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Documentation Index

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The Conductor MCP includes AI search data through the following tools:
  • ai_brand_insights
  • ai_citation_insights
These tools provide insights across the following dimensions and topics.

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Brands

This data reflects which brands (yours and competitors’) get mentioned in AI search answers across Conductor’s supported AI search engines. For an LLM, this is the “awareness” layer: it lets you answer “who’s showing up and how often?” with market share, share-of-voice, and breakdowns by topic, persona, intent, and search engine. It’s essential for competitive positioning and identifying where your brand is invisible.

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Citations

This data reflects which URLs and domains AI engines cite as sources when answering prompts. Where brand data answers “am I being talked about?”, Citations answers “am I being trusted as a source?” For an LLM, this is the “authority” layer. It’s critical for diagnosing why a brand might be mentioned frequently but not driving traffic, and for URL-level drill-down to see which specific pages earn citations for which prompts. This is where content strategy recommendations actually get actionable.

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Sentiment

This data captures the quality of brand mentions: positive, neutral, negative—plus category-level breakdowns (quality, price, ethics, experience, etc.) and source attribution (which domains are driving which sentiment). For an LLM, this turns raw mention counts into narrative. You can surface actual quotes, identify reputation risks, and explain why a brand’s perception is shifting. Indispensable for PR and reputation analysis, not just visibility counting. The Conductor MCP includes traditional search data through the keyword_insights tool. This tool provides insights across the following dimensions and topics.

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Rankings

This data reflects how your website ranks in traditional search results for your tracked keywords over time. It includes keyword group data to aggregate visibility reporting. For an LLM, this is the “position” layer: it answers “where do I rank, and is it improving?” across the whole keyword set or any slice of it (by group or location). It’s the foundation for diagnosing visibility wins and losses and for prioritizing which keywords or markets need attention.

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Seasonality

This data captures monthly search volume trends for your tracked keywords, typically spanning the last 24 months. For an LLM, this is the “demand” layer. It lets the model explain why visibility or traffic shifts—separating ranking changes from changes in underlying search interest—and time content and campaign recommendations to seasonal peaks.

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Result type

This data reflects which SERP features each keyword’s results are made up of: Standard Links, People Also Ask, Local Pack, and other result types. For an LLM, this is the “opportunity” layer. It explains why ranking well doesn’t always drive clicks and surfaces where features like PAA or Local Pack are reshaping the page—turning rank data into concrete content-format and optimization recommendations.

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Competitive rankings

This data compares your rankings head-to-head against up to three competitor domains for your tracked keywords. For an LLM, this is the “benchmark” layer. It answers “who’s beating me, and where?” so the model can frame visibility in competitive terms, identify keywords where rivals are pulling ahead, and ground gap analysis in real position data.

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Keyword details

This data provides single-keyword deep dives: a rank snapshot, week-by-week rank history, monthly search volume trend, and the full SERP results for one keyword. For an LLM, this is the “drill-down” layer. When a user asks about one specific keyword, it lets the model move from account-wide summaries to the exact rank trajectory, seasonality, and live SERP for that term—where granular, actionable answers actually come from.

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Account Configuration

The Conductor MCP includes account configuration data through the account_configuration tool. This is the metadata layer in your data: what topics, prompts, brands, competitors, personas, intents, locales, and search engines you are tracking for a given account. For an LLM, this information grounds queries, preventing hallucinations and letting the model resolve fuzzy user references (“my UK brand,” “the retirement topic”) into the exact identifiers needed for data queries. Without it, every downstream queries could be filtering on data that doesn’t exist.