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vantage-mcp

analyze_citation_trend

Read-onlyIdempotent

Track a domain's AI citation count month over month to reveal whether its visibility in AI search is trending upward or downward.

Instructions

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'.

Read-only: no side effects, safe to retry. Costs 1 quota unit/call (free tier: 3 checks/month total across all tools).

Returns: {"domain", "platform", "months" (list of {"year", "month", "mentions" (int, 0 for a month with no tracked citations - a real measured zero, not a gap), "ai_search_volume"}, oldest to newest), "trend": {"direction" ("up"/"down"/"flat"/"no_data"), "earliest_mentions", "latest_mentions"}}.

Use check_ai_visibility instead if you only need the current count, not how it's changed over time.

Args: domain: bare domain to check, e.g. "example.com" (no https://, no www). 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. months: how many recent months of history to return. Defaults to 6, capped at 13 - DataForSEO's historical data only goes back to 2025-08-01.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYes
monthsNo
platformNochat_gpt
Behavior5/5

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

Annotations already include readOnlyHint and idempotentHint, but the description adds valuable context: quota costs (1 unit/call, free tier limit), real measured zeros versus gaps, and data source limitations (DataForSEO begins 2025-08-01). No contradictions with annotations.

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

Conciseness5/5

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

The description is long but every sentence earns its place—purpose, usage, returns, parameters, alternatives, and constraints are all included without fluff. It is front-loaded with the core purpose and structured logically.

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 only basic schema, the description provides a full return format, parameter constraints, behavioral notes, and tool alternatives. It is complete enough for an agent to invoke correctly without ambiguity.

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%, but the description fully explains each parameter: domain format (bare, no https/www), platform options with defaults and unsupported values, and months default/cap with reason. This exceeds schema documentation and provides crucial context.

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 description clearly states the tool's purpose: tracking how a domain's AI-citation count changes month over month, with a specific verb ('track') and resource ('domain's AI-citation count'). It explicitly contrasts with check_ai_visibility for current counts, distinguishing it from sibling tools.

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?

The description provides explicit when-to-use guidance ('answer is our AI visibility improving'), names an alternative tool (check_ai_visibility) for a different need, and notes platform limitations (Perplexity and Gemini unavailable). This gives the agent clear decision criteria.

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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