> ## Content Index > Fetch the complete content index at: https://buildwire.ai/llms.txt > Use this file to discover other available public pages before exploring further. # 🎯 People Also Ask (PAA) Scraper + Content Ideas Generator - URL: https://buildwire.ai/blog/people-also-ask-paa-scraper-content-ideas-generator/ - Published: 2025-11-25T13:46:03.000Z - Updated: 2026-06-14T07:31:25.000Z - Description: An intelligent automation workflow that transforms Google Search Console data into actionable content clusters using AI - Author: Rananjay Raj - Tags: n8n, #beehiiv, #Migrated-1781422191461, #Import 2026-06-14 13:00 ![](https://buildwire.ai/content/images/2026/06/gemini_generated_image_zg4e5yzg4e5yzg4e__1_-png-t-1764072877.jpg) ## 🌟 What This Workflow Does This n8n workflow automates the entire content research process: 1. **Pulls search data** from your Google Search Console 2. **Scrapes Google results** for "People Also Ask" questions 3. **Analyzes questions** using OpenAI GPT to identify patterns 4. **Clusters insights** into coherent content topics 5. **Saves to Notion** for your content team **Result:** In minutes, transform raw search data into a prioritized content roadmap that your team can start creating from immediately. --- ## 📊 Technical Architecture ### Workflow Flow Schedule Trigger (Weekly) ↓ Google Search Console Node (Fetch site queries, impressions, clicks, rankings) ↓ normalizeSearchConsole Function (Filter by volume, dedup, quality checks) ↓ SearchAPI Integration (Scrape live Google results) ↓ extractPAA Function (Parse FAQ questions from results) ↓ OpenAI GPT-4 Analysis (Cluster questions → topics) ↓ formatForNotion Function (Structure data for database) ↓ Notion Database Write (Save content ideas) ### Core Components **Data Ingestion Layer** - Google Search Console: Real keyword performance metrics - SearchAPI: Live SERP scraping with People Also Ask data - Built-in deduplication and filtering **Processing Layer** - JavaScript function nodes for data transformation - Configurable filtering thresholds (impressions, position) - Fallback mechanisms for incomplete data **AI Analysis Layer** - OpenAI GPT-4 for semantic clustering - Temperature: 0 (deterministic, consistent results) - Structured JSON output validation - Question parsing from multiple SERP elements **Output Layer** - Notion database integration - Structured properties for filtering and sorting - Ready-to-use content cluster format ## 🔌 Integrations Required ### 1\. Google Search Console (Required) - **Connection:** OAuth 2.0 - **Scope:** Read-only access to search performance data - **Data:** Queries, impressions, clicks, CTR, ranking position ### 2\. SearchAPI.io (Required) - **Connection:** API Key authentication - **Purpose:** Scrape Google search results and "People Also Ask" sections - **Features:** Location/language targeting, pagination - **Pricing:** Free tier available (100 searches/month), paid plans for volume ### 3\. OpenAI API (Required) - **Model:** GPT-4 (or gpt-3.5-turbo for cost savings) - **Purpose:** Semantic clustering of questions into content topics - **Settings:** Temperature 0 for deterministic output - **Pricing:** \~$0.03-0.10 per workflow execution ### 4\. Notion (Required) - **Connection:** OAuth 2.0 or API token - **Purpose:** Store and organize content ideas - **Features:** Database page creation with properties - **Setup:** notion.so → Create integration → Share database with bot ### 5\. n8n (Required) - **Version:** Self-hosted or cloud.n8n.io - **Requirements:** Node.js 16+ (for self-hosted) - **Storage:** Minimal (executions stored in database) - **Scaling:** Runs scheduled 1x/week, no concurrent load ## 💡 Use Cases ### 1\. Content Gap Analysis 🎯 **Scenario:** Your SEO team wants to know what content to create next. **How it works:** - Workflow pulls all queries ranking #3-10 (good keyword difficulty) - Extracts "People Also Ask" questions for those topics - Groups into content clusters by topic - Notion shows which topics have the most questions **Result:** Content brief with 15+ article ideas, ranked by search demand **Perfect for:** Blog strategy, pillar content planning, niche expansion --- ### 2\. FAQ Page Generation 📝 **Scenario:** You need to create a comprehensive FAQ page for your product. **How it works:** - Search your brand name + product terms - Capture all "People Also Ask" questions - AI groups similar questions together - Export structured FAQs with answers **Result:** Organized FAQ outline ready for content writers **Perfect for:** Support pages, product pages, category pages --- ### 3\. Long-Form Content Outlining 📄 **Scenario:** Writing a 3,000-word guide on a topic your audience searches for. **How it works:** - Pull questions for your target keyword - AI clusters them into natural sections (Introduction, Advanced, Troubleshooting, etc.) - Each cluster becomes an H2 heading with subsections - Questions suggest internal linking opportunities **Result:** Battle-tested content outline based on real search demand **Perfect for:** Pillar pages, ultimate guides, how-to articles --- ### 4\. Competitor Content Intelligence 🔍 **Scenario:** Analyze competitor domains to find content gaps. **How it works:** - Run the workflow for competitor keywords - See what questions they're ranking for - Identify questions they DON'T answer (opportunity gap) - Create content around those gaps **Result:** Competitive advantage with untapped content opportunities **Perfect for:** Competitive positioning, market analysis, niche domination --- ### 5\. Product FAQ & Support Optimization 🛠️ **Scenario:** Reduce support tickets by creating answer content. **How it works:** - Search for your product category questions - Cluster by support ticket category - Create knowledge base articles for top clusters - Link from product pages to reduce support load **Result:** Better UX, fewer support tickets, improved SEO **Perfect for:** SaaS products, e-commerce, service providers --- ### 6\. SEO Strategy Development 📈 **Scenario:** Build a data-driven content strategy for the quarter. **How it works:** - Weekly workflow runs automatically - Accumulate content ideas in Notion over time - Filter by search volume, intent, difficulty - Identify content pillars and supporting articles - Plan quarterly roadmap **Result:** Quarter-long content calendar based on real search data **Perfect for:** Marketing agencies, in-house teams, content platforms ## 🗄️ Output Structure Each item saved to Notion contains: | **Field** | **Type** | **Example** | | ------------- | --------- | ----------------------------------------------------- | | Name (title) | Text | "Starter & Feeding" | | Keyword | Text | "how to make sourdough" | | Description | Rich Text | "Guide to creating and maintaining a healthy starter" | | Intent | Text | "informational" / "transactional" / "navigational" | | Priority | Number | 1-5 (5 = highest) | | Questions | Rich Text | "What is sourdough starter?..." | | Article Title | Text | "Sourdough Starter: Create & Maintain" | ## 🚀 Setup Requirements ### Prerequisites (You Need These) 1. **Google Search Console Access** - Must be verified property owner - At least 90 days of historical data - Not required: API key (OAuth handles authentication) 2. **SearchAPI.io Account** - Free tier: 100 searches/month - Paid tier: $19+ for 10,000 searches/month - Create account: searchapi.io - Get API key from dashboard 3. **OpenAI API Account** - Requires paid account (minimum $5 credit) - API key from: platform.openai.com - Estimated cost: $0.03-0.10 per workflow run 4. **Notion Workspace** - Create blank database (or duplicate template) - Create internal integration/bot - Generate API token or set up OAuth 5. **n8n Instance** - Cloud: Sign up at cloud.n8n.io - Self-hosted: Docker or Node.js installation - Either option works; cloud is simpler for beginners ### n8n Setup Steps **Step 1: Create Credentials** In n8n UI: Credentials → New Credential → Select Service ✓ Google Search Console OAuth2 ✓ OpenAI API ✓ SearchAPI ✓ Notion **Step 2: Import Workflow** ``` Import → From File → Select JSON Choose: PAA_Scraper_Content_Ideas_Generator_SANITIZED.json ``` **Step 3: Configure Nodes** ``` Each node with a ⚙️ icon needs configuration: Query search analytics: - Site URL: https://yourwebsite.com - Page path: /blog/ (or / for entire site) - Attach: Google Search Console credential Search google: - Attach: SearchAPI credential - Location: Set to your target country - Language: Set to your target language Message a model: - Model: gpt-4 (recommended) or gpt-3.5-turbo - Temperature: 0 (for consistency) - Attach: OpenAI credential Create a database page: - Database ID: From your Notion URL - Attach: Notion credential ``` **Step 4: Test** ``` Click "Execute Workflow" button Check execution logs for errors Verify data appears in Notion Once working, click "Activate" to schedule ``` ### Configuration Parameters **Data Filtering (in normalizeSearchConsole node)** ``` MIN_IMPRESSIONS = 25 // Only queries with 25+ monthly searches MAX_POSITION = 8 // Only queries ranking in top 8 DEDUPE = true // Remove duplicate keywords ``` Adjust these for your needs: - Blog content: MIN\_IMPRESSIONS = 50 - FAQ content: MIN\_IMPRESSIONS = 10 - Competitive analysis: MIN\_IMPRESSIONS = 5 **Schedule Settings** ``` Default: Every Monday at 9 AM Change in Schedule Trigger node: cron: "0 9 * * 1" // Monday 9 AM Other options: "0 0 * * *" // Daily at midnight "0 9 * * 1-5" // Weekdays at 9 AM "0 */6 * * *" // Every 6 hours ``` ## 🎨 Customization Options ### 1\. Customize the Clustering Prompt In the "Message a model" node, edit the system prompt: **Default:** Clusters questions into 3-5 topic groups **Custom Examples:** **For Blog Content (detailed outlines):** ``` "Task: Create detailed content outlines with H2 and H3 sections..." // Adds: suggested_outline_structure, word_count_estimate ``` **For FAQ (short answers):** ``` "Task: Group questions for concise FAQ answers..." // Adds: answer_length, difficulty_level ``` **For Product Documentation:** ``` "Task: Organize questions by product feature..." // Adds: feature_category, use_case, priority_for_docs ``` ### 3\. Filter by Search Intent Add filtering in the AI analysis to focus on specific intent types: ``` // In formatForNotion node, add filter: const intentsToKeep = ["informational", "navigational"]; out = out.filter(item => intentsToKeep.includes(item.intent)); ``` **Intent Types:** - **Informational:** "How to", "What is", "Why" (70% of searches) - **Transactional:** "Buy", "Price", "Reviews" (15% of searches) - **Navigational:** "Brand + feature", "Site search" (15% of searches) ### 4\. Add Competitor Domain Analysis Instead of your own GSC, analyze competitor keywords: ``` // In normalizeSearchConsole, replace with: const competitorKeywords = [ "how to make sourdough", "sourdough starter maintenance", "sourdough troubleshooting" ]; // Then search for these keywords via SearchAPI ``` --- ### 5\. Multi-Language Support In the "Search google" node: ``` // Current: Bulgarian (bg) "languageSettings": { "hl": "bg" }, "locationSettings": { "gl": "bg" } // Change to your target language: "hl": "en", // English "gl": "us", // United States // Other options: "hl": "es", "gl": "es" // Spanish "hl": "de", "gl": "de" // German "hl": "fr", "gl": "fr" // French ``` --- ### 6\. Custom Notion Properties Edit the "Create a database page" node to save additional fields: **Add Trend Data:** ``` "trend_direction": "{{ ascending | descending }}", "recent_momentum": "{{ positive | neutral | negative }}" ``` **Add Competition Data:** ``` "competitor_coverage": "high | medium | low", "gap_opportunity": "{{ percentage }}" ``` **Add Creation Metadata:** ``` "content_type": "blog | faq | guide | tutorial", "estimated_length": "{{ word_count }}", "required_visuals": "{{ image_count }}" ``` --- ### 7\. Extend with Slack Notifications Add a Slack node after Notion write to notify your team: ``` New node: Slack → Send Message Message: "📝 {{ $json.topic_title }} - Priority {{ $json.priority }}" Channel: #content-ideas Frequency: Only send when priority >= 4 ``` --- ### 8\. Add Email Report Generation Instead of just saving to Notion, email a weekly summary: ``` New node: Gmail → Send Email To: team@yourcompany.com Subject: "Weekly Content Ideas - {{ items_count }} new topics" Body: "Priority topics this week: [list]" Include: HTML table with top 10 ideas ``` ## 🏆 Success Tips ✅ **Start small:** Run on single page/topic first, expand later ✅ **Monitor costs:** Check SearchAPI and OpenAI usage monthly ✅ **Iterate prompts:** If clustering isn't right, tweak the AI prompt ✅ **Create workflow:** Team reviews Notion → writers create → track progress ✅ **Measure results:** Track which content ideas get best engagement ✅ **Optimize schedule:** Run when your team reviews (e.g., Monday morning) ✅ **Archive old ideas:** Keep Notion clean by archiving completed tasks --- ## Full JSON Workflow: ``` { "name": "People Also Ask (PAA) Scraper + Content Ideas Generator", "nodes": [ { "parameters": { "rule": { "interval": [ {} ] } }, "name": "Schedule Trigger", "type": "n8n-nodes-base.scheduleTrigger", "typeVersion": 1, "position": [ 208, -112 ], "id": "10d286d2-d473-4fb6-9be2-857e92f99890" }, { "parameters": { "functionCode": "// normalizeSearchConsole - robust + dedupe + configurable filters\n// Edit the threshold values below or set them as environment variables in n8n\nconst MIN_IMPRESSIONS = (process.env.MIN_IMPRESSIONS !== undefined) ? Number(process.env.MIN_IMPRESSIONS) : 25;\nconst MAX_POSITION = (process.env.MAX_POSITION !== undefined) ? Number(process.env.MAX_POSITION) : 8; // lower is better (1 is top)\nconst DEDUPE = true; // set to false if you want duplicates preserved\n\n// Gather incoming jsons\nconst inputJsons = items.map(i => i.json);\nlet rows = [];\n\nif (inputJsons.length === 1 && Array.isArray(inputJsons[0].rows)) {\n // shape: { rows: [ { keys: [...], clicks, impressions, ctr, position }, ... ] }\n rows = inputJsons[0].rows.map(r => ({\n query: Array.isArray(r.keys) ? r.keys[0] : r.keys,\n clicks: r.clicks ?? 0,\n impressions: r.impressions ?? 0,\n ctr: r.ctr ?? 0,\n position: r.position ?? null\n }));\n} else {\n // shape: [ { query, clicks, impressions, ctr, position }, ... ] OR similar flattened items\n rows = inputJsons.map(r => ({\n query: r.query ?? (Array.isArray(r.keys) ? r.keys[0] : r.keys) ?? null,\n clicks: r.clicks ?? 0,\n impressions: r.impressions ?? 0,\n ctr: r.ctr ?? 0,\n position: r.position ?? null\n }));\n}\n\n// Normalize strings and filter out empty queries\nrows = rows.map(r => ({\n query: typeof r.query === 'string' ? r.query.trim() : (r.query == null ? '' : String(r.query).trim()),\n clicks: Number(r.clicks) || 0,\n impressions: Number(r.impressions) || 0,\n ctr: Number(r.ctr) || 0,\n position: (r.position === null || r.position === undefined) ? null : Number(r.position)\n})).filter(r => r.query.length > 0);\n\n// Apply dedupe\nif (DEDUPE) {\n const seen = new Set();\n rows = rows.filter(r => {\n const q = r.query.toLowerCase();\n if (seen.has(q)) return false;\n seen.add(q);\n return true;\n });\n}\n\n// Apply metric filters: keep only rows with impressions >= MIN_IMPRESSIONS and position <= MAX_POSITION (if position exists)\nrows = rows.filter(r => {\n if (r.impressions < MIN_IMPRESSIONS) return false;\n if (r.position !== null && !Number.isNaN(r.position) && r.position > MAX_POSITION) return false;\n return true;\n});\n\n// Build output items\nconst out = rows.map(r => ({\n json: {\n query: r.query,\n clicks: r.clicks,\n impressions: r.impressions,\n ctr: r.ctr,\n position: r.position,\n source: 'search-console'\n }\n}));\n\nreturn out;\n" }, "name": "normalizeSearchConsole", "type": "n8n-nodes-base.function", "typeVersion": 1, "position": [ 656, -112 ], "id": "10cf3e74-3213-45d9-8cc9-d00182516837", "alwaysOutputData": true }, { "parameters": { "functionCode": "const input = items[0].json || {};\nconst out = {...input};\n\n// Determine position (try multiple places)\nlet position = input.position ?? input.search_information?.position ?? input.current ?? null;\nif ((position === undefined || position === null) && Array.isArray(input.organic_results) && input.organic_results.length > 0) {\n const first = input.organic_results[0];\n position = (typeof first.position === 'number') ? first.position : (first.position ? Number(first.position) : 1);\n}\nif (position === undefined) position = null;\n\n// Extract questions (same logic you already have)\nconst candidates = [];\nfunction pushIfQuestion(text) {\n if (!text || typeof text !== 'string') return;\n const t = text.trim();\n if (t.length < 5) return;\n if (t.includes('?') || /^(what|how|why|when|where|who|can|do|does|is|are|should|which|will|как|кога|какво|защо|къде|кой|може|дали)\\b/i.test(t)) {\n candidates.push(t);\n return;\n }\n if (t.length < 80 && /^[\\w\\s\\p{L}]{3,80}$/u.test(t) && /\\b(как|кога|какво|защо|къде|кой|може|дали)\\b/i.test(t)) {\n candidates.push(t);\n }\n}\n\nif (Array.isArray(input.related_searches)) {\n for (const r of input.related_searches) {\n if (typeof r === 'string') pushIfQuestion(r);\n else if (r && r.query) pushIfQuestion(r.query);\n else if (r && r.title) pushIfQuestion(r.title);\n }\n}\nif (input.answer_box) {\n if (typeof input.answer_box === 'object') {\n pushIfQuestion(input.answer_box.question || input.answer_box.title || input.answer_box.snippet);\n } else if (typeof input.answer_box === 'string') pushIfQuestion(input.answer_box);\n}\nif (input.ai_overview) {\n const ao = input.ai_overview;\n if (Array.isArray(ao)) {\n for (const a of ao) pushIfQuestion(typeof a === 'string' ? a : (a.title || a.snippet || a.text));\n } else pushIfQuestion(ao.title || ao.snippet || ao.text || ao);\n}\nif (Array.isArray(input.organic_results)) {\n for (const res of input.organic_results) {\n if (res.title) pushIfQuestion(res.title);\n if (res.snippet) pushIfQuestion(res.snippet);\n }\n}\n\nlet uniq = Array.from(new Set(candidates.map(s => s.trim())));\nif (uniq.length === 0 && Array.isArray(input.organic_results)) {\n for (const res of input.organic_results.slice(0, 10)) {\n if (res.title && res.title.length < 160) uniq.push(res.title.trim());\n if (uniq.length >= 10) break;\n }\n}\nconst questions = uniq.slice(0, 50);\nif (questions.length === 0 && (input.search_parameters?.q || input.q || input.query_displayed)) {\n const seed = input.search_parameters?.q || input.q || input.query_displayed;\n questions.push(`What does \"${seed}\" mean?`);\n}\n\nconst resultItem = {\n json: {\n ...out,\n position,\n questions\n }\n};\n\nreturn [ resultItem ];" }, "name": "extractPAA", "type": "n8n-nodes-base.function", "typeVersion": 1, "position": [ 1104, -176 ], "id": "270b3985-1724-4c85-ba45-fb392ce23573" }, { "parameters": { "functionCode": "/* formatForNotion — robust JSON extraction + keyword fallback */\n\nfunction extractJSONFromText(text) {\n const firstBracket = Math.min(\n ...['[','{'].map(ch => {\n const idx = text.indexOf(ch);\n return idx >= 0 ? idx : Infinity;\n })\n );\n if (firstBracket === Infinity) throw new Error(\"No JSON bracket found in text. Preview:\\n\" + (text||'').slice(0,1000));\n const openChar = text[firstBracket];\n const closeChar = openChar === '[' ? ']' : '}';\n const lastBracket = text.lastIndexOf(closeChar);\n if (lastBracket <= firstBracket) throw new Error(\"No matching close bracket in text. Preview:\\n\" + (text||'').slice(0,1000));\n const candidate = text.slice(firstBracket, lastBracket + 1);\n return JSON.parse(candidate);\n}\n\nfunction getRawTextFromItem(itemJson) {\n if (!itemJson) return '';\n // openai-like shapes\n if (itemJson.choices && Array.isArray(itemJson.choices) && itemJson.choices[0]) {\n const ch = itemJson.choices[0];\n if (ch.message && ch.message.content) {\n if (typeof ch.message.content === 'string') return ch.message.content;\n if (Array.isArray(ch.message.content)) {\n for (const seg of ch.message.content) if (typeof seg === 'string') return seg;\n for (const seg of ch.message.content) if (seg && typeof seg.text === 'string') return seg.text;\n }\n }\n if (typeof ch.text === 'string') return ch.text;\n // fallback stringify\n try { return JSON.stringify(ch); } catch(e){}\n }\n // other shape: output[0].content[0].text\n if (itemJson.output && Array.isArray(itemJson.output) && itemJson.output[0]) {\n const o0 = itemJson.output[0];\n if (o0.content && Array.isArray(o0.content) && o0.content[0]) {\n const c0 = o0.content[0];\n if (typeof c0.text === 'string') return c0.text;\n if (typeof c0 === 'string') return c0;\n }\n if (typeof o0.content === 'string') return o0.content;\n }\n // fallback: if any property looks like JSON string, return it\n for (const k of Object.keys(itemJson)) {\n const v = itemJson[k];\n if (typeof v === 'string' && (v.trim().startsWith('[') || v.trim().startsWith('{'))) return v;\n }\n // last resort\n try { return JSON.stringify(itemJson); } catch(e){ return ''; }\n}\n\n// -------------- begin processing -------------------\nconst itemJson = items[0] && items[0].json ? items[0].json : {};\nconst rawText = getRawTextFromItem(itemJson);\n\n// parse model output\nlet parsed;\ntry {\n parsed = extractJSONFromText(String(rawText));\n} catch (err) {\n const preview = {\n availableKeys: Object.keys(itemJson || {}).slice(0,50),\n rawPreview: String(rawText).slice(0,1200)\n };\n throw new Error('Failed to extract JSON from model output: ' + err.message + '\\nPreview keys: ' + JSON.stringify(preview));\n}\n\n// normalize clusters\nlet clusters;\nif (Array.isArray(parsed)) clusters = parsed;\nelse if (parsed && Array.isArray(parsed.clusters)) clusters = parsed.clusters;\nelse if (parsed && typeof parsed === 'object') clusters = [parsed];\nelse throw new Error('Parsed JSON not array/object: ' + typeof parsed);\n\n// try to discover keyword from parsed or from other places\nlet parsedKeyword = null;\n// if model returned top-level keyword or clusters contain keyword\nif (parsed && typeof parsed === 'object') {\n if (parsed.keyword && typeof parsed.keyword === 'string') parsedKeyword = parsed.keyword;\n else if (Array.isArray(parsed.clusters) && parsed.clusters.length > 0 && parsed.clusters[0].keyword) parsedKeyword = parsed.clusters[0].keyword;\n}\nif (!parsedKeyword && Array.isArray(clusters) && clusters.length > 0 && clusters[0].keyword) parsedKeyword = clusters[0].keyword;\n\n// fallback: try to pull from the inbound item (if upstream merging preserved them)\nlet inboundKeyword = itemJson.query || itemJson.q || (itemJson.search_parameters && itemJson.search_parameters.q) || itemJson.query_displayed || itemJson.keyword || null;\n\n// final resolvedKeyword function\nfunction resolveKeyword(c) {\n // cluster-level keyword > parsedKeyword > inboundKeyword > null\n return (c && c.keyword) || parsedKeyword || inboundKeyword || null;\n}\n\n// build output for Notion\nconst out = [];\nfor (const c of clusters) {\n out.push({\n json: {\n keyword: resolveKeyword(c),\n topic_title: c.topic_title || c.topic || null,\n suggested_article_title: c.suggested_article_title || c.title || null,\n short_description: c.short_description || c.summary || null,\n intent: c.intent || 'informational',\n priority: Number.isFinite(Number(c.priority)) ? Number(c.priority) : 3,\n questions: Array.isArray(c.questions) ? c.questions.join('\\n') : String(c.questions || '')\n }\n });\n}\n\nreturn out;\n" }, "name": "formatForNotion", "type": "n8n-nodes-base.function", "typeVersion": 1, "position": [ 1904, -112 ], "id": "13c286bd-30a9-4ac1-8c52-abdc1e3ee53f" }, { "parameters": {}, "name": "NoOp End", "type": "n8n-nodes-base.noOp", "typeVersion": 1, "position": [ 2352, -112 ], "id": "2654231a-cc0f-4ef7-b877-f2c7608b0668" }, { "parameters": { "operation": "getPageInsights", "siteUrl": "{{ $env.GSC_SITE_URL }}", "dateRangeMode": "last3mo", "rowLimit": 50, "dimensions": [ "query" ], "filters": { "filter": [ { "dimension": "page", "expression": "{{ $env.GSC_PAGE_PATH }}" } ] } }, "type": "n8n-nodes-google-search-console.googleSearchConsole", "typeVersion": 1, "position": [ 432, -112 ], "id": "14ce48f7-0a85-4a20-9264-fdc9c1fb229a", "name": "Query search analytics", "credentials": { "googleSearchConsoleOAuth2Api": { "id": "{{ $env.GSC_CREDENTIAL_ID }}", "name": "Google Search Console account" } } }, { "parameters": { "modelId": { "__rl": true, "value": "gpt-4", "mode": "list", "cachedResultName": "GPT-4" }, "responses": { "values": [ { "content": "=Input: {\n \"keyword\": \"{{ $json.search_parameters.q }}\",\n \"questions\": {{ JSON.stringify($json.questions || []) }},\n \"metrics\": {\n \"clicks\": {{ $json.clicks || 0 }},\n \"impressions\": {{ $json.impressions || 0 }},\n \"ctr\": {{ $json.ctr || 0 }},\n \"position\": {{ $json.position === null ? \"null\" : $json.position }}\n }\n}\n\nTask: Return strictly valid JSON — a root-level ARRAY named \"clusters\" (i.e. return only: [ {...}, {...} ]). For every cluster object, INCLUDE the same keyword from the Input exactly as provided, using the key \"keyword\". Each cluster object must have:\n\n- \"keyword\": string (must match Input.keyword)\n- \"topic_title\": string\n- \"questions\": [string,...]\n- \"suggested_article_title\": string\n- \"short_description\": string (20–40 words)\n- \"intent\": one of [\"informational\",\"transactional\",\"navigational\"]\n- \"priority\": integer 1-5\n\nConstraints:\n1. Return **only** the JSON array — no prose, no markdown.\n2. The model must echo Input.keyword into every cluster as the \"keyword\" field.\n3. Keep output compact and valid JSON.\n\nExample cluster element:\n{\n \"keyword\":\"how to make sourdough\",\n \"topic_title\":\"Starter & feeding\",\n \"questions\":[\"What is sourdough starter?\",\"How often to feed starter?\"],\n \"suggested_article_title\":\"Sourdough Starter: Create & Maintain a Healthy Starter\",\n \"short_description\":\"A concise how-to guide to start and maintain a healthy sourdough starter for reliable loaves.\",\n \"intent\":\"informational\",\n \"priority\":4\n}\n" }, { "role": "system", "content": "You are an assistant that clusters FAQ-style questions into coherent topic groups and returns valid JSON only. Do not emit any explanation, prose, or extra characters — only the JSON array requested. Be concise, factual, and consistent.\n" } ] }, "builtInTools": {}, "options": { "temperature": 0 } }, "type": "@n8n/n8n-nodes-langchain.openAi", "typeVersion": 2, "position": [ 1328, -176 ], "id": "ed351e67-e7b0-4ebe-a533-fc614ee11337", "name": "Message a model", "alwaysOutputData": false, "credentials": { "openAiApi": { "id": "{{ $env.OPENAI_CREDENTIAL_ID }}", "name": "OpenAi account" } } }, { "parameters": { "resource": "databasePage", "databaseId": { "__rl": true, "value": "{{ $env.NOTION_DATABASE_ID }}", "mode": "list", "cachedResultName": "New Topics", "cachedResultUrl": "https://www.notion.so/NOTION_DATABASE_ID" }, "title": "=New Topics", "propertiesUi": { "propertyValues": [ { "key": "Keyword|rich_text", "textContent": "={{ $json.keyword }}" }, { "key": "Name|title", "title": "={{ $json.topic_title }}" }, { "key": "Description|rich_text", "textContent": "={{ $json.short_description }}" }, { "key": "Intent|rich_text", "textContent": "={{ $json.intent }}" }, { "key": "Priority|rich_text", "textContent": "={{ $json.priority.toString() }}" }, { "key": "Questions|rich_text", "textContent": "={{ $json.questions }}" } ] }, "options": {} }, "type": "n8n-nodes-base.notion", "typeVersion": 2.2, "position": [ 2128, -112 ], "id": "10d06184-c37b-4aba-a0b4-fdfcab071c89", "name": "Create a database page", "credentials": { "notionApi": { "id": "{{ $env.NOTION_CREDENTIAL_ID }}", "name": "Notion account" } } }, { "parameters": { "q": "={{ $json.query }}", "locationSettings": { "gl": "bg" }, "languageSettings": { "hl": "bg" }, "searchOptions": {}, "timeFilters": {}, "pagination": { "num": "10" }, "advancedOptions": {}, "requestOptions": {} }, "type": "@searchapi/n8n-nodes-searchapi.searchApi", "typeVersion": 1, "position": [ 880, -112 ], "id": "fbf6db7d-d924-4812-8a7e-d504450f3d87", "name": "Search google", "credentials": { "searchApi": { "id": "{{ $env.SEARCHAPI_CREDENTIAL_ID }}", "name": "SearchApi account" } } }, { "parameters": {}, "type": "n8n-nodes-base.merge", "typeVersion": 3.2, "position": [ 1680, -112 ], "id": "d30f9e5e-a649-4f43-85b5-b05113c72f20", "name": "Merge" } ], "pinData": {}, "connections": { "Schedule Trigger": { "main": [ [ { "node": "Query search analytics", "type": "main", "index": 0 } ] ] }, "normalizeSearchConsole": { "main": [ [ { "node": "Search google", "type": "main", "index": 0 } ] ] }, "formatForNotion": { "main": [ [ { "node": "Create a database page", "type": "main", "index": 0 } ] ] }, "Query search analytics": { "main": [ [ { "node": "normalizeSearchConsole", "type": "main", "index": 0 } ] ] }, "extractPAA": { "main": [ [ { "node": "Message a model", "type": "main", "index": 0 } ] ] }, "Message a model": { "main": [ [ { "node": "Merge", "type": "main", "index": 0 } ] ] }, "Create a database page": { "main": [ [ { "node": "NoOp End", "type": "main", "index": 0 } ] ] }, "Search google": { "main": [ [ { "node": "extractPAA", "type": "main", "index": 0 }, { "node": "Merge", "type": "main", "index": 1 } ] ] }, "Merge": { "main": [ [ { "node": "formatForNotion", "type": "main", "index": 0 } ] ] } }, "active": false, "settings": { "executionOrder": "v1" }, "versionId": "1.0.0-sanitized", "meta": { "templateCredsSetupCompleted": false }, "tags": [ { "id": "seo", "name": "SEO" }, { "id": "content-research", "name": "Content Research" }, { "id": "ai-analysis", "name": "AI Analysis" } ] } ``` --- **Your content research is about to get a lot faster. Let's go!** 🚀