AI SEO

Full AI-SEO audit of a single URL: returns categorized findings (info/warning/error) with severity, fix instructions, and a 0-100 composite score plus per-dimension subscores. Read-only. Fetches the URL once and runs every sub-audit (schema, robots, technical, sitemap, AI-Overview eligibility) against the response. No writes, no third-party APIs, no auth required, no rate limits beyond polite per

{
  "tool": "audit_page",
  "arguments": {
    "url": "…"
  }
}

Validate JSON-LD structured data against Schema.org rules and AI-citation best practices. Accepts either a URL (fetched) or a raw JSON string (parsed directly). Read-only when given `url` (one HTTP GET). Zero network when given `schema_json`. No writes. Deterministic, rule-based; no LLM. Validates required/recommended properties, @context correctness, sameAs links, and AI-search-friendly pattern

{
  "tool": "audit_schema",
  "arguments": {
    "url": "…",
    "schema_json": "…",
    "respect_robots": true
  }
}

Audit a page's canonical link integrity: presence, self-reference, cross-domain mismatches, trailing-slash hygiene, and og:url consistency. Read-only. One HTTP GET to fetch the HEAD section. Deterministic, rule-based; no LLM. When to use: a focused canonical-only audit (e.g. debugging a duplicate-content issue). For a full HEAD audit including OpenGraph, hreflang, noindex, title, use `check_tec

{
  "tool": "audit_canonical",
  "arguments": {
    "url": "…"
  }
}

Fetch and parse a domain's robots.txt; report per-crawler allow/disallow posture for every known AI training crawler (GPTBot, CCBot, Anthropic-AI, Google-Extended, etc.), AI search crawlers (ChatGPT-User, PerplexityBot, OAI-SearchBot), and user-triggered fetchers. Read-only. One HTTP GET to /robots.txt. No auth, no rate limits applied. Deterministic, rule-based; no LLM. Returns structured findin

{
  "tool": "check_robots",
  "arguments": {
    "domain": "…"
  }
}

Validate a domain's XML sitemap: presence, accessibility, URL count, lastmod freshness, sitemap-index handling, and image/video sitemap extensions. Read-only. Issues N+1 HTTP GETs: one for robots.txt + sitemap, then up to `max_urls_to_check` HEADs against sampled URLs. Deterministic, rule-based; no LLM. When to use: site-wide indexing audits. Pair with `check_robots` for a full pre-crawl pictur

{
  "tool": "check_sitemap",
  "arguments": {
    "domain": "…"
  }
}

Audit a page's HEAD section for technical signals relevant to AI crawlers: HTTPS, canonical, OpenGraph, Twitter Card, hreflang, noindex, and title-vs-H1 hygiene. Read-only. One HTTP GET, inspects HEAD only (body is not parsed). Deterministic, rule-based; no LLM. When to use: when you specifically need HEAD-tag audit findings. For the full page including schema and AI-Overview scoring, use `audi

{
  "tool": "check_technical",
  "arguments": {
    "url": "…"
  }
}

Score a page's probability of appearing in Google AI Overviews. Returns an overall 0-100 score plus six factor subscores: semantic completeness, structured data, E-E-A-T signals, entity density, freshness, and technical hygiene. Read-only. One HTTP GET. Deterministic, rule-based scoring derived from published 2025-2026 AI-Overview correlation studies. No LLM calls. Same URL returns the same scor

{
  "tool": "score_ai_overview_eligibility",
  "arguments": {
    "url": "…"
  }
}

Score a page against the four signals Google added to the Lighthouse "Agentic Browsing" category in May 2026: presence of an llms.txt, WebMCP integration, accessibility-tree integrity, and layout stability. Returns an overall 0-100 score, a letter grade, and a per-factor breakdown. Read-only. One HTTP GET for the page plus one for /llms.txt (skip with check_llms_txt=false). Pass `html` instead of

{
  "tool": "score_agentic_browsing",
  "arguments": {
    "url": "…",
    "html": "…",
    "respect_robots": true
  }
}

Generate a spec-compliant llms.txt (and optionally llms-full.txt) for a domain by reading its sitemap, sampling up to `max_pages` pages, and synthesizing a grouped, sectioned summary. Read-only. Issues one HTTP GET for the sitemap then one per sampled page. Deterministic; no LLM. Output is the file content as a string - this tool does NOT write to disk or upload anywhere. The caller is responsib

{
  "tool": "llms_txt_generate",
  "arguments": {
    "domain": "…"
  }
}

Generate a machine-readable /pricing.md for AI shopping/agent flows. Finds the site's pricing page (or uses `pricing_url`), extracts named tiers and price lines, and returns the file content as a string. Read-only. Issues a few HTTP GETs probing common pricing paths. Deterministic; no LLM. Does NOT write or upload — the caller hosts the file at `https://<domain>/pricing.md`. When to use: a SaaS/

{
  "tool": "pricing_generate",
  "arguments": {
    "domain": "…"
  }
}

Validate an existing llms.txt or llms-full.txt against the spec: structure, section ordering, link format, and (optionally) broken-link detection. Read-only. One HTTP GET when given `url`; zero network when given `content`. Optional link-check issues HEAD requests against each link if `check_links` is true. Deterministic; no LLM. When to use: auditing an llms.txt you already have. To generate o

{
  "tool": "llms_txt_validate",
  "arguments": {
    "url": "…",
    "content": "…",
    "check_links": true
  }
}

Score how citable a page or text block is for AI engines (ChatGPT, Claude, Perplexity, Google AI Overviews). Evaluates BLUF (bottom-line-up-front) opening, FAQ patterns, statistic density, entity clarity, and answer-shape fit for the optional `target_query`. Also returns `extractability_score` plus per-section `chunk_analysis`: how cleanly an LLM can lift a self-contained answer from each heading

{
  "tool": "score_citation_worthiness",
  "arguments": {
    "url": "…",
    "text": "…",
    "target_query": "…"
  }
}

Rewrite a content block for Answer Engine Optimization. Adds a BLUF opening, FAQ structure, schema additions, and concise question-shaped headings tuned for ChatGPT / Perplexity / Google AI Overviews. Read-only when given `url` (one HTTP GET). Zero network when given `text`. The tool does NOT write back to the URL - it only returns the rewritten content as a string. No side effects on the source.

{
  "tool": "rewrite_aeo",
  "arguments": {
    "target_query": "…"
  }
}

Rewrite a content block for Generative Engine Optimization: entity-rich, comparison-ready, synthesis-friendly. Tuned for surfaces that summarize across sources (Perplexity, Google AI Mode, Claude search). Read-only on input. Does NOT write back to the source URL - returns the rewritten content as a string. This tool delegates the actual rewrite to the calling LLM via MCP sampling - it does not c

{
  "tool": "rewrite_geo",
  "arguments": {
    "target_query": "…"
  }
}

Extract named entities, linked concepts, and sameAs graph nodes from a page's content and structured data. Combines body-text NER with JSON-LD `@type` / `sameAs` walking. Read-only when given `url` (one HTTP GET). Zero network when given `text`. Primary path: MCP sampling - the host LLM does the NER and returns typed entities with sameAs URIs. Fallback path: deterministic regex-based extractor w

{
  "tool": "extract_entities",
  "arguments": {
    "url": "…",
    "text": "…",
    "respect_robots": true
  }
}

Simulate `would an AI engine cite this page for this query?`. The host LLM role-plays the chosen engine (chatgpt / claude / perplexity / google_ai_overviews / any), reads the page content, and returns a cite/no-cite verdict with the verbatim excerpt it would surface plus ranked improvements. Read-only when given `url` (one HTTP GET). Zero network when given `text`. Primary path uses MCP sampling

{
  "tool": "score_test_citation",
  "arguments": {
    "target_query": "…"
  }
}

Compare two URLs for AI citation-worthiness and return a structured breakdown of which page is more likely to be cited and why. Typical use: your page (url_a) vs a competitor's page (url_b). Read-only. Runs audit_page on both URLs in parallel (2 HTTP fetches per URL), then diffs dimension_scores and findings. No new fetch logic beyond what audit_page already does. Deterministic, rule-based; no L

{
  "tool": "diff_pages",
  "arguments": {
    "url_a": "…",
    "url_b": "…"
  }
}

Single-call site sweep: runs audit_page (homepage), check_robots, check_sitemap, and audit_schema in parallel and returns an overall grade (A–F) plus top-5 highest-impact fixes. Read-only. Issues several HTTP GETs against the domain (homepage fetch, robots.txt, sitemap.xml, and up to 50 sitemap URL HEAD checks); no writes, no auth required, no rate limits beyond polite per-host throttling. The ho

{
  "tool": "audit_site",
  "arguments": {
    "domain": "…"
  }
}

Site-wide content audit: discovers the sitemap, samples N URLs by deterministic uniform stride, runs audit_page on each, and returns score distribution + worst pages + most-common findings. Read-only. One HTTP GET for sitemap discovery, optionally a few more for sitemap-index children, then `sample_size` × audit_page calls (each one HTTP GET + parsing). Polite throttling is enforced per host. De

{
  "tool": "audit_sitemap",
  "arguments": {
    "domain": "…"
  }
}

Render an audit_page or audit_site result as a Markdown report and write it to a file under MCP_WORKSPACE_ROOT (defaults to cwd).

{
  "tool": "report_save",
  "arguments": {
    "path": "…"
  }
}