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SEO and Keyword Data API

by com.duoleads.keywords
OverviewDiscussionsAdmin

AI answers mentioning a keyword (up to 20)

llm_mentions
Read-onlyIdempotent

AI answers that mention a brand or keyword: up to 20 questions answered by Google AI Overviews or ChatGPT that mention it, with AI search volume, first / last seen and the pages the answer cites (no answer text), in 94 countries (ChatGPT: US English), from AI search answers, $0.21 a call. Use when tracking AI-search visibility (GEO). No mention is an empty answer, still charged. Bad input is a free 400; a data source failure is not charged. Price: $0.21 per successful call, paid over x402 (USDC on Base); pass the payment in _meta["x402/payment"].

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "keyword": "<keyword>"
    },
    "name": "llm_mentions"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoAnswers to return, 1-20. Default 10.
countryNoOptional ISO 3166-1 alpha-2 code or country name, used when locationCode is absent (uk = gb).
keywordYesThe brand or keyword (at most 80 characters, up to 10 words).
platformNogoogle (AI Overviews, default) or chat_gpt (United States, en only).
languageCodeNoLanguage code (2-letter). Defaults to en. An unsupported location falls back to 2840 (United States) with en (or the requested language if the United States offers it); a supported location with a language it does not offer uses that location's default language (en if offered). Either way market.fallback is true and market.warnings says what was used.
locationCodeNoGoogle Ads location code (geo target ID). Defaults to 2840 (United States); an unsupported code falls back to 2840 (United States, market.fallback).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. Added

TDQS

A4.2/5.0
Behavior5/5

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

Far beyond the annotations, which already say readOnly/openWorld/idempotent: the text discloses that empty results still cost money, that bad input returns a free 400, that data-source failures are not charged, that answer text is excluded, and that payment must be supplied through _meta["x402/payment"]. Those are exactly the operational facts an agent needs before calling a paid endpoint.

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?

The core payload, the result shape, the charging rules, and the payment mechanism are front-loaded and dense with useful facts. It loses a point for redundancy, since '0.21 a call' appears in the opening sentence and is restated in the trailing Price sentence, and the title duplicates 'up to 20'.

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?

There is no output schema, yet the text describes what each result contains and what it omits (no answer text). For a paid, multi-platform, geo-scoped tool, the pricing, fallback, and per-call billing behavior are all spelled out, leaving nothing material an agent would need before calling it.

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

Parameters3/5

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

Schema coverage is 100%, so every parameter is already documented in the schema, including the platform enum, limit range with default of 10, and the location fallback behavior. The description adds only the 94-country scope and the ChatGPT-US-English restriction, which the schema largely covers too. Baseline 3 applies when the schema does the heavy lifting.

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 opens with a specific resource and scope: 'AI answers that mention a brand or keyword,' then enumerates exactly what comes back (AI search volume, first/last seen, cited pages) and the two platforms and 94-country coverage. That is enough for an agent to tell it apart from siblings like brand_mentions or google_search without opening a schema.

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

Usage Guidelines3/5

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

It supplies a intended context, 'Use when tracking AI-search visibility (GEO),' which implies when to reach for it. But it names no alternatives and no exclusions, so the agent is not told why it should pick this over brand_mentions or google_search for a brand-visibility question.

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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