vantage-mcp

Find who AI cites for a question

find_citation_leaders

Read-onlyIdempotent

Identify which domains dominate AI-answer citations for a keyword, and compare where your domain or up to 10 competitors rank in those results.

Instructions

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 cover them for this check. compare_domain: optional bare domain to look up in the results (exact match against the registrable domain, e.g. "notion.so" will not match "mynotion.so.example.com"). country: market to check, e.g. "Italy". Defaults to "United States". chat_gpt only has data for the United States; use platform "google" for any other country. language: language code, e.g. "it". Defaults to "en" (the only option for chat_gpt). compare_domains: optional list of up to 10 bare domains to rank side by side, e.g. ["notion.so", "asana.com", "monday.com"]. Same exact-match rule as compare_domain.

Input Schema

Name	Required	Default
`country`	No	United States
`keyword`	Yes
`language`	No	en
`platform`	No	chat_gpt
`compare_domain`	No
`compare_domains`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv1.10.0Oct 16, 2026

addedInput schema / properties / compare_domains

Added value: +{
+  "anyOf": [
+    {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Compare Domains"
+}

Changed2 schema fields changedv1.7.0Sep 23, 2026

addedInput schema / properties / country

Added value: +{
+  "default": "United States",
+  "title": "Country",
+  "type": "string"
+}

addedInput schema / properties / language

Added value: +{
+  "default": "en",
+  "title": "Language",
+  "type": "string"
+}

Addedv1.5.2Aug 22, 2026

TDQS

A4.7/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations cover safety (readOnly, idempotent, non-destructive), yet the description adds substantial context beyond them: exact quota cost (10 units/call, 30-unit free tier = up to 3 calls), retry safety, and crucially the caveat that absence from top_domains is not evidence of zero citations. This is genuinely useful behavioral disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose and usage framing are front-loaded, and the long return-value section is justified because there is no output schema. A few lines are slightly redundant (re-stating read-only/retry safety already in annotations), so it is dense rather than perfectly tight, but nearly every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and 0% schema coverage across 6 params, the description compensates fully: it spells out the full return shape, per-parameter semantics, platform/country/language constraints, and how it relates to a sibling metric. An agent has everything needed to call and interpret it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must carry the load and does: it documents keyword, platform (enumerating 'chat_gpt'/'google' with defaults and coverage limits), country (defaults plus chat_gpt-US-only constraint), language (defaults/constraint), and both compare_domain/compare_domains with exact-match rules and a 10-domain cap. Nothing material is left undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Find which domains dominate AI-answer citations for a topic/keyword') and immediately illustrates the questions it answers. It also actively differentiates itself from sibling analyze_citation_structure by explaining the measurement differs ('tracked mention corpus' vs 'single live answer').

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives concrete usage framing ('who's winning AI search for this topic', 'is my competitor cited more than me') and routes the agent away from analyze_citation_structure by naming the measurement difference. It does not give explicit when-not-to-use conditions or a full sibling decision table, but the context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Find who AI cites for a question

Instructions

Input Schema

Schema Changelog

TDQS

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