"Answer Engine Optimization (AEO) Audit Guide" matching MCP tools:

• run_aeo_visibility_checkA

  marketing-complianceA

  Run the AEO / GEO (Answer Engine / Generative Engine Optimization) visibility audit (12 rules). Analyses whether AI answer engines (ChatGPT, Perplexity, Claude, Gemini) will cite the project for its category, and the gaps stopping it. Returns a 0–100 citeability score, per-engine read, per-rule analysis, and a ranked fix list. The 2026 successor to SEO.

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• audit_domainA

  AEO AuditB

  Run a full AEO (Answer Engine Optimization) audit for a domain. Checks how the domain appears across AI answer engines for given queries. Returns citation rate, grade (A-F), competitor comparison, and per-query results.

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• how_to_appear_in_ai_searchA

  radveoA

  Get Radveo's honest, practical playbook for getting a local business found and cited inside AI engine answers (ChatGPT, Gemini, Google AI Overviews). Answer-engine optimization (AEO) steps any owner can act on.

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• proximens_geo_compare_urlsA

  Proximens OracleA

  Pro-tier. Fetch two web pages (your URL and a competitor's) and audit both against the Proximens GEO Engine principles using the same audit engine as audit_url, then compute the delta. INPUT: self_url and competitor_url (both required, http/https). RETURNS: JSON with a 0-100 score per URL (same scoring as audit_url), the principles each page satisfies, the principles each page VIOLATES that the other satisfies (delta_principles), and strategic insights on where to close the gap. USE WHEN you want a competitive GEO gap analysis between your page and a rival's.

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• get_productA

  Tolstoy ShopperA

  Load full details for one product by its `productRef` (from search_products or browse_products): description, price, media, the brand's real product videos, size guide, per-variant stock, and buy links. Returns each variant's id and options (size/color). Use it to resolve the exact `variantId` the shopper wants before calling build_cart, and to answer fit/sizing questions from the size guide. The response includes the product image so it can be seen directly.

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• lekta_auditA

  Lekta — AEO/GEO Audit for AI Answer EnginesA

  Audits ONE live page for AEO (Answer Engine Optimization), also called GEO (Generative Engine Optimization): whether ChatGPT, Claude, Perplexity, Google AI Overviews and Bing can reach, read, quote and cite it. Fetches the page the way those engines do — a plain HTTP GET as LektaBot, then a headless-Chromium render pass — and grades it A+ to F across four weighted layers: Access 25% (robots.txt permission for 36 published AI crawler tokens: GPTBot, OAI-SearchBot, ClaudeBot, Claude-SearchBot, PerplexityBot, Googlebot, bingbot, CCBot and more), Indexability 25% (noindex / nosnippet / canonical controls, and how much main content survives with JavaScript off), Answerability 30% (concrete data density, external evidence, semantic section structure, hedging), Recency 20% (a machine-readable and visible last-updated date). Returns Markdown under fixed headings: "## Verdict" (grade, score, per-layer scores, and the URL actually measured after redirects), "## Top issues" (failing and warned checks ranked by points lost, tagged critical/serious/moderate/minor, with evidence), "## Fixes" (the full remediation text for each). Unmeasured layers cap the grade at B and say so. USE THIS for "run an AEO audit", "run a GEO audit", "why is my page not cited in AI answers", "will ChatGPT / Claude / Perplexity quote this page", "check my robots.txt for AI crawlers" — and again after every deploy to re-measure. DO NOT use it when code will parse the result (lekta_report is the same run as JSON), when an audit already exists and you only want the ranked work list (lekta_fix_plan, which never fetches), or to compare two runs (lekta_diff). It scores one page, not a site: no crawling, no sitemap expansion. COST AND BEHAVIOUR: read-only — the audited site is never modified. It READS AND GRADES robots.txt rather than obeying it as a fetch gate: the audit runs even where LektaBot is disallowed, and the report flags that. A blocking call — a live fetch plus a render pass, cut off at a 75 s hard limit. NOT idempotent: a URL not audited in the last 15 minutes starts a fresh run and spends one slot of the daily MCP quota (free plan default 10 fresh audits per UTC day across 1 hostname; every result prints the count used and the host slot in use), while a repeat inside that window replays the cached measurement for free. An unreachable, blocked or HTTP-error target still returns a report, graded U for "unknown" — never F. Only these return an error instead of a grade: a timeout or a server-side engine failure, an exhausted daily quota or host slot, 2 audits already in flight for this account, or the per-target ceiling of 1 audit per minute and 5 per hour.

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• lekta_auditA

  lektaA

  Audits ONE live page for AEO (Answer Engine Optimization), also called GEO (Generative Engine Optimization): whether ChatGPT, Claude, Perplexity, Google AI Overviews and Bing can reach, read, quote and cite it. Fetches the page the way those engines do — a plain HTTP GET as LektaBot, then a headless-Chromium render pass — and grades it A+ to F across four weighted layers: Access 25% (robots.txt permission for 36 published AI crawler tokens: GPTBot, OAI-SearchBot, ClaudeBot, Claude-SearchBot, PerplexityBot, Googlebot, bingbot, CCBot and more), Indexability 25% (noindex / nosnippet / canonical controls, and how much main content survives with JavaScript off), Answerability 30% (concrete data density, external evidence, semantic section structure, hedging), Recency 20% (a machine-readable and visible last-updated date). Returns Markdown under fixed headings: "## Verdict" (grade, score, per-layer scores, and the URL actually measured after redirects), "## Top issues" (failing and warned checks ranked by points lost, tagged critical/serious/moderate/minor, with evidence), "## Fixes" (the full remediation text for each). Unmeasured layers cap the grade at B and say so. USE THIS for "run an AEO audit", "run a GEO audit", "why is my page not cited in AI answers", "will ChatGPT / Claude / Perplexity quote this page", "check my robots.txt for AI crawlers" — and again after every deploy to re-measure. DO NOT use it when code will parse the result (lekta_report is the same run as JSON), when an audit already exists and you only want the ranked work list (lekta_fix_plan, which never fetches), or to compare two runs (lekta_diff). It scores one page, not a site: no crawling, no sitemap expansion. COST AND BEHAVIOUR: read-only — the audited site is never modified. It READS AND GRADES robots.txt rather than obeying it as a fetch gate: the audit runs even where LektaBot is disallowed, and the report flags that. A blocking call — a live fetch plus a render pass, cut off at a 75 s hard limit. NOT idempotent: a URL not audited in the last 15 minutes starts a fresh run and spends one slot of the daily MCP quota (free plan default 10 fresh audits per UTC day across 1 hostname; every result prints the count used and the host slot in use), while a repeat inside that window replays the cached measurement for free. An unreachable, blocked or HTTP-error target still returns a report, graded U for "unknown" — never F. Only these return an error instead of a grade: a timeout or a server-side engine failure, an exhausted daily quota or host slot, 2 audits already in flight for this account, or the per-target ceiling of 1 audit per minute and 5 per hour.

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• get_sc_theoryA

  SCModelingA

  Reference guide to supply-chain simulation concepts: ordering policies, BOM, FDD formulas, event-driven simulation. Pure static text — no engine call, deterministic output. Use this when the user asks a conceptual 'how does this work' question rather than asking for a number.

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• list_opt_demosA

  SCModelingA

  List the bundled SCModeling optimization demos. Returns id + label + one-line summary for each (Tariff, Coffee Co-pack, SSO Basic). Use this before describe_opt_demo or get_opt_result to know which demo_id values are valid. All demos are precomputed sample-only fixtures — for optimization on real client data, the SCModeling desktop tool is the product.

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• create_guidesA

  serpmanticsA

  Create new guides Create one or more new guides based on provided queries. Each guide targets exactly ONE engine and ONE analysis mode, chosen with the optional `source` field (default `google`). How to request each guide type: 1. Google SERP guide (1 credit per guide): omit `source`, or pass `source: "google"`. Example payload: {"queries": ["best crm"], "lang": "en-us"} 1bis. Google AI Overview guide (1 credit per guide). Two modes, like AI engines: `source: "google_ai_overview"` builds the guide from the TEXT of Google's AI answers (AI Overview, completed with AI Mode answers) ; `source: "google_ai_overview_citations"` builds it from the content of the web SOURCES those answers cite (recommended for GEO). Same language/country parameters as a Google SERP guide, 1 credit per guide in both modes. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "google_ai_overview_citations"} 2. LLM ANSWER guide (4 credits per guide): pass the engine name alone, e.g. `source: "chatgpt"`. The guide is built from the answer text the AI generates for the query. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt"} 3. LLM CITATIONS guide (4 credits per guide) [RECOMMENDED AI mode]: pass the engine name with the `_citations` suffix, e.g. `source: "chatgpt_citations"`. The guide is built from the content of the web pages the AI cites in its answer. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt_citations"} Which AI mode to pick? For GEO (getting a page visible in AI answers), prefer `<engine>_citations`: AI engines send traffic by CITING pages as sources, so the winning move is to look like the pages they cite. The answer-text mode (`<engine>` alone) is mostly useful to analyze how the AI phrases its own answer. When in doubt, pick `<engine>_citations`. The same two modes exist for every AI engine (chatgpt, perplexity, claude, gemini, grok, mistral, deepseek). To optimize the same page for several engines or modes (e.g. Google AND ChatGPT answers AND ChatGPT sources), create one guide per source value on the same query. IMPORTANT, HOW TO READ THE RESPONSE OF THIS ENDPOINT, WHICH SPENDS CREDITS. Queries listed in `guidesFailed` are PROVEN not to have produced a guide and their credit was given back (unless the account has unlimited credits, where nothing was reserved): re-sending them is free and correct. Queries listed in `guidesUnknown` have an UNDECIDABLE outcome and their credit is deliberately KEPT, because the guide was most likely written: DO NOT re-send them, you would pay for the same guide twice. Look them up in `GET /api/v1/guides` after a few minutes instead, and contact support if nothing shows up. Finally, a `200` is NOT a promise that every query produced a guide: compare `guides.length` with the number of queries you sent, never read `success` alone, and never re-send a query just because it is missing from `guides`.

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• get_leadproof_evaluation_pathA

  LeadProof Sales AgentA

  Use first when a buyer is exploring LeadProof without supplying workflow data. Returns the official no-purchase sequence: buyer guide, fictional example audit, browser-only calculator, public workflow tests, and no-card trial. For a scored workflow diagnosis, use audit_lead_workflow; for replay testing, use get_leadproof_replay_trial; for paid checkout, use get_leadproof_checkout only after authorization.

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• list_compiled_corridorsA

  lr-labsA

  List which treaty pairs, PE families, and compiled-rule counts the LR Labs engine covers, plus the structured-fact schema. Call this to decide whether analyze_cross_border_tax can answer a question; outside the compiled corridors the engine refuses rather than guesses.

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• get_visibility_indexA

  dabyte.aiA

  The whole current release in one call: every tracked brand in SaaS & AI tools with its rank, share of answer overall and per engine, commercial intent and quadrant. Share of answer is the percentage of a fixed panel of category buyer prompts in which an engine names the brand. Use this when the question is about the field — who leads, who is absent, how the category looks. It is one response of roughly 8 KB for 20 brands, so prefer it over calling get_brand_visibility repeatedly. Do NOT use it for one named brand (get_brand_visibility is the direct answer), for movement over time (get_history holds the series; a single release cannot show a trend), or to audit a website's own AI visibility — this is a measured dataset about third-party brands, not a site audit. Covers SaaS & AI tools only; the sibling index at dablock.ai covers the other niche. Re-measured weekly, so the same call returns the same figures until the next release. Data is CC BY 4.0 and free: no key, no account, no rate limit — cite the release date and dabyte.ai when quoting a number.

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• get_visibility_indexA

  DABLOCK AI Visibility IndexA

  The whole current release in one call: every tracked brand in crypto/Web3 with its rank, share of answer overall and per engine, commercial intent and quadrant. Share of answer is the percentage of a fixed panel of category buyer prompts in which an engine names the brand. Use this when the question is about the field — who leads, who is absent, how the category looks. It is one response of roughly 8 KB for 24 brands, so prefer it over calling get_brand_visibility repeatedly. Do NOT use it for one named brand (get_brand_visibility is the direct answer), for movement over time (get_history holds the series; a single release cannot show a trend), or to audit a website's own AI visibility — this is a measured dataset about third-party brands, not a site audit. Covers crypto/Web3 only; the sibling index at dabyte.ai covers the other niche. Re-measured weekly, so the same call returns the same figures until the next release. Data is CC BY 4.0 and free: no key, no account, no rate limit — cite the release date and dablock.ai when quoting a number.

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• get_visibility_indexA

  dablock.aiA

  The whole current release in one call: every tracked brand in crypto/Web3 with its rank, share of answer overall and per engine, commercial intent and quadrant. Share of answer is the percentage of a fixed panel of category buyer prompts in which an engine names the brand. Use this when the question is about the field — who leads, who is absent, how the category looks. It is one response of roughly 8 KB for 24 brands, so prefer it over calling get_brand_visibility repeatedly. Do NOT use it for one named brand (get_brand_visibility is the direct answer), for movement over time (get_history holds the series; a single release cannot show a trend), or to audit a website's own AI visibility — this is a measured dataset about third-party brands, not a site audit. Covers crypto/Web3 only; the sibling index at dabyte.ai covers the other niche. Re-measured weekly, so the same call returns the same figures until the next release. Data is CC BY 4.0 and free: no key, no account, no rate limit — cite the release date and dablock.ai when quoting a number.

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• get_visibility_indexA

  DABYTE AI Visibility IndexA

  The whole current release in one call: every tracked brand in SaaS & AI tools with its rank, share of answer overall and per engine, commercial intent and quadrant. Share of answer is the percentage of a fixed panel of category buyer prompts in which an engine names the brand. Use this when the question is about the field — who leads, who is absent, how the category looks. It is one response of roughly 8 KB for 20 brands, so prefer it over calling get_brand_visibility repeatedly. Do NOT use it for one named brand (get_brand_visibility is the direct answer), for movement over time (get_history holds the series; a single release cannot show a trend), or to audit a website's own AI visibility — this is a measured dataset about third-party brands, not a site audit. Covers SaaS & AI tools only; the sibling index at dablock.ai covers the other niche. Re-measured weekly, so the same call returns the same figures until the next release. Data is CC BY 4.0 and free: no key, no account, no rate limit — cite the release date and dabyte.ai when quoting a number.

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• ios_accessibility_auditA

  Robot Actions — Remote Device ControlB

  Run an accessibility audit on the CURRENT screen of an iOS device — Apple's own XCTest audit engine (the same one Xcode's "Audit for Accessibility" button runs), so findings match what Apple reports. Audits whatever is in the foreground right now, so navigate to the screen you care about FIRST (ios_tap_by_label / ios_navigate_url), then call this. Reports contrast failures, tap targets under 44×44pt, missing/unhelpful labels, elements the accessibility engine cannot see, Dynamic Type and clipped-text problems, and wrong traits. Each issue carries the offending element's label, type and screen-point rect — the rect centre is directly tappable with ios_tap. Pass `auditTypes` to narrow the run (much faster on dense screens). TIMING: the audit sees the screen as it is at that instant — running it immediately after a launch or navigation, while the UI is still animating in, under-reports (measured on device: 4 issues mid-animation vs 5 once settled). Let the screen settle first. Requires iOS 17+ (errors on older) and an active iOS automation session (auto-starts if needed).

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• analyze_aeoA

  mcpA

  Analyze how well content is optimized for AI answer engines. Evaluates content for AI answer engines (ChatGPT, Perplexity, Gemini, Claude). Combines Q&A pattern detection, snippet extractability, and entity clarity analysis with a full Citation Readiness assessment. AEO Scoring Framework (100 points): - Answer Format Detection: 30 points (Q&A extractability patterns) - FAQ Schema Presence: 20 points (FAQPage schema markup) - HowTo Schema Presence: 15 points (HowTo schema markup) - Direct Answer Snippets: 20 points (short extractable blocks <50 words) - Entity Clarity Score: 15 points (clear entity definitions) Neutral Schema Scoring: If no FAQ/HowTo-style content detected, those schema metrics score full points rather than penalizing. Grade Scale: A (85-100), B (70-84), C (55-69), D (40-54), F (0-39) Results are cached for fast subsequent lookups. Rate limited to 1 request per minute per domain. Args: url: The website URL to analyze (e.g., "https://example.com"). Returns: AEO analysis with: - url: The analyzed URL - aeo_score: Overall AEO score (0-100) - aeo_grade: Letter grade (A-F) - aeo_metrics: Individual metric scores - citation: Full Citation Readiness analysis (score, grade, issues, signals) - issues: Problems detected (critical, warnings, info) - signals: Positive signals detected - recommendations: Prioritized improvements - cached: Whether result was from cache

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• get_guideA

  VeteranHQA

  Use this when a slug from search_guides names the guide a veteran needs and the answer should carry what the guide says. Returns the slug, title and summary, the date the facts in it were last verified against its sources, the public URL of the guide on veteranhq.app, the body split into the sections the guide itself defines, each with a heading and its text, and the sources the guide cites, each with a title and a URL. A slug this library does not hold returns a tool error carrying GUIDE_NOT_FOUND, whose message says to call search_guides and use a slug from its results. A guide longer than one result can carry returns the sections that fit, plus omittedSections counting the sections left out and a note stating that count and the guide URL. A single section larger than the whole budget is served shortened at a paragraph or sentence boundary, with truncatedSection naming its heading and the note repeating it, so a section that was cut is distinguishable from one served whole. Those three fields are absent when the whole guide is returned, so a shortened answer is distinguishable from a complete one. A guide states the rules as VeteranHQ read them from the cited sources on the date it reports, and VA decides a claim. It reads no account data, so the answer is the same for every caller with the same inputs.

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• auditA

  mcpA

  Perform comprehensive audit of a website URL. Fetches the URL content ONCE and provides a combined report with: - Classification: category, subcategory, language, sentiment, demographics - SEO Analysis: score, grade, issues, recommendations - EEAT Analysis: experience, expertise, authoritativeness, trustworthiness scores - AEO Analysis: AI answer engine optimization score, metrics, issues, signals (includes full Citation Readiness analysis in the nested 'citation' key) - Advertiser Matching: best-fit advertising networks with scores - Similar Sites: competitor/related sites from the same category This is more efficient than calling classify_url, analyze_seo, analyze_eeat, analyze_aeo, select_advertiser, and find_similar_sites separately as it only fetches the page once. Args: url: The website URL to audit (e.g., "https://example.com"). Returns: Comprehensive audit report with: - url: The analyzed URL - classification: Category, subcategory, language, sentiment, demographics - seo: Score, grade, issues, recommendations - eeat: EEAT score, grade, category scores, issues, signals - aeo: AEO score, grade, metrics, issues, signals (includes citation results) - advertisers: Matched advertising networks with scores - similar_sites: Related sites from the same category (up to 10) - cached: Whether result was from cache

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• aeo_auditA

  mcpA

  Lance un audit AEO/GEO d’une URL (asynchrone, ~2 min) et renvoie immédiatement l’audit_id. Suivre avec aeo_audit_status jusqu’à completed, puis aeo_report / aeo_improvements. Coûte 50 GTO, débités à la complétion. NE PAS relancer le même audit pendant qu’un run est en cours.

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