Scan a website for Agent Readiness using the ASO (Agent Signal Optimization) framework and return an Agent Readiness Report. Use this for a full site-level baseline, competitive audit, or before/after readiness measurement; use check_signal instead when you only need one named signal, and use get_fix_plan when you only need remediation steps. Runs 34 checks across discoverability (robots.txt, sitemap, llms.txt, DNS-AID, Link headers), content accessibility (markdown negotiation), bot access control (AI bot rules, Content Signals, Web Bot Auth), invocation (API catalog, OAuth discovery, OAuth protected resource, auth.md, MCP Server Card, Google A2A Agent Card, Agent Skills, WebMCP), commerce (x402, MPP, UCP, ACP, pricing), Google generative AI search basics, browser-agent UX, and identity/trust signals. Returns the ASO Score (0-100, formally the Agent Readiness Index), ASO maturity level (ASO-0 Invisible … ASO-5 Autonomous-Commerce-Ready), an agent-readiness verdict, per-pillar scores, per-check evidence, and prioritized recommendations.

Run one specific agent-readiness check against a site (e.g. 'a2a-agent-card', 'llms-txt', 'mcp-server-card', 'x402'). Use this for targeted validation after making a fix or when debugging one signal; use scan_site for the complete ASO Score and get_fix_plan for a prioritized remediation roadmap. Use list_checks first when you need valid check ids. Returns status, evidence, and a fix recommendation, and omits raw remote artifacts by default.

List the full catalog of supported ASO checks with id, name, category, description, and spec link. Use this before check_signal to discover valid check ids, to build UI filters, or to explain the scanner coverage; it does not scan a site or produce a score.

Scan a site and return a prioritized remediation plan: which signals to add first, the ASO Score points each fix is worth, and ready-to-paste artifact templates (robots.txt AI rules, llms.txt, agent.json, A2A agent-card.json, MCP server card, x402 manifest, pricing.json, security.txt, status endpoint). Use this when the user wants an implementation roadmap or copy-paste fixes; use scan_site when they need full evidence and per-check scoring, and use check_signal to verify one completed fix.

Return the ASO (Agent Signal Optimization) framework reference: the six signal pillars with point weights, the Agent Readiness Index maturity levels (ASO-0 through ASO-5), certification thresholds, and the scoring rubric. Use this for education, documentation, or explaining how scores are calculated; it does not fetch or scan a website. Source: https://agentsignaloptimization.com