"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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• seo_audit_aeoA

  mcp-seoB

  Performs Answer Engine Optimization audit: Direct answer blocks, question headings (What/How/Why), FAQ schema alignment, concise definition snippets.

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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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• 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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• 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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• accessibility_auditA

  render-mcpA

  Run a WCAG accessibility audit on a page using axe-core in a real browser, and report the violations with the elements responsible. Because it runs against a genuinely rendered page, colour-contrast and other rules that depend on layout and computed colour actually fire — these are silently skipped by audits that parse HTML without a layout engine. Use this to check a page meets WCAG before shipping.

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• create_campaignA

  xmagnetB

  Create a DRAFT email campaign via a programmatic wizard. Call this tool and it will guide through the steps — no manual orchestration needed. WIZARD STEPS (handled automatically by the tool): 1. Call with contacts + total_contacts → tool returns engine picker (NextGen vs MyConvo) 2. Add campaign_type from user's click → tool returns campaign category chips (promotional, newsletter, event…) 3. Add campaign_category from user's click → tool returns engine-specific template gallery MyConvo: shows plain_email_templates (personal plain-text). NextGen: shows campaign_templates (HTML). 4. Add template_id from user's pick → tool creates the draft campaign. RULES: Reuse contacts from prior search — never re-search. Pass total_contacts from search result's total_in_crm so the user always sees the full count. Saves as DRAFT only — no emails sent.

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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_instructionsA

  debridge-mcpB

  Returns the full guide for the deBridge MCP server. CALL THIS FIRST.

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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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• get_support_guideA

  GuruWalkB

  Returns an official GuruWalk support guide for a specific traveler-support topic. GuruWalk is a platform for free walking tours and paid activities; these guides are GuruWalk's own source of truth on how bookings, cancellations, account settings and contacting guides actually work, including current policies and the exact URLs travelers should use. These guides apply only to bookings and accounts on guruwalk.com. Available topics: - account_settings: The traveler wants to manage their GuruWalk account: edit their details (name, surname, phone, city, password), change their email, stop receiving emails / unsubscribe, or delete their account; or they can't access their account. These are concrete steps you shouldn't improvise: consult this before answering. - contact_guru: The traveler wants to contact or coordinate something with the guide of their GuruWalk booking, or thinks they are talking directly to the guide: they can't find them at the meeting point, the guide didn't show up, they're running late, they treat you as if you were the guide, ask for the tour photos, or ask about bringing a pet or paying the guide, or have a question only the guide can answer. - free_tour_modification: The traveler wants to modify or reschedule their GuruWalk free tour — change the day, time, language or number of people — or asks how to do it. - group_booking: The traveler wants to book or extend a GuruWalk booking for a group (they usually say how many; treat it as a large group from around 6 people), asks how to book for many people, can't book for the whole group, sees a large-group notice or is asked for a card or payment for the group, or had a booking cancelled as "group or duplicate". The rules aren't intuitive; consult this before advising. - paid_cancellation: The traveler wants to cancel or change a paid activity booked on GuruWalk, asks about a refund, or can't cancel from their account. Call this when the traveler raises a support topic covered above. Pass the exact topic; the guide content is returned.

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• accounting_delete_journal_entryA

  Lovie Company FormationC

  Delete or void a journal entry. For entries tied to a transaction prefer re-categorizing the transaction (the entry re-derives). Deleting a derived entry is temporary — the engine re-creates it on the next sync; deleting a manual per-transaction override reverts the transaction to its automatic entry; a standalone manual entry is voided (kept for audit, excluded from reports). Every outcome is audit-logged.

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  Destructive

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