sitepulsar.ai

Fast synchronous AEO / agent-readiness read of a single URL: robots and bot access, structured data (schema), and content structure.

Run a full AEO audit of a URL covering FIND, READ, and USE.

Fetch an audit's status and, when complete, a compact decision-ready SUMMARY: AEO score (overall + FIND/READ/USE), a short summary, the weakest pillar, headline takeaways, and the top fixes.

Full structured per-section breakdown of a completed audit, on demand (only call after get_audit when you need depth): per-dimension FIND/READ/USE sub-scores, reputation across AI engines, competitor cluster (named for paid tiers), crawl/schema/robots findings, agentic-readiness + USE functional probes, and rendered-DOM analysis (paid).

Compare AEO posture across multiple URLs (e.g.

Return the prioritized, pillar-tagged (FIND / READ / USE) action plan for a completed audit, deduplicated across sources, with machine-actionable implementation steps included on fixes where available.

One call that returns a completed audit's SUMMARY, full per-section DETAIL, and deduplicated prioritized FIXES together — so you don't have to chain get_audit → get_audit_detail → get_fixes.

Samples the major AI engines for which companies they name for a query (e.g.

Checks selected registries (official MCP registry, PyPI, GitHub) for packages/servers tied to a domain or brand.

Inspects /.well-known/ucp to report whether AI shopping agents can transact with the site (presence + advertised capabilities only — never a live purchase).

Discovers a site's advertised MCP endpoint (mcp.json / .well-known) and inspects its *declared* OAuth/transport posture (advertised, not guaranteed-working — it does not run a full live handshake).

Deterministically scores one product page (schema, price, availability, image) 0–100 for shopping-agent readability — no LLM, fully repeatable.

Live AI-visibility scan for a brand: crawl + reputation sampled across AI engines, returning where *that* brand is mentioned (any public brand, not just your own).