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vantagemcp

vantage-mcp

by vantagemcp
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Find questions AI cites your site for

find_cited_questions
Read-onlyIdempotent

Identify which questions people ask AI answer engines where your domain is already cited, sorted by ask volume. Use it to pick keywords before analyzing citation coverage or gaps.

Instructions

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 An empty list means the provider's tracked answers do not cite the domain, not that no answer anywhere does.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.

Input Schema

NameRequiredDescriptionDefault
limitNo
domainYes
countryNoUnited States
languageNoen
platformNochat_gpt

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.8.0

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already cover safety (readOnly, idempotent, openWorld, non-destructive), but the description adds substantial context beyond them: the 10 quota-unit cost and the 30-unit/month shared free tier, the fact that this is a tracked corpus rather than a live answer, and the crucial empty-list caveat ('not that no answer anywhere does'). None of this is available in the schema or annotations.

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?

Front-loaded with purpose, then cost, then return shape, then args — a sensible order, and the verbose return block is justified by the absence of an output schema. Slight redundancy ('most-asked first' appears in both the opening and the returns block) is the only waste.

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?

Given no output schema, five undocumented-in-schema parameters, and a metered API, the description supplies return field semantics, quota economics, and corpus-vs-live distinctions. Nothing an agent needs to call this correctly and interpret the result is missing.

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 description coverage is 0%, so the description carries the full burden and does: domain format (bare, no https:// or www, subdomains included), platform enum values with what they mean, limit bounds (1-20) with default, country default plus the platform constraint, and language default plus its chat_gpt restriction. This fully compensates for the empty schema descriptions.

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?

The opening sentence names a specific verb and resource ('find the questions people ask AI answer engines where a domain is already cited') plus the ordering ('most-asked first'). It also distinguishes itself from siblings by naming find_citation_leaders as the same measurement and analyze_citation_coverage/gap as the consumers of its output.

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

Usage Guidelines5/5

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

Explicitly states the use case in the user's own words ('what does ChatGPT already cite us for') and the downstream workflow (feed keywords to analyze_citation_coverage and analyze_citation_gap). It also routes platform/country choices ('chat_gpt only has United States data; use platform google elsewhere'), which is exactly the when-to-use guidance an agent needs.

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

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