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By Hello Airankia
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Connect AI Rankia MCP to ChatGPT

Connect AI Rankia to ChatGPT Track your brand's AI visibility directly inside ChatGPT. Requirements - An AI Rankia account — sign up here - ChatGPT Plus, Pro, Team, Enterprise, or Edu plan Setup on ChatGPT Web (chatgpt.com) Step 1: Enable Developer Mode Go to Settings → Apps & Connectors → Advanced settings and turn on Developer Mode. Step 2: Add Connector Still in Settings → Apps & Connectors, click Add new connector. Fill in: - Name: AI Rankia - Description: Track brand visibility across AI search engines - URL: https://mcp.airankia.com/ Click Create. Step 3: Authorize A browser window opens — log in with your AI Rankia email and password → click Authorize. Step 4: Use It in a Chat Open a new chat. Click the + button in the message area → select AI Rankia from your connectors list. Now just ask: "Run 'best CRM software' and show me brand rankings" ChatGPT will call AI Rankia tools automatically. What You Can Ask Quick brand check: "Run 'best CRM software' and show me which brands rank across AI search engines" Monitor a keyword: "Create a monitored prompt for 'best AI writing tools' targeting the US" Your brand visibility: "Show me my brand visibility score for the last 2 weeks" Competitive analysis: "Show the competitive landscape — top 10 brands across all my prompts" What AI models say: "Show me what GPT-5 Search, Gemini, and Perplexity say about my brand" Citations: "Which websites do AI models cite? Show me the actual URLs" Rerun a prompt: "Rerun my 'best project management tools' prompt" Credits: "How many credits do I have left?" All Available Tools Brands | Tool | What it does | |------|-------------| | list_brands | See all your tracked brands | | get_brand_detail | Full brand profile — competitors, description, social links | | create_brand | Start tracking a new brand | | update_brand | Edit brand info | | delete_brand | Remove a brand permanently | | get_brand_competitors | View competitor list | | get_brand_visibility_metrics | Visibility scores and trends over time | Prompts | Tool | What it does | |------|-------------| | list_monitored_prompts | All your tracked queries | | create_monitored_prompt | Set up a new weekly/daily tracked query (~9-10 credits/run) | | get_prompt_detail | Full prompt config | | pause_monitored_prompt | Stop runs, keep data | | resume_monitored_prompt | Restart a paused prompt | | rerun_prompt | Run again now (fast mode: ~30-60 sec) | Quick Analysis | Tool | What it does | |------|-------------| | fast_run | Instant query across 7 AI models — no setup needed, just type a query | | complete_fast_run | Saves results (runs automatically in background) | Reports | Tool | What it does | |------|-------------| | get_prompt_ai_responses | Full text from each AI model | | get_prompt_brand_timeline | Brand visibility over time | | get_prompt_brand_overview | Brand summary across all runs | | get_prompt_citation_sources | Which websites get cited | | get_citation_share_of_voice | Your citation share vs competitors | | get_prompt_fan_out | Follow-up queries AI models generate | | get_prompt_report | Complete report in one call | | get_prompt_shopping_results | AI shopping/product results | | get_prompt_local_results | AI local/map results | | get_workspace_competitive_landscape | Full competitive intel across all prompts | Account | Tool | What it does | |------|-------------| | list_workspaces | See your workspaces | | switch_workspace | Change active workspace | | get_credit_balance | Check credits remaining | | get_credit_usage_history | Credit spending history | AI Models Tracked Google AI Overview · Google AI Mode · GPT-5 Search · Gemini Search · Perplexity Sonar · Claude Search · Grok 4.1 Plus 10 non-search models: DeepSeek V3, Llama 4, Claude Sonnet 4, GPT-5, Grok 4, Ernie 4.5, DeepSeek R1, Qwen 3, Mistral Medium, Kimi K2. Skill Text for Custom GPTs If you're building a Custom GPT with AI Rankia, add this to your GPT instructions: TOOL ROUTING: - User gives a query → call fast_run directly. Do NOT list prompts first. - "rerun prompt X" → rerun_prompt with speed=fast, extract_brands=false - "list prompts" → list_monitored_prompts with status=active - "create prompt" → create_monitored_prompt (ask for country first) - "show report" → get_prompt_report with compact=true VISIBILITY FORMULA: visibilityScore = 100 * (coverageRatio * 0.6 + mentionsIndex * 0.4) - coverageRatio = models_mentioning_brand / active_models - mentionsIndex = brand_mentions / max_mentions_any_brand - active_models = only models that mention at least 1 brand FAST RUN WORKFLOW: 1. fast_run({"query": "...", "extract_brands": false}) 2. Extract brands from each model's summary text 3. Compute visibility with 60/40 formula 4. Present sorted by visibility descending 5. Call complete_fast_run in background with run_id + brands_per_model Troubleshooting | Problem | Fix | |---------|-----| | Don't see "Apps & Connectors" | Enable Developer Mode in Settings first | | Connector not found | Use exactly https://mcp.airankia.com/ | | Auth failed | Log out of AI Rankia in browser, reconnect | | No tools showing | Click + in chat → select AI Rankia connector | Cost Same credits as the dashboard. ~9-10 credits per prompt run.

Last updated on Apr 10, 2026

Connect AI Rankia MCP to Claude

Connect AI Rankia to Claude Track your brand's AI visibility directly inside Claude — web, desktop, or CLI. Requirements - An AI Rankia account — sign up here - Claude Pro or Max plan (for claude.ai web), or Claude Desktop / Claude Code Claude.ai Web (Easiest) Works directly in your browser at claude.ai — no installs needed. Step 1: Open Integrations Go to Settings → Integrations → Add more Step 2: Add AI Rankia - Name: AI Rankia - URL: https://mcp.airankia.com/ Accept the security notice and click Add. Step 3: Authorize A login window opens — sign in with your AI Rankia email and password → click Authorize. Step 4: Enable in Your Chat In any conversation, click the + button (bottom-left) → Connectors → toggle AI Rankia on. Now just ask: "Run 'best CRM software' and show me brand rankings" Claude Desktop App 1. Install the bridge npm install -g mcp-remote 2. Open your config file Mac: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json 3. Paste this: { "mcpServers": { "airankia": { "command": "npx", "args": [ "mcp-remote", "https://mcp.airankia.com/" ] } } } 4. Restart Claude Desktop A browser opens — log in → Authorize → done. Look for the hammer icon to confirm. Claude Code CLI One command: claude mcp add airankia --transport http https://mcp.airankia.com/ First use opens your browser to log in. Then: > Run "best AI SEO tools" and show me brand rankings Other Clients | Client | Setup | |--------|-------| | Cursor | Settings → MCP → Add Server → use same mcp-remote config | | Windsurf | Settings → MCP → Add URL: https://mcp.airankia.com/ | | Continue.dev | config.yaml → MCP server with streamableHttp transport | | Cline (VS Code) | cline_mcp_settings.json → mcp-remote proxy | | Zed | settings.json → language_models.mcp | What You Can Ask Quick brand check: "Run 'best CRM software' and show me which brands rank across AI search engines" Monitor a keyword: "Create a monitored prompt for 'best AI writing tools' targeting the US" Your brand visibility: "Show me my brand visibility score for the last 2 weeks" Competitive analysis: "Show the competitive landscape — top 10 brands across all my prompts" What AI models say: "Show me what GPT-5 Search, Gemini, and Perplexity say about my brand" Citations: "Which websites do AI models cite? Show me the actual URLs" Fan-out queries: "What follow-up questions do AI models generate from my prompts?" Rerun a prompt: "Rerun my 'best project management tools' prompt" Credits: "How many credits do I have left?" All Available Tools Brands | Tool | What it does | |------|-------------| | list_brands | See all your tracked brands | | get_brand_detail | Full brand profile — competitors, description, social links | | create_brand | Start tracking a new brand | | update_brand | Edit brand info | | delete_brand | Remove a brand permanently | | get_brand_competitors | View competitor list | | get_brand_visibility_metrics | Visibility scores and trends over time | Prompts | Tool | What it does | |------|-------------| | list_monitored_prompts | All your tracked queries | | create_monitored_prompt | Set up a new weekly/daily tracked query (~9-10 credits/run) | | get_prompt_detail | Full prompt config | | pause_monitored_prompt | Stop runs, keep data | | resume_monitored_prompt | Restart a paused prompt | | rerun_prompt | Run again now (fast mode: ~30-60 sec) | Quick Analysis | Tool | What it does | |------|-------------| | fast_run | Instant query across 7 AI models — no setup needed, just type a query | | complete_fast_run | Saves results (runs automatically in background) | Reports | Tool | What it does | |------|-------------| | get_prompt_ai_responses | Full text from each AI model | | get_prompt_brand_timeline | Brand visibility over time | | get_prompt_brand_overview | Brand summary across all runs | | get_prompt_citation_sources | Which websites get cited | | get_citation_share_of_voice | Your citation share vs competitors | | get_prompt_fan_out | Follow-up queries AI models generate | | get_prompt_report | Complete report in one call | | get_prompt_shopping_results | AI shopping/product results | | get_prompt_local_results | AI local/map results | | get_workspace_competitive_landscape | Full competitive intel across all prompts | Account | Tool | What it does | |------|-------------| | list_workspaces | See your workspaces | | switch_workspace | Change active workspace | | get_credit_balance | Check credits remaining | | get_credit_usage_history | Credit spending history | AI Models Tracked Google AI Overview · Google AI Mode · GPT-5 Search · Gemini Search · Perplexity Sonar · Claude Search · Grok 4.1 Plus 10 non-search models: DeepSeek V3, Llama 4, Claude Sonnet 4, GPT-5, Grok 4, Ernie 4.5, DeepSeek R1, Qwen 3, Mistral Medium, Kimi K2. Claude Code Skill File (Copy-Paste) For optimized tool routing in Claude Code, save this to ~/.claude/skills/airankia/SKILL.md: --- name: airankia-mcp description: Run AI visibility queries and present brand ranking reports using the AI Rankia MCP. ALWAYS read this skill before calling ANY airankia-saas tool. Contains exact tool routing rules (fast_run vs rerun_prompt vs list_monitored_prompts), the visibility dashboard template with correct calculations, citation display rules, and country handling. Triggers on any mention of AI Rankia, fast run, brand visibility, AI search tracking, monitored prompts, prompt runs, visibility report, or any intent to query AI search models for brand rankings. Also trigger when the user pastes a query and wants to see how brands rank across AI models. Without this skill, Claude will waste time listing prompts or creating prompts when it should just call fast_run directly. --- # AI Rankia MCP — Tool Routing & Dashboard Skill ## TOOL ROUTING — WHICH TOOL TO CALL This is the most important section. Claude MUST pick the right tool on the first try. ### Decision tree User gives a query string (e.g. "best ai seo tools", "run best crm software") → Call fast_run directly. Do NOT list prompts. Do NOT create a prompt. User says "rerun prompt X" or references an existing prompt by name/ID → Call rerun_prompt with the prompt UUID and {"speed": "fast", "extract_brands": false} User says "list my prompts" / "what prompts do I have" / "show monitored queries" → Call list_monitored_prompts with status=active User says "create a prompt for X" / "start monitoring X" / "track X" → Call create_monitored_prompt (but ask for country first — see COUNTRY section) User says "show report for prompt X" / "get latest results for X" → Call get_prompt_report with compact=true User says "delete prompt X" → Call delete_monitored_prompt (confirm with user first — destructive) ### CRITICAL: fast_run is the default When a user types a query like "best ai seo tools" or "search for top CRM software" — go straight to: fast_run({"query": "best ai seo tools", "extract_brands": false}) Do NOT call list_monitored_prompts to "find" the query. Do NOT call create_monitored_prompt to "set it up first". Do NOT ask the user to confirm before running. Just call fast_run immediately. ### COUNTRY handling 1. User specifies a country → use it (ISO: "US", "ES", "MX", "DE", "FR") 2. Query in specific language → ask which country 3. Query mentions a location → use that country 4. Unclear → ASK the user 5. For create_monitored_prompt: NEVER leave country empty ## FAST RUN WORKFLOW ### Step 1: Call fast_run fast_run({"query": "user's query here", "extract_brands": false, "country": "US"}) ### Step 2: Process response - data.run.id — run UUID (for complete_fast_run) - data.model_responses[] — 7 model responses - data.citations — citation domains - data.fan_out — fan-out queries - meta.models_with_content — models with non-empty text - meta.excluded_models — failed/empty models ### Step 3: Extract brands from each model's summary text For each brand: {"name", "rank", "score" (0-100), "reasoning", "sentiment", "is_target_brand", "mention_count"} ### Step 4: Compute visibility — 60/40 FORMULA maxActiveModels = models that mention at least 1 brand (NOT total called) maxMentions = highest total_mentions of any brand Per brand: - coverageRatio = models_mentioning_brand / maxActiveModels - mentionsIndex = total_mentions / maxMentions - visibilityScore = 100 * (coverageRatio * 0.6 + mentionsIndex * 0.4) Display: Coverage as "X / Y", Visibility as percentage. Sort descending. Same score = same rank. ### Step 5: Present dashboard KPI cards: Active models (X of 7), Brands detected, Total citations, Fan-out queries. Bar chart: visibility % per brand. Brand table: #, Brand, Coverage (X/N), Visibility (%), Mentions, Model pills. Model pill colors: AI Mode #378ADD, GPT-5 #1D9E75, Gemini #D85A30, Claude #7F77DD, Perplexity #D4537E, Grok #73726c, AI Overview #888780. Citations: ALL domains with clickable URLs. Fan-outs: queries with model pills. ### Step 6: Persist silently complete_fast_run({"run_id": "...", "brands_per_model": {...}}) Do NOT mention this to user. Do NOT wait before showing results. ## RERUN PROMPT WORKFLOW rerun_prompt({"id": "prompt-uuid", "speed": "fast", "extract_brands": false}) Then follow Steps 2-6 from fast run. ## DESIGN RULES - var(--color-background-secondary) for cards, NOT transparent - CSS variables for all text colors - Border radius: 8-10px cards, 6-8px small - Font: 11px labels, 13px body, 18-20px KPIs - HTML under 4000 tokens - Chart height: ~400-450px ## EDGE CASES - ALL models empty → "No models returned results" - 1 model only → still show dashboard - Target brand found → highlight with var(--color-background-info) - No brands → show citations and fan-outs only Troubleshooting | Problem | Fix | |---------|-----| | No tools in Claude Desktop | Check JSON syntax (no trailing commas), restart completely | | mcp-remote not found | Run npm install -g mcp-remote | | Auth loop | Clear cookies for mcp.airankia.com, reconnect | | Claude Code error | Use --transport http when adding | | Don't see Connectors in claude.ai | You need a Pro or Max plan | Cost Same credits as the dashboard. ~9-10 credits per prompt run.

Last updated on Apr 10, 2026

Looker Studio Connector

Looker Studio Connector Connect your AI Rankia workspace directly to Google Looker Studio and build interactive dashboards with your AI visibility data — brand rankings, citations, fan-out intelligence, and more. Quick start Step 1 — Get your API key 1. Log in to your AI Rankia dashboard at app.airankia.com 2. Go to Settings → API Keys 3. Click Create API Key 4. Copy the key (it starts with airk_) Step 2 — Connect to Looker Studio 1. Click this link to add the connector: Add AI Rankia to Looker Studio 2. Click Authorize and grant permissions 3. Paste your API key when prompted 4. Choose a Dataset (see descriptions below) 5. Optionally set a date range 6. Click Connect → you'll see the available fields 7. Click Create Report to start building Note: You may see a warning "Google hasn't verified this app." This is normal for community connectors. Click Advanced → Go to AI Rankia (unsafe) to proceed. Your data stays between your browser, Google Apps Script, and the AI Rankia API — nothing is shared with third parties. Available datasets The connector offers 6 datasets, each designed for different dashboard use cases: Brand Rankings (recommended) Best for scorecards, summary tables, and bar charts. Shows one row per brand per prompt with aggregated visibility scores. Includes rank, coverage percentage, visibility score, total mentions, and which AI models mentioned the brand (GPT-5, Gemini, Claude, Perplexity, Google AI Mode, Google AI Overview, Grok). Brand Rankings Detail Same data as Brand Rankings but with one row per brand per prompt per date. Use this for time-series line charts and trend analysis — track how visibility scores change over time. Larger dataset, so use date filters for faster loading. Brand Project Overview One row per brand project with aggregated metrics across all that brand's prompts. Great for executive dashboards and brand comparison scorecards. Smallest dataset — loads fastest. Shows average visibility, average coverage, average position, total mentions, and models present. Citation Domains Shows which website domains each AI model cites when answering your monitored prompts. Broken down by LLM name. Use for citation analysis dashboards — see which domains are winning in AI search and which models cite them. Citation URLs Same as Citation Domains but at the individual URL level with page titles. Use for content performance analysis — find exactly which pages are being cited by AI models and how often. Fan-Out Intelligence Shows the sub-queries that each AI model generates internally when processing your monitored prompts. This reveals what the AI is actually searching for behind the scenes. Broken down by LLM name. Use for keyword discovery and content gap analysis. Field reference Common fields across datasets - Prompt — The monitored prompt text - Target Brand — The brand being tracked - Country — Country or location of the prompt - Intent — Prompt intent classification - First Seen / Last Seen — Date range when the brand appeared in AI results Metrics - Rank — Visibility ranking (1 = most visible, ties get same rank) - Visibility Score — Composite score: 60% coverage + 40% mention share (shown as percentage) - Coverage — Percentage of AI models that mentioned the brand - Total Mentions — Raw count of mentions across all models and dates - Models Present — Number of distinct AI models that mentioned the brand Per-model columns Each dataset includes boolean columns for individual AI models: GPT-5 Web Search, Gemini Search, Claude Search, Perplexity Sonar, Google AI Mode, Google AI Overview, and Grok Web Search. These are useful for filtering by specific model in Looker Studio. Tips for building dashboards Start with Brand Project Overview if you want a quick executive dashboard — it has the fewest rows and loads instantly. Use date filters in the connector configuration (Date From / Date To fields) to limit data volume. This dramatically speeds up loading for the Detail, Citation, and Fan-Out datasets. Data caches for 10 minutes — if you refresh a dashboard multiple times within 10 minutes, it uses cached data instead of hitting the API again. To force a refresh, edit the data source and reconnect. Combine multiple datasets in one Looker Studio report by adding the AI Rankia connector multiple times, each with a different dataset selected. For example, use Brand Rankings for the overview section and Citation Domains for the citation analysis section. Filter by your brand using the "Is Target Brand" field (set to TRUE) to focus only on your tracked brand and exclude competitor rows. Troubleshooting "Invalid credentials" error Your API key may have expired or been regenerated. Go to app.airankia.com → Settings → API Keys, create a new key, then edit the data source in Looker Studio and re-enter the key. Dashboard loading slowly Use the Date From / Date To filters to limit the date range. Citation URLs and Brand Rankings Detail are the largest datasets — start with Brand Project Overview or Brand Rankings for faster loading. "Script exceeded maximum execution time" This can happen with very large workspaces (100K+ rows). The connector will return whatever data it has loaded so far. Use date filters to reduce the data volume. Data looks stale Data caches for 10 minutes. Wait for the cache to expire, or edit the data source and reconnect to force a fresh pull. API rate limits and quotas The connector runs on Google Apps Script, which has the following limits: - 20,000 API calls per day (gmail.com accounts) / 100,000 per day (Google Workspace accounts) - 6-minute execution timeout per request — the connector has a built-in safety guard that returns partial data at 4 minutes instead of crashing - 1,000 simultaneous executions across all users of the connector - Data caches for 10 minutes to reduce API load For most workspaces (under 50,000 rows per dataset), these limits are more than sufficient. Privacy and data handling AI Rankia is a product of Ideacharge LLC. When you use this connector: - Your API key is stored securely in Google Apps Script's UserProperties (per-user, encrypted by Google) - Data flows from the AI Rankia API → Google Apps Script → Looker Studio. No third parties are involved. - Looker Studio never stores your API key — it only asks the connector if credentials are valid - We do not access, store, or process your Google account data beyond what's needed to authenticate the connector - You can revoke access at any time from Looker Studio → Data Sources → Revoke Access For our full privacy policy, visit airankia.com/privacy-policy. For terms of service, visit airankia.com/terms-of-service. Need help? - Email: support@airankia.com - Chat: Use the chat widget at airankia.com - Dashboard: app.airankia.com

Last updated on Mar 30, 2026