sitepulsar

Name: sitepulsar
Author: sitepulsar

Agent Experience Optimization (AEO) audits over MCP — score how findable, readable, and usable your site is to AI agents across FIND/READ/USE, then get…

От сообществаРаботаетБез входаГлобальныйБесплатноТолько чтение

Что умеет

Check Agent Readiness: Fast AEO / agent-readiness read of a URL (robots, schema, content structure). Synchronous.
Run Audit: Run a full AEO audit of a URL. Async: returns an audit_id to poll with get_audit. Accepts an optional target_keyword.
Get Audit: 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

Какие данные видит

Нужен ли аккаунт

Не нужен: сервер работает без входа

Agent Experience Optimization (AEO) audits over MCP — score how findable, readable, and usable your site is to AI agents across FIND/READ/USE, then get prioritized machine-actionable fixes.

Список инструментов сервера (13)

Технические названия из tools/list. Нужны только разработчикам.

check_agent_readiness	Fast AEO / agent-readiness read of a URL (robots, schema, content structure). Synchronous.
run_audit	Run a full AEO audit of a URL. Async: returns an audit_id to poll with get_audit. Accepts an optional target_keyword.
get_audit	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. Lead with this; call get_audit_detail only when you need the full per-section breakdown.
get_audit_detail	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). Typed, sanitized, size-capped (see truncated/dropped_sections). Composite *_score fields listed in experimental_fields may change methodology — do not hardcode thresholds. Also surfaces author/E-E-A-T, content freshness, images, hreflang, per-page per-bot access, Schema.org Action microformats, OpenAPI sub-metrics, Google Intelligence, product readability, site maturity, and a methodology block — each tagged with an availability state in the `availability` map (present \| not_detected \| not_run_free_tier \| phase_c_disabled \| probe_failed \| truncated \| not_measured_legacy). Wave C adds deterministic signals: homepage content quality (named quotes, stats-with-source, answer-shape) under crawl.content_signals; per-page video + per-locale schema in page_signals; OpenAPI per-operation coverage %, OAuth scopes, and MCP tool annotations in agentic_detail.use_probes; and self-disclosed trust claims (certifications, SLA/uptime, AI-content disclosure, verifiable-claims) under agentic_detail.trust_claims — each labeled "disclosed"/"mentioned" (never "verified") with an evidence URL and extraction-confidence. All carry an availability state in the `availability` map.
compare_aeo	Compare AEO posture across multiple URLs. Async: returns an audit_id to poll with get_audit.
get_fixes	Return prioritized, pillar-tagged (FIND / READ / USE) fixes for a completed audit.
get_audit_full	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. Use `expand` to trim the payload ('summary' \| 'detail' \| 'fixes' \| 'all'; default 'all'). Same ownership, tier gating, and sanitization as those tools. For an in-progress audit it returns the status so you can keep polling. The detail layer includes Wave-B surfaced sections (author/E-E-A-T, freshness, images, hreflang, per-bot access, Action microformats, OpenAPI sub-metrics, Google Intelligence, product readability, site maturity, methodology) and Wave-C deterministic signals (content quality, video/locale, USE sub-metrics, trust claims) each with an availability state.
search_companies	Samples the major AI engines for which companies they name for a query; returns a consensus shortlist (≤5). Use to see who agents recommend — not where a brand is mentioned (scan_visibility). Result: { companies[], tool_schema_version }.
probe_agent_discovery	Checks selected registries (official MCP registry, PyPI, GitHub) for packages/servers tied to a domain or brand. A discovery-surface check, not a visibility check. Result: { state, score, tier, hits[], tool_schema_version }.
probe_ucp_readiness	Inspects /.well-known/ucp to report whether AI shopping agents can transact with the site (presence + advertised capabilities only). Result: { has_ucp_profile, capabilities[], score, tool_schema_version }.
probe_mcp_functional	Discovers a site's advertised MCP endpoint (mcp.json / .well-known) and inspects its declared OAuth/transport posture (advertised, not guaranteed-working). Result: { handshake_ok, declared_endpoint, declared_tool_names[], score, tool_schema_version }.
scan_product_page	Deterministically scores one product page (schema, price, availability, image) 0–100 for shopping-agent readability. No LLM. Pro+. Result: { result: { readability_score, ... }, tool_schema_version }.
scan_visibility	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). Pro+ (LLM cost). Result: { reputation[], tool_schema_version }.