Vantage

dev.vantagemcp/vantageVersion 1.14.0

Checks whether ChatGPT, Gemini and Perplexity cite your site, and who they cite instead.

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Claude Code

claude mcp add --transport http vantage https://vantagemcp.dev/mcp

Claude Desktop and claude.ai

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Settings > Connectors > Add custom connector
Name: vantage
Remote MCP server URL: https://vantagemcp.dev/mcp

Cursor

{
  "mcpServers": {
    "vantage": {
      "url": "https://vantagemcp.dev/mcp"
    }
  }
}

VS Code

code --add-mcp '{"name":"vantage","type":"http","url":"https://vantagemcp.dev/mcp"}'

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Tools

11 tools

Tools of Vantage
	Description		Read-only	Idempotent	Open world
`analyze_citation_coverage`Check which of several prompts/keywords actually cite a specific domain, and which ones don't. It answers "out of everything we care about, where do we already show up, and where are we invisible"; its not_cited list is the set of keywords worth a closer look. Read-only for the caller, safe to retry. Costs 1 quota unit per keyword per sample (1 sample by default; free tier is 30 units/month shared across every metered tool, so up to 30 keyword checks that period if nothing else is used). A per-keyword provider error doesn't fail the whole call - that keyword's entry just carries an "error" field instead. On the hosted endpoint each keyword's result is remembered for 180 days against your API key, so the next check of the same domain and keyword reports what changed (see "previous" and "change"); get_check_history reads that record back for free. Returns: {"domain", "engine", "samples", "keywords_checked" (int, excludes any that errored), "keywords_cited" (int), "coverage_pct" (float, 0-100), "not_cited" (list of the keyword strings where domain was not cited - the actionable list), "newly_cited" and "no_longer_cited" (keywords whose status flipped since your last check of them), "results" (one entry per keyword, in the order given: {"keyword", "cited" (bool: cited in at least half of the samples), "cited_runs" (how many sampled answers cited it), "samples_ok" (how many answers came back), "rank" (int\|null, best 1-based position among the answers' sources, null when never cited), "cited_pages" (list of your own page URLs the answers cited, empty when none - saved with the check, so get_check_history can follow one page over time), "num_sources_cited", "source_domains" (who IS cited, for a keyword you are not in), "source_mix" (how much of that is community sites such as Reddit, YouTube, X - where to get discussed to close the gap), "source_frequency" (only with samples above 1: {"domain", "runs"} per domain), "leads_with_list", "opening_word_count", "mentioned" (bo	Check which of several prompts/keywords actually cite a specific domain, and which ones don't. It answers "out of everything we care about, where do we already show up, and where are we invisible"; its not_cited list is the set of keywords worth a closer look. Read-only for the caller, safe to retry. Costs 1 quota unit per keyword per sample (1 sample by default; free tier is 30 units/month shared across every metered tool, so up to 30 keyword checks that period if nothing else is used). A per-keyword provider error doesn't fail the whole call - that keyword's entry just carries an "error" field instead. On the hosted endpoint each keyword's result is remembered for 180 days against your API key, so the next check of the same domain and keyword reports what changed (see "previous" and "change"); get_check_history reads that record back for free. Returns: {"domain", "engine", "samples", "keywords_checked" (int, excludes any that errored), "keywords_cited" (int), "coverage_pct" (float, 0-100), "not_cited" (list of the keyword strings where domain was not cited - the actionable list), "newly_cited" and "no_longer_cited" (keywords whose status flipped since your last check of them), "results" (one entry per keyword, in the order given: {"keyword", "cited" (bool: cited in at least half of the samples), "cited_runs" (how many sampled answers cited it), "samples_ok" (how many answers came back), "rank" (int\|null, best 1-based position among the answers' sources, null when never cited), "cited_pages" (list of your own page URLs the answers cited, empty when none - saved with the check, so get_check_history can follow one page over time), "num_sources_cited", "source_domains" (who IS cited, for a keyword you are not in), "source_mix" (how much of that is community sites such as Reddit, YouTube, X - where to get discussed to close the gap), "source_frequency" (only with samples above 1: {"domain", "runs"} per domain), "leads_with_list", "opening_word_count", "mentioned" (bo	No	Yes	Yes	Yes
`analyze_citation_gap`Compare your own page's structure against the AI-generated answer actually cited for this keyword, and return a fix brief: the differences between the two, each with the change worth testing, not just a description of the winner. Use this to answer 'how does my page differ from what AI cites for this, and what could I try'. The brief lists things to test, not proven causes of citation. It never contains the cited answer's text. Read-only: no side effects, safe to retry. Costs 1 quota unit per sample (1 by default; free tier is 30 units/month shared across every metered tool, so up to 30 single-sample calls to this tool alone if nothing else is used that period). A sample that fails is not charged. Returns: {"keyword", "your_url", "already_cited" (bool: your page's site is among the answer's sources, in at least half of them with samples above 1; when true, fix_brief is one line saying so instead of changes), "basis" (one sentence: how many answers, which engine and market, and that the brief lists things to test), "samples_ok" and "cited_in_answers" (only with samples above 1), "winning" (structure of the AI-cited answer, same shape as analyze_citation_structure), "yours" (same structure computed for your_url, including its own "outline" and "has_table", with "num_links_out"/"linked_domains" standing in for source count), "gaps" (list of plain-English differences worth acting on), "possibly_missing" (heads from the winning outline whose key words mostly do not appear on your page; word matching, so check each before adding it), "fix_brief" (list, most important first, each line what was observed then what is worth testing: opening, number, list, sections, missing points, table, then a reminder to write in your own words - or a "no structural change indicated" line when every check already matches - and always last, one off-site option drawn from the winning answer's source_mix: which community sites (Reddit, YouTube, X...) it cites, or which other sites to get men	Compare your own page's structure against the AI-generated answer actually cited for this keyword, and return a fix brief: the differences between the two, each with the change worth testing, not just a description of the winner. Use this to answer 'how does my page differ from what AI cites for this, and what could I try'. The brief lists things to test, not proven causes of citation. It never contains the cited answer's text. Read-only: no side effects, safe to retry. Costs 1 quota unit per sample (1 by default; free tier is 30 units/month shared across every metered tool, so up to 30 single-sample calls to this tool alone if nothing else is used that period). A sample that fails is not charged. Returns: {"keyword", "your_url", "already_cited" (bool: your page's site is among the answer's sources, in at least half of them with samples above 1; when true, fix_brief is one line saying so instead of changes), "basis" (one sentence: how many answers, which engine and market, and that the brief lists things to test), "samples_ok" and "cited_in_answers" (only with samples above 1), "winning" (structure of the AI-cited answer, same shape as analyze_citation_structure), "yours" (same structure computed for your_url, including its own "outline" and "has_table", with "num_links_out"/"linked_domains" standing in for source count), "gaps" (list of plain-English differences worth acting on), "possibly_missing" (heads from the winning outline whose key words mostly do not appear on your page; word matching, so check each before adding it), "fix_brief" (list, most important first, each line what was observed then what is worth testing: opening, number, list, sections, missing points, table, then a reminder to write in your own words - or a "no structural change indicated" line when every check already matches - and always last, one off-site option drawn from the winning answer's source_mix: which community sites (Reddit, YouTube, X...) it cites, or which other sites to get men	No	Yes	Yes	Yes
`analyze_citation_structure`Analyze the structural shape of the AI-generated answer actually cited for a keyword: does it lead with a list, how long is the opening passage, how many sources does it cite and from which domains. Use this to understand what a winning AI-search answer looks like for a topic, e.g. before writing content on it. Read-only: no side effects, safe to retry. Costs 1 quota unit per sample (1 by default; free tier is 30 units/month shared across every metered tool, so up to 30 single-sample calls to this tool alone if nothing else is used that period). Returns: {"keyword", "engine", "model" (the answering model's version, as the provider reports it), "checked_at" (when the answer was fetched, UTC), "leads_with_list" (bool), "opening" (the answer's opening paragraph, as text), "opening_word_count" (int), "opening_has_number" (bool), "detail_preview" (up to 240 characters of what follows the opening, cut at a word), "outline" (list of up to 12 section heads, in order: the answer's headings, or its top-level list items when it has fewer than two headings; heads only, never the text under them), "has_table" (bool), "num_sources_cited" (int), "source_domains" (list of up to 10 domain strings), "source_urls" (the exact cited page URLs, up to 30, so you can see which page of a domain was cited), "source_mix" ({"community_pct" (share of those sources that are community sites such as Reddit, YouTube, X, Quora), "community_domains", "other_domains"}), "country", "language"}. With samples above 1 the shape fields describe the first answer, plus "samples_ok" (answers that came back) and "source_frequency" (list of {"domain", "runs"}: how many of the answers cited each domain, most often first). Answers change from run to run, so a domain cited in every sample is a far stronger signal than one sample. Args: keyword: the topic/query to analyze, e.g. "how to reduce churn". country: market to read the answer in, e.g. "Italy". Defaults to "United States". For perplexity	Analyze the structural shape of the AI-generated answer actually cited for a keyword: does it lead with a list, how long is the opening passage, how many sources does it cite and from which domains. Use this to understand what a winning AI-search answer looks like for a topic, e.g. before writing content on it. Read-only: no side effects, safe to retry. Costs 1 quota unit per sample (1 by default; free tier is 30 units/month shared across every metered tool, so up to 30 single-sample calls to this tool alone if nothing else is used that period). Returns: {"keyword", "engine", "model" (the answering model's version, as the provider reports it), "checked_at" (when the answer was fetched, UTC), "leads_with_list" (bool), "opening" (the answer's opening paragraph, as text), "opening_word_count" (int), "opening_has_number" (bool), "detail_preview" (up to 240 characters of what follows the opening, cut at a word), "outline" (list of up to 12 section heads, in order: the answer's headings, or its top-level list items when it has fewer than two headings; heads only, never the text under them), "has_table" (bool), "num_sources_cited" (int), "source_domains" (list of up to 10 domain strings), "source_urls" (the exact cited page URLs, up to 30, so you can see which page of a domain was cited), "source_mix" ({"community_pct" (share of those sources that are community sites such as Reddit, YouTube, X, Quora), "community_domains", "other_domains"}), "country", "language"}. With samples above 1 the shape fields describe the first answer, plus "samples_ok" (answers that came back) and "source_frequency" (list of {"domain", "runs"}: how many of the answers cited each domain, most often first). Answers change from run to run, so a domain cited in every sample is a far stronger signal than one sample. Args: keyword: the topic/query to analyze, e.g. "how to reduce churn". country: market to read the answer in, e.g. "Italy". Defaults to "United States". For perplexity	No	Yes	Yes	Yes
`analyze_citation_structure_batch`Analyze the structural shape of the winning AI answer across several related keywords/topics in one call: does each lead with a list, how long is the opening, how many sources it cites. It covers a topic cluster in one call, for content planning across related pieces. Read-only: no side effects, safe to retry. Costs 1 quota unit per keyword in the batch (free tier is 30 units/month shared across all the metered tools, so up to 30 keywords total that period if nothing else is used). A per-keyword provider error doesn't fail the whole batch - that keyword's entry just carries an "error" field instead. Returns: {"results" (list, one {"keyword", ...same shape as analyze_citation_structure, or "error"} per keyword, in the order given), "summary": {"topics_analyzed", "topics_requested", "list_led_count", "avg_sources_cited", "avg_community_pct" (average source_mix.community_pct across the analyzed topics)}}. Args: keywords: topics/queries to analyze, e.g. ["how to reduce churn", "churn rate benchmarks", "reduce customer churn saas"]. Max 10. country: market to read the answers in, e.g. "Italy". Defaults to "United States". language: language code, e.g. "it". Defaults to "en". engine: "chat_gpt" (default), "gemini" or "perplexity", as in analyze_citation_structure. One answer per topic.	Analyze the structural shape of the winning AI answer across several related keywords/topics in one call: does each lead with a list, how long is the opening, how many sources it cites. It covers a topic cluster in one call, for content planning across related pieces. Read-only: no side effects, safe to retry. Costs 1 quota unit per keyword in the batch (free tier is 30 units/month shared across all the metered tools, so up to 30 keywords total that period if nothing else is used). A per-keyword provider error doesn't fail the whole batch - that keyword's entry just carries an "error" field instead. Returns: {"results" (list, one {"keyword", ...same shape as analyze_citation_structure, or "error"} per keyword, in the order given), "summary": {"topics_analyzed", "topics_requested", "list_led_count", "avg_sources_cited", "avg_community_pct" (average source_mix.community_pct across the analyzed topics)}}. Args: keywords: topics/queries to analyze, e.g. ["how to reduce churn", "churn rate benchmarks", "reduce customer churn saas"]. Max 10. country: market to read the answers in, e.g. "Italy". Defaults to "United States". language: language code, e.g. "it". Defaults to "en". engine: "chat_gpt" (default), "gemini" or "perplexity", as in analyze_citation_structure. One answer per topic.	No	Yes	Yes	Yes
`analyze_citation_trend`Track how a domain's AI-citation count has moved month over month, so you can see whether visibility is growing or fading instead of only ever checking a single point in time. Use this to answer 'is our AI visibility improving' or 'did that content push actually move the needle'. Pass compare_domains to put competitors' trends next to it. Read-only: no side effects, safe to retry. Costs 10 quota units per domain (the main domain plus each one in compare_domains; free tier is 30 units/month shared across every metered tool, so up to 3 single-domain calls to this tool alone if nothing else is used that period). A domain the provider fails on is not charged. Returns: {"domain", "platform", "months" (list of {"year", "month", "mentions" (int, 0 for a month with no tracked citations. A zero between two large months can be a gap in the provider's history rather than a real drop, so read isolated zeros with care), "ai_search_volume"}, oldest to newest), "trend": {"direction" ("up"/"down"/"flat"/"no_data"), "earliest_mentions", "latest_mentions", "excluded_current_partial_month" (bool, only present and true when the most recent calendar month was excluded from the trend calculation because it is still in progress and its count is not yet final - it is still returned inside `months`, just not compared)}}. The most recent entry in `months` (or `trend.latest_mentions` when the current month is not excluded) already IS the current count, so there is no need for a separate call just to see it right now. With compare_domains the result also carries "comparisons" (one {"domain", "months", "trend"} per extra domain, same shape and month window as above, in the order given, or {"domain", "error"} for one the provider failed on - that one is refunded and the rest still return). Args: domain: bare domain to check, e.g. "example.com" (no https://, no www). compare_domains: optional list of up to 5 more bare domains to track over the same months, e.g. ["asana.com",	Track how a domain's AI-citation count has moved month over month, so you can see whether visibility is growing or fading instead of only ever checking a single point in time. Use this to answer 'is our AI visibility improving' or 'did that content push actually move the needle'. Pass compare_domains to put competitors' trends next to it. Read-only: no side effects, safe to retry. Costs 10 quota units per domain (the main domain plus each one in compare_domains; free tier is 30 units/month shared across every metered tool, so up to 3 single-domain calls to this tool alone if nothing else is used that period). A domain the provider fails on is not charged. Returns: {"domain", "platform", "months" (list of {"year", "month", "mentions" (int, 0 for a month with no tracked citations. A zero between two large months can be a gap in the provider's history rather than a real drop, so read isolated zeros with care), "ai_search_volume"}, oldest to newest), "trend": {"direction" ("up"/"down"/"flat"/"no_data"), "earliest_mentions", "latest_mentions", "excluded_current_partial_month" (bool, only present and true when the most recent calendar month was excluded from the trend calculation because it is still in progress and its count is not yet final - it is still returned inside `months`, just not compared)}}. The most recent entry in `months` (or `trend.latest_mentions` when the current month is not excluded) already IS the current count, so there is no need for a separate call just to see it right now. With compare_domains the result also carries "comparisons" (one {"domain", "months", "trend"} per extra domain, same shape and month window as above, in the order given, or {"domain", "error"} for one the provider failed on - that one is refunded and the rest still return). Args: domain: bare domain to check, e.g. "example.com" (no https://, no www). compare_domains: optional list of up to 5 more bare domains to track over the same months, e.g. ["asana.com",	No	Yes	Yes	Yes
`check_page_readiness`Check whether one page has what AI answers look for before they cite it, for the question it should answer, and say exactly what to change, before or after a page is edited. It reads the HTML a crawler gets, so it also catches content that only appears with JavaScript. Checks: the <title> asks the question and fits (60 characters); one <h1> matching it; the first paragraph after the heading is the answer itself (12 to 45 words, a sentence, with a number or date); a visible "Updated" date that matches the JSON-LD dateModified and is under 90 days old; at least 300 words without JavaScript; valid structured data, and FAQ markup only for questions shown on the page; canonical and noindex; robots.txt letting OAI-SearchBot, PerplexityBot, Claude-SearchBot and Googlebot reach the page. Costs 0 quota units: it fetches the page and its robots.txt itself and never calls the data provider. Read-only, safe to retry. Passing makes a page easier to cite, it does not cause a citation: whether the question is one your site can own matters more, which only analyze_citation_coverage measures. Returns: {"url" (after redirects), "question", "passed", "failed", "fixes" (what to change, one line per failed check, in page order), "checks" (each {"rule", "ok", "observed", "fix"}), "basis" (one sentence on where the checks come from)}, or {"url", "error"} when the page cannot be fetched (not public, not HTML, an HTTP error). Args: url: full address of the page, e.g. "https://example.com/pricing". question: what the page should answer, in the words people search with, e.g. "how much does example cost".	Check whether one page has what AI answers look for before they cite it, for the question it should answer, and say exactly what to change, before or after a page is edited. It reads the HTML a crawler gets, so it also catches content that only appears with JavaScript. Checks: the <title> asks the question and fits (60 characters); one <h1> matching it; the first paragraph after the heading is the answer itself (12 to 45 words, a sentence, with a number or date); a visible "Updated" date that matches the JSON-LD dateModified and is under 90 days old; at least 300 words without JavaScript; valid structured data, and FAQ markup only for questions shown on the page; canonical and noindex; robots.txt letting OAI-SearchBot, PerplexityBot, Claude-SearchBot and Googlebot reach the page. Costs 0 quota units: it fetches the page and its robots.txt itself and never calls the data provider. Read-only, safe to retry. Passing makes a page easier to cite, it does not cause a citation: whether the question is one your site can own matters more, which only analyze_citation_coverage measures. Returns: {"url" (after redirects), "question", "passed", "failed", "fixes" (what to change, one line per failed check, in page order), "checks" (each {"rule", "ok", "observed", "fix"}), "basis" (one sentence on where the checks come from)}, or {"url", "error"} when the page cannot be fetched (not public, not HTML, an HTTP error). Args: url: full address of the page, e.g. "https://example.com/pricing". question: what the page should answer, in the words people search with, e.g. "how much does example cost".	No	Yes	Yes	Yes
`find_citation_leaders`Find which domains dominate AI-answer citations for a topic/keyword, and optionally check where one domain, or several competitors at once, show up among them. Use this to answer 'who's winning AI search for this topic' or 'is my competitor cited more than me for X'. Comparing several domains costs the same single call. Read-only: no side effects, safe to retry. Costs 10 quota units/call (free tier is 30 units/month shared across every metered tool, so up to 3 calls to this tool alone if nothing else is used that period). Returns: {"keyword", "platform", "top_domains" (list of {"domain", "mentions"}, most-cited domains for this keyword/platform, order as ranked by the provider), "top_domains_limit" (int, the provider's own cap on this list - absence from it is NOT evidence a domain has zero citations, only that it did not rank in the top `top_domains_limit`), "compare_domain_rank" (int\|null, only present when compare_domain was passed: the domain's 1-based position in top_domains, or null if it did not rank in the top `top_domains_limit`), "compare_domain_ranks" (only present when compare_domains was passed: {domain: rank or null}, same rank rule as compare_domain_rank), "country", "language", "source_mix" ({"community_pct" (share of these mentions that go to community sites such as Reddit, YouTube, X, Quora), "community_domains", "other_domains"}: a high community_pct means this topic is won by what people say about a brand elsewhere, not by any one site's pages)}. This tool's citation universe is the provider's tracked mention corpus for the keyword, which is a different measurement from analyze_citation_structure's single live answer - the two can legitimately disagree on whether a given domain shows up. Args: keyword: the topic/query to check, e.g. "best project management tool". platform: "chat_gpt" or "google" (Google's AI Overview). Defaults to chat_gpt. Perplexity and Gemini aren't available - the underlying data provider doesn't	Find which domains dominate AI-answer citations for a topic/keyword, and optionally check where one domain, or several competitors at once, show up among them. Use this to answer 'who's winning AI search for this topic' or 'is my competitor cited more than me for X'. Comparing several domains costs the same single call. Read-only: no side effects, safe to retry. Costs 10 quota units/call (free tier is 30 units/month shared across every metered tool, so up to 3 calls to this tool alone if nothing else is used that period). Returns: {"keyword", "platform", "top_domains" (list of {"domain", "mentions"}, most-cited domains for this keyword/platform, order as ranked by the provider), "top_domains_limit" (int, the provider's own cap on this list - absence from it is NOT evidence a domain has zero citations, only that it did not rank in the top `top_domains_limit`), "compare_domain_rank" (int\|null, only present when compare_domain was passed: the domain's 1-based position in top_domains, or null if it did not rank in the top `top_domains_limit`), "compare_domain_ranks" (only present when compare_domains was passed: {domain: rank or null}, same rank rule as compare_domain_rank), "country", "language", "source_mix" ({"community_pct" (share of these mentions that go to community sites such as Reddit, YouTube, X, Quora), "community_domains", "other_domains"}: a high community_pct means this topic is won by what people say about a brand elsewhere, not by any one site's pages)}. This tool's citation universe is the provider's tracked mention corpus for the keyword, which is a different measurement from analyze_citation_structure's single live answer - the two can legitimately disagree on whether a given domain shows up. Args: keyword: the topic/query to check, e.g. "best project management tool". platform: "chat_gpt" or "google" (Google's AI Overview). Defaults to chat_gpt. Perplexity and Gemini aren't available - the underlying data provider doesn't	No	Yes	Yes	Yes
`find_cited_questions`Find the questions people ask AI answer engines where a domain is already cited as a source, most-asked first. Starts from the domain, so nobody has to guess keywords first. Use this to answer 'what does ChatGPT already cite us for' or to pick the keywords to feed analyze_citation_coverage and analyze_citation_gap. Read-only: no side effects, safe to retry. Costs 10 quota units/call (free tier is 30 units/month shared across every metered tool, so up to 3 calls to this tool alone if nothing else is used that period). Returns: {"domain", "platform", "country", "language", "total_questions" (int, every tracked question citing the domain, which can exceed the list), "questions" (up to `limit`, most-asked first: {"question", "ai_search_volume" (monthly asks as the provider estimates them), "your_position" (1-based position of the domain among that answer's sources), "source_domains" (who else that answer cites), "last_seen" (when the provider last recorded this answer, UTC)})}. An empty list means the provider's tracked answers do not cite the domain, not that no answer anywhere does. This reads the provider's tracked answer corpus, the same measurement as find_citation_leaders, not a live answer: re-check a question with analyze_citation_coverage to see today's answer. Args: domain: bare domain, e.g. "example.com" (no https://, no www). Subdomains are included. platform: "chat_gpt" (default) or "google" (Google's AI Overview). limit: how many questions to return, 1 to 20. Default 20. country: market, e.g. "Italy". Defaults to "United States". chat_gpt only has United States data; use platform "google" elsewhere. language: language code, e.g. "it". Defaults to "en", the only option for chat_gpt.	Find the questions people ask AI answer engines where a domain is already cited as a source, most-asked first. Starts from the domain, so nobody has to guess keywords first. Use this to answer 'what does ChatGPT already cite us for' or to pick the keywords to feed analyze_citation_coverage and analyze_citation_gap. Read-only: no side effects, safe to retry. Costs 10 quota units/call (free tier is 30 units/month shared across every metered tool, so up to 3 calls to this tool alone if nothing else is used that period). Returns: {"domain", "platform", "country", "language", "total_questions" (int, every tracked question citing the domain, which can exceed the list), "questions" (up to `limit`, most-asked first: {"question", "ai_search_volume" (monthly asks as the provider estimates them), "your_position" (1-based position of the domain among that answer's sources), "source_domains" (who else that answer cites), "last_seen" (when the provider last recorded this answer, UTC)})}. An empty list means the provider's tracked answers do not cite the domain, not that no answer anywhere does. This reads the provider's tracked answer corpus, the same measurement as find_citation_leaders, not a live answer: re-check a question with analyze_citation_coverage to see today's answer. Args: domain: bare domain, e.g. "example.com" (no https://, no www). Subdomains are included. platform: "chat_gpt" (default) or "google" (Google's AI Overview). limit: how many questions to return, 1 to 20. Default 20. country: market, e.g. "Italy". Defaults to "United States". chat_gpt only has United States data; use platform "google" elsewhere. language: language code, e.g. "it". Defaults to "en", the only option for chat_gpt.	No	Yes	Yes	Yes
`get_check_history`Read back your own earlier analyze_citation_coverage results for a domain, newest first, to show progress over time or to confirm whether a change (a rewrite, a new mention somewhere) moved anything. Use this for 'has our citation status changed since last week' without spending units. Pass `page` to follow ONE page's citations over time instead of the whole domain's. Costs 0 quota units: it only reads Vantage's own record of your checks, never the data provider. Results are kept 180 days per API key and are only visible to that key. Returns: {"domain", "keyword" (or null for every keyword), "page" (the URL asked about, or null), "checks" (newest first: {"keyword", "engine", "country", "language", "samples", "cited_runs", "best_rank", "mentioned_runs", "checked_at", "cited_pages" (your page URLs cited in that check, or null for a check saved before page tracking), and with `page` set, "page_cited" (true/false, or null when that check did not record pages))}, and with `page` set also "times_cited" (checks that cited that page) and "checks_with_page_data". Args: domain: the bare domain the checks were run for, e.g. "example.com". keyword: optional, only this keyword's history. limit: how many rows to return, 1 to 200. Default 50. page: optional full URL of one page on the domain, e.g. "https://example.com/pricing". Matched ignoring "www.", the scheme, a trailing slash and any #fragment.	Read back your own earlier analyze_citation_coverage results for a domain, newest first, to show progress over time or to confirm whether a change (a rewrite, a new mention somewhere) moved anything. Use this for 'has our citation status changed since last week' without spending units. Pass `page` to follow ONE page's citations over time instead of the whole domain's. Costs 0 quota units: it only reads Vantage's own record of your checks, never the data provider. Results are kept 180 days per API key and are only visible to that key. Returns: {"domain", "keyword" (or null for every keyword), "page" (the URL asked about, or null), "checks" (newest first: {"keyword", "engine", "country", "language", "samples", "cited_runs", "best_rank", "mentioned_runs", "checked_at", "cited_pages" (your page URLs cited in that check, or null for a check saved before page tracking), and with `page` set, "page_cited" (true/false, or null when that check did not record pages))}, and with `page` set also "times_cited" (checks that cited that page) and "checks_with_page_data". Args: domain: the bare domain the checks were run for, e.g. "example.com". keyword: optional, only this keyword's history. limit: how many rows to return, 1 to 200. Default 50. page: optional full URL of one page on the domain, e.g. "https://example.com/pricing". Matched ignoring "www.", the scheme, a trailing slash and any #fragment.	No	Yes	Yes	Yes
`get_supported_options`List which AI engines, platforms and markets each Vantage tool accepts, and the per-call limits, so you can pick valid arguments before spending any quota. Use this to answer 'does Vantage cover Gemini in Italy' or 'what can I pass as engine or platform'. Costs 0 quota units - a fixed list, it never touches the data provider. Returns: {"live_answer": {"tools" (analyze_citation_coverage, analyze_citation_structure, analyze_citation_structure_batch, analyze_citation_gap), "engines" (the values for `engine`), "country" (any country name the provider accepts; for perplexity one of "perplexity_countries" or a 2-letter ISO code), "language" (language code), "max_samples", "max_keywords_per_call"}, "tracked_data": {"tools" (find_citation_leaders, analyze_citation_trend, find_cited_questions), "platforms" (the values for `platform`), "chat_gpt_market" (the only market ChatGPT has tracked data for), "other_markets" (where to get the rest), "max_trend_months", "max_compare_domains" ({"find_citation_leaders", "analyze_citation_trend"})}, "default_market" ({"country", "language"})}. A market the provider does not cover comes back as a refunded error, never a charge.	List which AI engines, platforms and markets each Vantage tool accepts, and the per-call limits, so you can pick valid arguments before spending any quota. Use this to answer 'does Vantage cover Gemini in Italy' or 'what can I pass as engine or platform'. Costs 0 quota units - a fixed list, it never touches the data provider. Returns: {"live_answer": {"tools" (analyze_citation_coverage, analyze_citation_structure, analyze_citation_structure_batch, analyze_citation_gap), "engines" (the values for `engine`), "country" (any country name the provider accepts; for perplexity one of "perplexity_countries" or a 2-letter ISO code), "language" (language code), "max_samples", "max_keywords_per_call"}, "tracked_data": {"tools" (find_citation_leaders, analyze_citation_trend, find_cited_questions), "platforms" (the values for `platform`), "chat_gpt_market" (the only market ChatGPT has tracked data for), "other_markets" (where to get the rest), "max_trend_months", "max_compare_domains" ({"find_citation_leaders", "analyze_citation_trend"})}, "default_market" ({"country", "language"})}. A market the provider does not cover comes back as a refunded error, never a charge.	No	Yes	Yes	Yes
`get_usage`Check how much of this billing period's quota is left, before spending any of it. Use this to answer 'how many checks do I have left' or to decide whether a batch call will fit before running it. Costs 0 quota units - this never touches the paid data provider, it only reads Vantage's own record of what has been used. Returns: {"tier", "period" (YYYY-MM), "units_used", "units_limit", "units_remaining"}. find_citation_leaders and find_cited_questions cost 10 units/call, analyze_citation_trend 10 per domain looked up; analyze_citation_structure (and its batch form, per keyword), analyze_citation_coverage (per keyword) and analyze_citation_gap cost 1, times `samples` where a tool takes it. get_check_history, check_page_readiness and get_supported_options cost 0. stdio/local-dev mode (no HTTP access token) has no metering at all - this returns tier "unmetered" with no real limit in that case.	Check how much of this billing period's quota is left, before spending any of it. Use this to answer 'how many checks do I have left' or to decide whether a batch call will fit before running it. Costs 0 quota units - this never touches the paid data provider, it only reads Vantage's own record of what has been used. Returns: {"tier", "period" (YYYY-MM), "units_used", "units_limit", "units_remaining"}. find_citation_leaders and find_cited_questions cost 10 units/call, analyze_citation_trend 10 per domain looked up; analyze_citation_structure (and its batch form, per keyword), analyze_citation_coverage (per keyword) and analyze_citation_gap cost 1, times `samples` where a tool takes it. get_check_history, check_page_readiness and get_supported_options cost 0. stdio/local-dev mode (no HTTP access token) has no metering at all - this returns tier "unmetered" with no real limit in that case.	No	Yes	Yes	Yes

Details

Registry status: Active
Published: Nov 14, 2026
Updated: Nov 14, 2026
Website: https://vantagemcp.dev/⁠ (opens in a new tab)
Repository: https://github.com/vantagemcp/vantage-mcp⁠ (opens in a new tab)
Categories: Uncategorized
GDPR: Not declared

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