A full breakdown of Qwairy's MCP server: what each tool does, how it compares to Visiblie, Omnia, and Foglift, and why native MCP matters for GEO strategy.
Generative Engine Optimization is no longer optional. Brands that aren't measuring their presence in ChatGPT, Perplexity, Claude, Gemini, and Copilot are flying blind.
Most GEO solutions today offer dashboards, reports, and exports. But there's a fundamental limitation: the data lives in a separate tool, disconnected from where strategy actually happens.
Qwairy solves this differently. Instead of asking you to go to the data, Qwairy brings GEO data directly into your AI environment via a native MCP integration.
This article walks through what the Qwairy MCP is, what it can do, and why it changes the way SEOs, marketers, and CMOs can work with AI visibility data.
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Qwairy v1.17: AI Assistant & Ads Monitoring for Everyone
Launch your agency practice with pitch workspaces and a public directory. Run full GEO audits on any competitor with PDF export. Per-prompt monitoring frequency, redesigned Social Insights, and an AI Assistant now open to every user.
Qwairy v1.16: Smarter Metrics, Deeper Insights
My Website gives you a unified page inventory with AI citation data, GEO scores, and AI Readiness. Brand Associations reveal HOW AI describes you across 6 dimensions. Plus: Prompt Discover, Sentiment Analysis, Competitor Influence & 45+ Languages.
MCP (Model Context Protocol) is an open standard that lets AI assistants like Claude connect directly to external tools and data sources.
Here is a dashboard we built with a single prompt.
Instead of copy-pasting data into prompts or switching between tabs, an AI can call a live data source mid-conversation and use the results to reason, analyze, and take action.
For GEO, this is a game-changer. AI visibility data is only valuable when you can act on it quickly.
With an MCP GEO tool, you can ask natural-language questions about your brand's performance in AI systems — and get live, structured answers — without ever leaving your AI workspace.
Qwairy was built with this workflow in mind. Its MCP server is not an afterthought or a third-party connector. It's a native integration that exposes Qwairy's full data layer directly to compatible AI clients.
Qwairy's MCP server exposes over 20 tools. Here's what each one does — and why it matters to your GEO strategy.
list_brands
Returns all brands monitored in your Qwairy account, with their IDs and primary domains. The starting point for any workflow — no manual lookup required.
get_overview
Your cockpit view. Delivers a full summary: number of monitored prompts, AI answers collected, competitors detected, sources cited, global GEO scores, and trend versus the previous period. One call to understand where you stand.
get_brand_performance
Aggregated visibility metrics — mention rate, source citation rate, share of voice, and average sentiment — filterable by date range and AI provider. Essential for weekly reporting and trend detection.
get_visibility_trend
Day-by-day (or week-by-week) AI visibility over up to 365 days: mentions, source citations, visibility rate, sentiment, and average position. The trend direction (up/down/stable) is computed automatically from the first vs. second half of the period.
get_sentiment_trend
Sentiment scored 0–100 on a daily basis, alongside mention rate and average position. Detect narrative shifts before they become reputation problems.
get_competitor_comparison
Head-to-head: your brand vs. top competitors. Mentions, sentiment, position — with a win/loss breakdown and the identification of your biggest threat. Perfect for positioning decisions and competitive briefs.
get_competitors
Full list of competitors detected in AI answers, ranked by mention frequency. Relationship-classified: DIRECT, INDIRECT, or ECOSYSTEM. Filter by type to understand where competition is coming from.
See your mentions across ChatGPT, Claude and Perplexity in real time, the moment buyers ask.
get_topics
GEO performance broken down by topic (keyword groups). Each topic returns a GEO score, mention rate, citation rate, share of voice, and number of monitored prompts. Instantly tells you which content areas are winning — and which are invisible in AI answers.
get_content_opportunities
The most actionable tool for content teams. Surfaces monitored prompts where competitors are mentioned but your brand is not, and highlights low-visibility questions. A direct input to content prioritization.
get_keyword_triggers
Identifies which keywords and question types most reliably trigger brand mentions in AI answers. Breaks down by trigger rate and question type (informational, transactional, comparative). Crucial for shaping content angle and keyword targeting.
get_provider_breakdown
Visibility metrics per AI engine: ChatGPT, Claude, Perplexity, Gemini, Copilot, Grok, and others. Total answers, mention count, visibility rate, sentiment, and average position — per provider. Spot which AI systems are working for you and which are ignoring you.
get_matrix
A visibility matrix crossing topics (or prompts, or funnel stages) against AI providers. Each cell contains a GEO score, sub-scores, brand ranking, and top competitor. The most comprehensive single view of your brand's AI footprint.
get_brand_perception
The latest perception snapshot: sentiment score, alignment score (how well the AI narrative matches your actual positioning), and consistency score (across providers). Includes a SWOT-style breakdown — strengths, weaknesses, opportunities, threats as inferred from AI answers.
get_perception_history
Tracks how perception scores evolve over time. Useful for measuring the impact of content or PR changes on how AI models describe your brand.
get_source_domains
Lists third-party domains that AI providers cite when answering your monitored prompts — ranked by citation count, classified by type (media, forums, institutional, commercial, etc.). Know which third-party sources shape AI answers about you.
get_query_fan_out
When AI systems like Perplexity or Copilot process a prompt, they silently expand it into multiple web search queries. This tool surfaces those derived queries, your brand visibility on each, and which competitors appear. An unprecedented view into how AI systems interpret search intent around your brand.
get_technical_status
Checks your website's AI-readiness: robots.txt, llms.txt, sitemap.xml, blocked AI crawlers (GPTBot, ClaudeBot, PerplexityBot, etc.), and open technical issues by severity. The bridge between GEO strategy and technical SEO.
get_shopping_insights
For brands with products, this surfaces your presence in AI shopping results: which stores, which products, ratings — and gaps where competitors appear in shopping cards but you don't.
get_local_insights
For location-based brands, this returns the local businesses AI providers recommend when answering your monitored prompts.
search_documentation
Searches Qwairy's own documentation — definitions, methodology, scoring logic, best practices — directly from within your AI assistant.
With Qwairy's MCP connected to an AI client, a GEO workflow that used to take 30 minutes of dashboard navigation can happen in a single conversation:
"What's our AI visibility trend over the last 90 days, and which topics are pulling us down?"
The AI calls get_visibility_trend, then get_topics — and returns a synthesized analysis with the relevant data, already contextualized. No export. No pivot table. No context-switching.
Or:
"Where are our biggest content gaps versus competitors, and what should we write first?"
The AI combines get_content_opportunities and get_competitor_comparison to produce a prioritized content brief on the spot.
This is what a native MCP GEO tool enables: strategy at the speed of conversation.
See your mentions across ChatGPT, Claude and Perplexity in real time, the moment buyers ask.
Not all MCP integrations are equal. Qwairy's MCP server was designed alongside the platform — every tool maps directly to a core analytical function, with consistent parameters, clean data structures, and full coverage of the Qwairy data model.
This means AI assistants can reason across tools, chain calls logically, and deliver coherent analysis — rather than just returning raw JSON for you to interpret manually.
It also means the integration is maintained as the product evolves, not dependent on a third-party connector that might lag behind.
Qwairy's MCP is available to all Qwairy users. To connect it:
https://mcp.qwairy.co as an MCP serverFull setup documentation is available at docs.qwairy.co.
Several GEO platforms now offer MCP integrations — Visiblie, Omnia, and Foglift among them.
Qwairy is the only MCP GEO tool that exposes the full analytical stack from brand perception and competitor SWOT to funnel-level matrices and query fan-out in a format designed for marketing strategy, not just engineering pipelines.
For teams whose GEO workflows happen inside AI assistants like Claude rather than inside a code editor, Qwairy's MCP is the most complete option available today.
Every tool available in Qwairy's MCP server, organized by category:
Visibility & Performance: get_overview · get_brand_performance · get_visibility_trend · get_sentiment_trend · get_provider_breakdown
Competitive Intelligence: get_competitor_comparison · get_competitors
Content & Topic Strategy: get_topics · get_content_opportunities · get_keyword_triggers · get_matrix
Brand Perception: get_brand_perception · get_perception_history
Source & Citation Analysis: get_source_domains
Advanced & Emerging: get_query_fan_out · get_technical_status · get_shopping_insights · get_local_insights
Account & Setup: list_brands · search_documentation
All tools are accessible via the single MCP endpoint at https://mcp.qwairy.co.
GEO data is only useful if it's accessible when decisions get made. Qwairy's MCP integration closes the gap between measurement and action — putting 20+ live GEO tools directly inside your AI workflows.
For SEOs who want to understand which topics are invisible in AI answers, marketers who need to track how AI models perceive their brand, or CMOs comparing performance across ChatGPT, Perplexity, and Gemini — the Qwairy MCP is built for that work.
It's not a dashboard you check. It's a data layer you work with. *Want to see it in action? *Try Qwairy →
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