibility, Security, and Conversion.

Plug it into Cursor, Claude, or any MCP-compatible client and your agent can read scan results, drill into findings, surface quick wins, and close the fix loop — all without leaving the IDE.

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MCP Endpoint

https://www.pagelensai.com/api/mcp

This is a remote MCP server — no local install required.

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Quick Start

Cursor

Add to your ~/.cursor/mcp.json (or workspace .cursor/mcp.json):

{
  "mcpServers": {
    "pagelens": {
      "url": "https://www.pagelensai.com/api/mcp"
    }
  }
}

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "pagelens": {
      "url": "https://www.pagelensai.com/api/mcp"
    }
  }
}

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Authentication

This server uses OAuth 2.0. Your MCP client will open a browser window to authorize with your PageLens account on first connect. No API keys to manage.

Scopes granted on authorization:

Scope	What it unlocks
read:scans	List and read your scan results
read:findings	Read findings and quick wins
write:feedback	Submit finding feedback and owner decisions

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Available Tools

whoami

Confirm which PageLens account this MCP session is operating on, plus granted OAuth scopes.

{}

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list_domains

List the domains you've verified ownership of, along with badge tier and the scan currently anchored to the public badge.

{
  "include_unverified": false
}

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list_scans

List your most recent PageLens scans. Filter by status, domain, or date. Returns a slim summary per scan (id, URL, score, grade, severity counts).

{
  "limit": 20,
  "status": "COMPLETE",
  "domain": "example.com",
  "since": "2026-01-01T00:00:00Z"
}

Status values: PENDING · RUNNING · COMPLETE · FAILED · CANCELLED

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get_scan

Read the full summary for a single scan: score, grade, severity counts, executive summary, top-5 highest-priority findings, and per-persona reviews.

{
  "scan_id": "clxxxxxxxxxxxxxxx"
}

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list_findings

Page through all findings for a scan. Filter by severity, category, persona, page URL, or rule ID. Use format: "full" to include descriptions, suggestions, and evidence.

{
  "scan_id": "clxxxxxxxxxxxxxxx",
  "severity": "HIGH",
  "category": "SECURITY",
  "format": "full",
  "limit": 50
}

Severity levels: CRITICAL · HIGH · MEDIUM · LOW · INFO

Categories: UX · SEO · PERFORMANCE · ACCESSIBILITY · SECURITY · CONTENT · HEADERS · DESIGN · ERROR

Personas: MARKETER · CRO · UX · ACCESSIBILITY · BRAND · EXECUTIVE · PERFORMANCE · SEO

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get_quick_wins

Return the top N quick-win findings — high impact, low-to-moderate effort — ranked by the same Impact × Effort scorer used in the PageLens dashboard. Optionally override the scan's preset to re-rank under a different lens.

{
  "scan_id": "clxxxxxxxxxxxxxxx",
  "limit": 5,
  "preset_override": "CONVERSION"
}

Presets: PRE_SALES · PRE_LAUNCH · CONVERSION · INVESTOR · BRAND_POLISH

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report_finding_feedback

Flag a finding as a false positive, wrong severity, wrong category, or not actionable. Requires a paragraph of reasoning and a concrete evidence snippet.

{
  "finding_id": "clxxxxxxxxxxxxxxx",
  "kind": "FALSE_POSITIVE",
  "reason": "The selector .pointer-events-auto matches 100+ utility classes, not a single offending element.",
  "evidence": "<div class=\"pointer-events-auto ...\"> — Tailwind utility, not an event-handler.",
  "proposed_severity": "LOW"
}

Feedback kinds: FALSE_POSITIVE · INCORRECT_SEVERITY · INCORRECT_CATEGORY · NOT_ACTIONABLE · OTHER

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acknowledge_finding_decision

Attach owner-controlled context to a finding when the issue is real, but reflects an intentional architecture, security, or product tradeoff.

This does not hide the finding, edit the report, or change the PageLens score. It records the rationale so the report can show that the owner has acknowledged the tradeoff, and future scans can recognise the same finding as previously acknowledged.

{
  "finding_id": "clxxxxxxxxxxxxxxx",
  "decision": "INTENTIONAL_TRADEOFF",
  "reason": "Next.js Cache Components and PPR currently prevent us from using per-request CSP nonces safely.",
  "evidence": "proxy.ts documents the CSP tradeoff: script-src uses 'unsafe-inline' because cached HTML shells cannot vary nonces per request.",
  "expires_at": "2026-10-01T00:00:00Z"
}

Decision kinds: ACKNOWLEDGED · ACCEPTED_RISK · INTENTIONAL_TRADEOFF · WONT_FIX_NOW

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clear_finding_decision

Clear a previously acknowledged decision so it stops appearing on current and future reports. The audit history is preserved.

{
  "decision_id": "clxxxxxxxxxxxxxxx",
  "reason": "We have migrated to a nonce-compatible rendering path."
}

You can also clear by finding_id when you do not have the decision_id.

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What PageLens Checks

Each scan runs a deterministic rule engine + AI reviewer pipeline across every page:

Area	Examples
SEO	Title/meta length, canonical URL, Open Graph, heading hierarchy
Performance	Core Web Vitals (LCP, CLS, INP), page weight, render-blocking resources, DOM size, third-party load
Security	HTTP→HTTPS redirect, exposed .env/.git files, SSL expiry, XSS surfaces, source maps, exposed secrets in page source
Accessibility	Focus-visible CSS, reduced-motion support, ARIA patterns
UX	Hero hierarchy, mobile menu patterns, CTA structure
Content	Placeholder text, stale copyright year, mixed-content references

Findings include severity, effort estimate, copy-pasteable evidence, and a one-line fix suggestion.

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Example Agent Workflows

Pre-launch audit in Cursor:

"Run PageLens on my staging site, list all CRITICAL and HIGH findings, and create GitHub issues for the top 5."

CRO review:

"Fetch the latest scan for example.com, get the CONVERSION quick wins, and summarise what to fix before the campaign launch."

Security sweep:

"List all SECURITY findings with severity HIGH or above from my last scan and show me the evidence for each."

Accepted architecture decision:

"For the CSP unsafe-inline finding, acknowledge this as an intentional Next.js/PPR tradeoff with the rationale from our security docs. Do not mark it as a false positive."

Post-fix validation:

"After I've fixed the findings, start a new scan and compare the score to the previous one."

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Pricing

PageLens is pay-per-scan — no subscription required for one-off audits.

Tier	Price	Pages per scan
Starter	$1	3 pages
Professional	$15	25 pages
Monitor	$5 / month	Weekly automated scans · 5 pages

Every tier produces the same full report — Starter through Professional differ only by page-count cap, not report depth. The Monitor subscription runs weekly scans automatically and surfaces drift between runs.

See full pricing →

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Links

• Website: https://www.pagelensai.com
• MCP endpoint: https://www.pagelensai.com/api/mcp
• Dashboard: https://www.pagelensai.com/dashboard
• GitHub: https://github.com/PageLens-AI/pagelensai-mcp-server
• MCP docs: https://www.pagelensai.com/mcp

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License

MIT © PageLens AI