AIScan - The AI Visibility Standard
AIScan API audits websites for AI visibility, generating readiness scores for agents, search, and MCP protocols.
Trust signal
4 of 4 pillars
coverage 100%
Pillars
Signals
Evidence tiers, not an endorsement · trust model v0.1.0 (provisional) · CDP-lane settlement only (D3) · Trust API coming soon
On-chain traction
ERC-8004 agent registry
Not found in the ERC-8004 agent registry. This is the default state — absence is not a negative signal.
Identity & classification
Settlement volume
USDC settled on-chain · monthly
as of Jul 12, 2026
Endpoints(19)
AIScan check_health: Site availability, robots.txt, AI crawler access (GPTBot, ClaudeBot, PerplexityBot), sitemap presence
AIScan check_agent_files: openapi.json, ai-plugin.json, x402 payment support detection
AIScan check_llms_txt: llms.txt existence and quality check: sections, links, statistics
AIScan check_schema: Schema.org JSON-LD detection: types found, FAQ schema, Organization schema
AIScan check_mcp: MCP discoverability: /.well-known/mcp.json, agent.json, MCP Server Card
AIScan score_aeo: Full AEO (AI Search Visibility) score 0-100 with detailed checks
AIScan score_agent: Full Agent Readiness score 0-100 with detailed checks
AIScan score_geo: Full GEO (Citation Readiness) score 0-100 with detailed checks
AIScan score_mcp: Full MCP Readiness score 0-100, the only scanner that checks this
AIScan generate_llms_txt: Generate a ready-to-deploy llms.txt tailored to the site
AIScan generate_mcp_json: Generate ready-to-deploy mcp.json + agent.json for the site
AIScan full_audit: Complete audit: all 4 scores (AEO, GEO, Agent, MCP), 80+ checks
AIScan generate_skill: Generate a ready-to-run SKILL.md fix file that Claude Code and other AI coding agents execute to apply every fix automatically
AIScan fix_pack: Copy-paste fix instructions for every failed check
AIScan compare: Gap analysis vs a competitor URL: who wins each category and why
AIScan visibility_fix_pack: Ready-to-deploy fix materials for weak visibility areas: citable passages, FAQ schema JSON-LD, llms.txt sections. Params: brand + niche + weak_spots array (from a prior visibility_check)
AIScan full_report: Everything: full audit + fix pack + generated llms.txt and mcp.json
AIScan visibility_check: Brand visibility in AI answers: AIScan runs 30 realistic niche prompts through a frontier AI model - mention rate, share of voice, who gets recommended instead. Params: brand + niche (no url needed)
AIScan visibility_vs_competitor: Head-to-head brand visibility in AI answers: 30 niche prompts, both brands measured on the same answers - mention rates, share of voice, verdict. Params: brand + competitor_brand + niche (no url needed)