mcp server

mcp

SitePulsar AEO audits: fetch FIND/READ/USE agent-readiness scores for any website.

Description as published by the maintainer. Source

• version 1.0.1
• active

active — Most recent push to the repository was 2026-06-10.

What this server can do

13 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.

check_agent_readiness(url)

Fast synchronous AEO / agent-readiness read of a single URL: robots and bot access, structured data (schema), and content structure. Returns immediate signals without running a full audit. Use this to triage a page or sanity-check before deciding whether the heavier run_audit is worth a credit. Required: url.

compare_aeo(urls)

Compare AEO posture across multiple URLs (e.g. a brand versus its competitors) on the same FIND/READ/USE pillar scale. Async: returns an audit_id to poll with get_audit. Spends credits only for freshly-audited URLs; recent audits are reused uncharged. Required: urls.

get_audit(audit_id)

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. Required: audit_id.

get_audit_detail(audit_id)

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. Required: audit_id.

get_audit_full(expand, audit_id)

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. Required: audit_id.

get_fixes(audit_id)

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. Use this when you want the to-do list to act on (or hand to a coding agent), rather than the scores or section detail. Required: audit_id.

probe_agent_discovery(url, brand)

Checks selected registries (official MCP registry, PyPI, GitHub) for packages/servers tied to a domain or brand. A discovery-surface check (can agents find your published tooling?), not a visibility check. Use when you want to know whether a brand has discoverable agent/developer artifacts listed where agents look for them. Result: { state, score, tier, hits[], tool_schema_version }. Required: url.

probe_mcp_functional(url, brand)

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). Use when checking whether a site exposes a connectable MCP server and what it claims to support. Result: { handshake_ok, declared_endpoint, declared_tool_names[], score, tool_schema_version }. Required: url.

probe_ucp_readiness(url, brand)

Inspects /.well-known/ucp to report whether AI shopping agents can transact with the site (presence + advertised capabilities only — never a live purchase). Use when evaluating an e-commerce or merchant site for agentic-commerce readiness. Result: { has_ucp_profile, capabilities[], score, tool_schema_version }. Required: url.

run_audit(url, target_keyword)

Run a full AEO audit of a URL covering FIND, READ, and USE. Async: returns an audit_id to poll with get_audit. Accepts an optional target_keyword. Spends one audit credit per fresh run; a same-URL re-run within 24h reuses the cached audit, uncharged. Required: url.

scan_product_page(url, brand)

Deterministically scores one product page (schema, price, availability, image) 0–100 for shopping-agent readability — no LLM, fully repeatable. Use when you want a precise, single-page readability score for a specific product URL rather than a whole-site audit. Available on Pro+ plans. Result: { result: { readability_score, ... }, tool_schema_version }. Required: url.

scan_visibility(url, brand)

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). Use when you want to know whether and how a named brand already surfaces in AI answers — complementary to search_companies, which finds who agents recommend for a category. Pro+ (LLM cost). Result: { reputation[], tool_schema_version }. Required: url.

search_companies(query)

Samples the major AI engines for which companies they name for a query (e.g. "best CRM for startups"); returns a consensus shortlist (≤5). Use when you want to know who agents *recommend* for a category — not where a specific brand is mentioned (use scan_visibility for that). Free, no URL needed. Result: { companies[], tool_schema_version }. Required: query.

Last successful function declaration observed on 2026-08-13. Source: https://mcp.sitepulsar.ai/mcp. We list what the server declared; we do not call any of these functions.

Endpoint status observed on 2026-08-13. Source: https://mcp.sitepulsar.ai/mcp.

Signals

These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.

Where to get it

• Repository — https://github.com/SitePulsar/mcp-server
• Website — https://www.sitepulsar.ai
• Remote endpoint (streamable-http) — https://mcp.sitepulsar.ai/mcp

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These share tags the maintainers applied themselves, such as aeo, ai-visibility, generative-engine-optimization, structured-data. Common tags like "mcp" or "ai" are ignored for this: agreeing with six hundred other projects is not a similarity.

This is not a recommendation and not a test result. It is a map of what the authors said their work is about.

How the author describes it

Topics the maintainer set on GitHub: aeo, agent-experience-optimization, agent-infrastructure, ai-agents, ai-visibility, anthropic, claude, generative-engine-optimization, llm, mcp, model-context-protocol, structured-data.