PageLens AI

Connect PageLens AI reports to Claude Desktop, Cursor, Codex, and other MCP clients so your AI assistant can understand what failed, why it matters, and what to fix next.

AnMCP (Model Context Protocol)server that gives AI agents direct access toPageLens AI- the independent launch reviewer for AI-built websites. PageLens AI turns launch risk across UX, SEO, Performance, Accessibility, Security, Conversion, and QA journey audits into plain-English priorities and agent-ready fix context.

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

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

[mcp_servers.pagelens] url = "https://www.pagelensai.com/api/mcp"

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

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

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

{ "include_unverified": false }

List your most recent PageLens scans. Filter by status, domain, or date. Returns a slim summary per scan (id, URL, score, grade, severity counts, andlaunchVerdict). Scans may includelaunchContextwithbuilderPlatformandlaunchMomentso agents can frame fixes for the owner's workflow without changing evidence or severity. QA Audit scans include a compactqaAuditsummary 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

Read the full summary for a single scan: score, grade, owner-firstlaunchVerdict, severity counts, launch context, executive summary, top-5 highest-priority findings, and per-persona reviews. For QA Audit scans,get_scanalso returns aqaAuditblock containing the product-flow synthesis, journey replay, safe blocked actions, confidence, authenticated-route context, and page-budget coverage.

{ "scan_id": "clxxxxxxxxxxxxxxx" }

Fetch the same agent-flavoured Markdown report available from the PageLens UI. Standard reports include launch-context and verdict front matter such asai_builder,launch_moment,launch_verdict_status, andlaunch_verdictwhen captured, followed by anOwner launch verdictsection and an AI-builder priority prompt pack your agent can act on before it reaches the detailed evidence. For QA Audit scans, this includes front matter such asqa_journey_event_count,qa_confidence, andqa_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

Fetch compact JSON for a scan. Standard scans include the samelaunchVerdictobject returned bylist_scansandget_scan: status, label, headline, summary, and next step. For QA Audit scans, theqaAuditobject includes journey metadata, synthesis, page-budget coverage, and anyqueue_exhaustedreason explaining why fewer than the purchased page count were discoverable.

pagelensai://scan/clxxxxxxxxxxxxxxx/summary.json

Page through all findings for a scan. Filter by severity, category, persona, page URL, or rule ID. Useformat: "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

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

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

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

This doesnothide 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

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 byfinding_idwhen you do not have thedecision_id.

Each scan runs adeterministic rule engine + AI reviewer pipelineacross every page:

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.

"Read the latest PageLens Launch Pack report for example.com, list the top 3 owner-risk fixes, and turn each one into a patch plan I can apply in this repo."

"Open the latest PageLens report. If it was built with Codex or Cursor and the launch moment is Product Hunt, prioritise the fixes that make it safe to post publicly, then give me exact patches."

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

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

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

"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."

"I re-ran PageLens from the CLI/API after fixing the findings. Compare the latest scan to the previous one and tell me what improved, regressed, or still needs work."

MCP reads and works with report data through OAuth. To create a new scan from automation, use the PageLens CLI, API, GitHub Action, or deploy hooks with a Pro+ plan; then ask your MCP-connected assistant to inspect the completed report.

"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."

PageLens AI supports one-off launch reviews and ongoing plan automation.

MCP access is included with paid reports so an agent can work from the same evidence the owner sees. Continuous Health Watch monitoring, API keys, CLI scans, GitHub Actions, deploy hooks, competitors, alerts, and team/client seats are plan features:

There is no separate new-user Monitor product in the current pricing model. Health Watch is included with paid account plans; legacy monitor rows may still exist for existing customers.

- 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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