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, Conversion, and QA journey audits.

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). QA Audit scans include a compact qaAudit summary with confidence, journey-step count, pages reviewed versus page budget, blocked/needs-review step counts, auth-profile status, and the reason fewer than the page budget were captured when the safe same-origin link graph is exhausted.

{
  "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. For QA Audit scans, get_scan also returns a qaAudit block containing the product-flow synthesis, journey replay, safe blocked actions, confidence, authenticated-route context, and page-budget coverage.

{
  "scan_id": "clxxxxxxxxxxxxxxx"
}

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Available Resources

pagelensai://scan/{id}/markdown

Fetch the same agent-flavoured Markdown report available from the PageLens UI. For QA Audit scans, this includes front matter such as qa_journey_event_count, qa_confidence, and qa_needs_review_step_count, followed by the application interpretation, journey replay, blocked/risky paths, safe actions, and next QA tests.

Use this when an agent needs rich context to reason about a QA Audit:

pagelensai://scan/clxxxxxxxxxxxxxxx/markdown

pagelensai://scan/{id}/summary.json

Fetch compact JSON for a scan. For QA Audit scans, the qaAudit object includes journey metadata, synthesis, page-budget coverage, and any queue_exhausted reason explaining why fewer than the purchased page count were discoverable.

pagelensai://scan/clxxxxxxxxxxxxxxx/summary.json

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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
QA Audit	Journey replay, safe form/input exploration, blocked risky actions, app-flow synthesis, page-budget coverage

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

QA Audit scans are different from standard technical scans: the primary artifact is the agentic journey report. PageLens will attempt to review the purchased page budget (for example, up to 10 pages on QA Audit) and should only return fewer pages when the safe same-origin link graph is exhausted. It can use validated auth profiles for scoped post-login routes, while still avoiding SSO, CAPTCHA, MFA, signup, payment, destructive changes, and other committing actions.

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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."

QA Audit review:

"Fetch the latest QA Audit for example.com, read the markdown resource, and tell me which user journeys were verified, which actions were blocked safely, and whether PageLens reviewed the full page budget."

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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
QA Audit	$10	Up to 10 pages · agentic journey review
Professional	$15	25 pages
Monitor	$5 / month	Weekly automated scans · 5 pages

Every technical scan tier produces the same full report — Starter through Professional differ only by page-count cap, not report depth. QA Audit produces a journey-first report focused on application flow, safe exploration, design/UX critique, and next QA tests. 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