Workflow Templates

Complete MCP server exposing 9 NPR Listening Service API operations to AI agents. ⚡ Quick Setup Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free? Join the community Import this workflow into your n8n instance Credentials Add NPR Listening Service credentials Activate the workflow to start your MCP server Copy the webhook URL from the MCP trigger node Connect AI agents using the MCP URL 🔧 How it Works This workflow converts the NPR Listening Service API into an MCP-compatible interface for AI agents. • MCP Trigger: Serves as your server endpoint for AI agent requests • HTTP Request Nodes: Handle API calls to https://listening.api.npr.org • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (9 total) 🔧 V2 (9 endpoints) • GET /v2/aggregation/{aggId}/recommendations: Get a set of recommendations for an aggregation independent of the user's lis... • GET /v2/channels: List Available Channels • GET /v2/history: Get User Ratings History • GET /v2/organizations/{orgId}/categories/{category}/recommendations: Get a list of recommendations from a category of content from an organization • GET /v2/organizations/{orgId}/recommendations: Get a variety of details about an organization including various lists of rec... • GET /v2/promo/recommendations: Get Recent Promo Audio • POST /v2/ratings: Submit Media Ratings • GET /v2/recommendations: Get User Recommendations • GET /v2/search/recommendations: Get Search Recommendations 🤖 AI Integration Parameter Handling: AI agents automatically provide values for: • Path parameters and identifiers • Query parameters and filters • Request body data • Headers and authentication Response Format: Native NPR Listening Service API responses with full data structure Error Handling: Built-in n8n HTTP request error management 💡 Usage Examples Connect this MCP server to any AI agent or workflow: • Claude Desktop: Add MCP server URL to configuration • Cursor: Add MCP server SSE URL to configuration • Custom AI Apps: Use MCP URL as tool endpoint • API Integration: Direct HTTP calls to MCP endpoints ✨ Benefits • Zero Setup: No parameter mapping or configuration needed • AI-Ready: Built-in $fromAI() expressions for all parameters • Production Ready: Native n8n HTTP request handling and logging • Extensible: Easily modify or add custom logic > 🆓 Free for community use! Ready to deploy in under 2 minutes.

Automated Cold Email Personalization This workflow automates the creation of highly personalized cold outreach emails by extracting lead data, scraping company websites, and leveraging AI to craft unique email components. This is ideal for sales teams, marketers, and business development professionals looking to scale their outreach efforts while maintaining a high degree of personalization. How It Works Generate Batches: The workflow starts by generating a sequence of numbers, defining how many leads to process in batches. Scrape Lead Data: It uses an external API (Apify) to pull comprehensive lead information, including contact details, company data, and social media links. Fetch Client Data: The workflow then retrieves relevant client details from your Google Sheet based on the scraped data. Scrape Company Website: The lead's company website is automatically scraped to gather content for personalization. Summarize Prospect Data: An OpenAI model analyzes both the scraped website content and the individual's profile data to create concise summaries and identify unique angles for outreach. Craft Personalized Email: A more advanced OpenAI model uses these summaries and specific instructions to generate the "icebreaker," "intro," and "value proposition" components of a personalized cold email. Update Google Sheet: Finally, these generated email components are saved back into your Google Sheet, enriching your lead records for future outreach. Google Sheet Structure Your Google Sheet must have the following exact column headers to ensure proper data flow: Email** (unique identifier for each lead) Full Name** Headline** LinkdIn** cityName** stateName** company/cityName** Country** Company Name** Website** company/businessIndustry** Keywords** icebreaker** (will be populated by the workflow) intro** (will be populated by the workflow) value\_prop** (will be populated by the workflow) Setup Instructions Add Credentials: In n8n, add your OpenAI API key via the Credentials menu. Connect your Google account via the Credentials menu for Google Sheets access. You will also need an Apify API key for the Scraper node. Configure Google Sheets Nodes: Select the Client data and Add email data to sheet nodes. For each, choose your Google Sheets credential, select your spreadsheet, and the specific sheet name. Ensure all column mappings are correct according to the "Google Sheet Structure" section above. Configure Apify Scraper Node: Select the Scraper node. Update the Authorization header with your Apify API token (Bearer KEY). In the JSON Body, set the searchUrl to your Apollo link (or equivalent source URL for lead data). Configure OpenAI Nodes: Select both Summarising prospect data and Creating detailed email nodes. Choose your OpenAI credential from the dropdown. In the Creating detailed email node's prompt, replace PUT YOUR COMPANY INFO HERE with your company's context and verify the target sector for the email generation. Verify Update Node: On the final Add email data to sheet node, ensure the Operation is set to Append Or Update and the Matching Columns field is set to Email. Customization Options 💡 Trigger: Change the When clicking 'Execute workflow' node to an automatic trigger, such as a **Cron node for daily runs, or a Google Sheets trigger when new rows are added. Lead Generation: Modify the **Code node to change the number of leads processed per run (currently set to 50). Scraping Logic**: Adjust the Scraper node's parameters (e.g., count) or replace the Apify integration with another data source if needed. AI Prompting: Experiment with the prompts in the **Summarising prospect data and Creating detailed email OpenAI nodes to refine the tone, style, length, or content focus of the generated summaries and emails. AI Models**: Test different OpenAI models (e.g., gpt-3.5-turbo, gpt-4o) in the OpenAI nodes to find the optimal balance between cost, speed, and output quality. Data Source/CRM**: Replace the Google Sheets nodes with integrations for your preferred CRM (e.g., HubSpot, Salesforce) or a database (e.g., PostgreSQL, Airtable) to manage your leads.

📊 Description Automate post-purchase workflows by instantly fetching successful Stripe payments, matching them to corresponding automation templates in Google Sheets, and sending customers personalized access emails using AI-generated content. This system ensures each buyer receives their digital template, password, and onboarding details automatically after payment. 💳📩🤖 What This Template Does Step 1: Triggers daily at 7:00 AM IST to fetch all successful payment charges from Stripe. ⏰ Step 2: Retrieves payment intent and product details for each successful charge to enrich context. 💰 Step 3: Validates required fields (order reference, product name, customer name, email). ✅ Step 4: Matches purchased product with the automation record in Google Sheets via AI lookup. 🔍 Step 5: Combines Stripe and Sheet data into one record, ensuring accuracy and completeness. 🔄 Step 6: Filters out already-processed customers to avoid duplicate sends. 🧮 Step 7: Generates a personalized thank-you email using Azure OpenAI (GPT-4o-mini) including access links, password, and onboarding tips. 💌 Step 8: Sends the email through Gmail to the customer automatically. 📧 Step 9: Logs each transaction and email delivery into Google Sheets for tracking and auditing. 📊 Key Benefits ✅ Fully automated Stripe-to-email delivery flow ✅ Zero manual intervention — instant template delivery ✅ AI-personalized HTML emails with customer details ✅ Centralized purchase logging and analytics ✅ Eliminates duplicates and ensures smooth customer experience Features Scheduled daily trigger (7:00 AM IST) Stripe API integration for payment and product details Google Sheets lookup for automation files and passwords GPT-powered email content generation Gmail API integration for delivery Google Sheets logging for audit trail Requirements Stripe API credentials Google Sheets OAuth2 credentials Gmail OAuth2 credentials Azure OpenAI API credentials Target Audience SaaS or digital product sellers using Stripe Automation template marketplaces Small teams delivering digital assets via email Businesses seeking instant customer fulfillment

Description: Automate your developer onboarding quality checks with this n8n workflow template. Whenever a new onboarding task is created in ClickUp, the workflow logs it to Google Sheets, evaluates its completeness using Azure OpenAI GPT-4o-mini, and alerts your team in Slack if critical details are missing. Perfect for engineering managers, DevOps leads, and HR tech teams who want to maintain consistent onboarding quality and ensure every developer gets the tools, credentials, and environment setup they need — without manual review. ✅ What This Template Does (Step-by-Step) ⚡ Step 1: Auto-Trigger on ClickUp Task Creation Listens for new task creation events (taskCreated) in your ClickUp workspace to initiate the audit automatically. 📊 Step 2: Log Task Details to Google Sheets Records essential task data — task name, assignee, and description — creating a central audit trail for all onboarding activities. 🧠 Step 3: AI Completeness Analysis (GPT-4o-mini) Uses Azure OpenAI GPT-4o-mini to evaluate each onboarding task for completeness across key areas: Tooling requirements Credential setup Environment configuration Instruction clarity Outputs: ✅ Score (0–100) ⚠️ List of Missing Items 💡 Suggestions for Improvement 🚦 Step 4: Apply Quality Gate Checks whether the AI-generated completeness score is below 80. Incomplete tasks automatically move to the alert stage for review. 📢 Step 5: Alert Team via Slack Sends a structured Slack message summarizing the issue, including: Task name & assignee Completeness score Missing checklist items Recommended next actions This ensures your team fixes incomplete onboarding items before they impact new hires. 🧠 Key Features 🤖 AI-driven task completeness scoring 📊 Automatic task logging for audit visibility ⚙️ Smart quality gate (score threshold < 80) 📢 Instant Slack alerts for incomplete tasks 🔄 End-to-end automation from ClickUp to Slack 💼 Use Cases 🎓 Audit onboarding checklists for new developers 🧩 Standardize environment setup and credential handover 🚨 Identify missing steps before onboarding deadlines 📈 Maintain onboarding consistency across teams 📦 Required Integrations ClickUp API – to detect new onboarding tasks Google Sheets API – to store audit logs and history Azure OpenAI (GPT-4o-mini) – to evaluate completeness Slack API – to alert the team on incomplete entries 🎯 Why Use This Template? ✅ Ensures every new developer receives a full, ready-to-start setup ✅ Eliminates manual checklist verification ✅ Improves onboarding quality and compliance tracking ✅ Creates a transparent audit trail for continuous improvement

🛡️ Jamf Policy Integrity Monitor 🎯 Overview A security-focused n8n workflow that monitors Jamf Pro policies for any unauthorized or accidental modification. It delivers configuration integrity and real-time visibility across managed Apple environments. ⚙️ Setup Instructions Add your Jamf Pro and Slack credentials in n8n. Import the workflow template. Configure your preferred schedule and alert channel. No coding required. The setup takes minutes. 🔍 How It Works The workflow connects to Jamf Pro API, detects configuration changes, and sends instant alerts to Slack. It maintains awareness of policy integrity while minimizing manual checks. The logic runs automatically in the background for continuous monitoring. 📢 Slack Notification Example :warning: Policy: Uninstall EDR modified :calendar: DateTime: Oct 5, 2025, 10:23:27 AM ✅ Why It Matters Jamf provides no built-in alerts when policies are modified. This workflow closes that visibility gap and gives your team instant awareness of policy changes without manual auditing. Ideal for security engineers, Jamf administrators, and compliance teams focused on operational assurance.

Who's it for? This template is perfect for educational institutions, coaching centers (like UPSC, GMAT, or specialized technical training), internal corporate knowledge bases, and SaaS companies that need to provide instant, accurate, and source-grounded answers based on proprietary documents. It's designed for users who want to leverage Google Gemini's powerful reasoning but ensure its answers are strictly factual and based only on their verified knowledge repository. How it works / What it does This workflow establishes a Retrieval-Augmented Generation (RAG) pipeline to build a secure, fact-based AI Agent. It operates in two main phases: 1. Knowledge Ingestion: When a new document (e.g., a PDF, lecture notes, or policy manual) is uploaded via a form or Google Drive, the Embeddings Google Gemini node converts the content into numerical vectors. These vectors are then stored in a secure MongoDB Atlas Vector Store, creating a private knowledge base. 2. AI Query & Response: A user asks a question via Telegram. The AI Agent uses the question to perform a semantic search on the MongoDB Vector Store, retrieving the most relevant, source-specific passages. It then feeds this retrieved context to the Google Gemini Chat Model to generate a precise, factual answer, which is sent back to the user on Telegram. This process ensures the agent never "hallucinates" or uses general internet knowledge, making the responses accurate and trustworthy. Requirements To use this template, you will need the following accounts and credentials: n8n Account Google Gemini API Key: For generating vector embeddings and powering the AI Agent. MongoDB Atlas Cluster: A free-tier cluster is sufficient, configured with a Vector Search index. Telegram Bot: A bot created via BotFather and a Chat ID where the bot will listen for and send messages. Google Drive Credentials (if using the Google Drive ingestion path). How to set up Set up MongoDB Atlas:** Create a free cluster and a database. Create a Vector Search Index on your collection to enable efficient searching. Configure Ingestion Path:** Set up the Webhook trigger for your "On form submission" or connect your Google Drive credentials. Configure the Embeddings Google Gemini node with your API Key. Connect the MongoDB Atlas Vector Store node with your database credentials, collection name, and index name. Configure Chat Path:** Set up the Telegram Trigger with your Bot Token to listen for incoming messages. Configure the Google Gemini Chat Model with your API Key. Connect the MongoDB Atlas Vector Store 1 node as a Tool within the AI Agent. Ensure it points to the same vector store as the ingestion path. Final Step:* Configure the Send a text message node with your *Telegram Bot Token and the Chat ID**. How to customize the workflow Change Knowledge Source:** Replace the Google Drive nodes with nodes for Notion, SharePoint, Zendesk, or another document source. Change Chat Platform:** Replace the Telegram nodes with a Slack, Discord, or WhatsApp Cloud trigger and response node. Refine the Agent's Persona:** Open the AI Agent node and edit the System Instruction to give the bot a specific role (e.g., "You are a senior UPSC coach. Answer questions politely and cite sources."). 💡 Example Use Case An UPSC/JEE/NEET coaching uploads NCERT summaries and previous year notes to Google Drive. Students ask questions in the Telegram group — the bot instantly replies with contextually accurate answers from the uploaded materials. The same agent can generate daily quizzes or concise notes from this curated content automatically.

Introduction Generates complete scientific papers from title and abstract using AI. Designed for researchers, automating literature search, content generation, and citation formatting. How It Works Extracts input, searches academic databases (CrossRef, Semantic Scholar, OpenAlex), merges sources, processes citations, generates AI sections (Introduction, Literature Review, Methodology, Results, Discussion, Conclusion), compiles document. Workflow Template Webhook → Extract Data → Search (CrossRef + Semantic Scholar + OpenAlex) → Merge Sources → Process References → Prepare Context → AI Generate (Introduction + Literature Review + Methodology + Results + Discussion + Conclusion via OpenAI) → Merge Sections → Compile Document Workflow Steps Input & Search: Webhook receives title/abstract; searches CrossRef, Semantic Scholar, OpenAlex; merges and processes references AI Generation: OpenAI generates six sections with in-text citations using retrieved references Assembly: Merges sections; compiles formatted document with reference list Setup Instructions Trigger & APIs: Configure webhook URL; add OpenAI API key; customize prompts Databases: Set up CrossRef, Semantic Scholar, OpenAlex API access; configure search parameters Prerequisites OpenAI API, CrossRef API, Semantic Scholar API, OpenAlex API, webhook platform, n8n instance Customization Adjust reference limits, modify prompts for research fields, add citation styles (APA/IEEE), integrate databases (PubMed, arXiv), customize outputs (DOCX/LaTeX/PDF) Benefits Automates paper drafting, comprehensive literature integration, proper citations

Description: Overview This workflow generates automated revenue and expense comparison reports from a structured Google Sheet. It enables users to compare financial data across the current period, last month, and last year, then uses an AI agent to analyze and summarize the results for business reporting. Prerequisites A connected Google Sheets OAuth2 credential. A valid DeepSeek AI API (or replaceable with another Chat Model). A sub-workflow (child workflow) that handles processing logic. Properly structured Google Sheets data (see below). Required Google Sheet Structure Column headers must include at least: Date, Amount, Type. Setup Steps Import the workflow into your n8n instance. Connect your Google Sheets and DeepSeek API credentials. Update: Sheet ID and Tab Name (already embedded in node: Get revenual from google sheet). Custom sub-workflow ID (in the Call n8n Workflow Tool node). Optionally configure chatbot webhook in the When chat message received node. What the Workflow Does Accepts date inputs via AI chat interface (ChatTrigger + AI Agent). Fetches raw transaction data from Google Sheets. Segments and pivots revenue by classification for: Current period Last month Last year Aggregates totals and applies custom titles for comparison. Merges all summaries into a final unified JSON report. Customization Options Replace DeepSeek with OpenAI or other LLMs. Change the date fields or cycle comparisons (e.g., quarterly, weekly). Add more AI analysis steps such as sentiment scoring or forecasting. Modify the pivot logic to suit specific KPI tags or labels. Troubleshooting Tips If Google Sheets fetch fails: ensure the document is shared with your n8n Google credential. If parsing errors: verify that all dates follow the expected format. Sub-workflow must be active and configured to accept the correct inputs (6 dates). SEO Keywords (ẩn hoặc mô tả ngầm): Google Sheets report, AI financial report, compare revenue by month, expense analysis automation, chatbot n8n report generator, n8n Google Sheet integration

Let AI agents fetch communicate with your Bubble app automatically. It connects direcly with your Bubble data API. This workflow is designed for teams building AI tools or copilots that need seamless access to Bubble backend data via natural language queries. How it works Triggered via a webhook from an AI agent using the MCP (Model-Chain Prompt) protocol. The agent selects the appropriate data tool (e.g., projects, user, bookings) based on user intent. The workflow queries your Bubble database and returns the result. Ideal for integrating with ChatGPT, n8n AI-Agents, assistants, or autonomous workflows that need real-time access to app data. Set up steps Enable access to your Bubble data or backend APIs (as needed). Create a Bubble admin token. Add your Bubble node/s to your n8n workflow. Add your Bubble admin token. Configer your Bubble node/s. Copy the generated webhook URL from the MCP Server Trigger node and register it with your AI tool (e.g., LangChain tool loader). (Optional) Adjust filters in the “Get an Object Details” node to match your dataset needs. Once connected, your AI agents can automatically retrieve context-aware data from your Bubble app, no manual lookups required.

How it works This workflow automatically scrapes commercial real estate listings from LoopNet and sends opportunity alerts to Telegram while logging data to Google Sheets. Key Steps Scheduled Trigger - Runs every 24 hours to collect fresh CRE market data AI-Powered Scraping - Uses ScrapeGraphAI to extract property information from LoopNet Market Analysis - Analyzes listings for opportunities and generates market insights Smart Notifications - Sends Telegram alerts only when investment opportunities are found Data Logging - Stores daily market metrics in Google Sheets for trend analysis Set up steps Setup time: 10-15 minutes Configure ScrapeGraphAI credentials - Add your ScrapeGraphAI API key for web scraping Set up Telegram connection - Connect your Telegram bot and specify the target channel Configure Google Sheets - Set up Google Sheets integration for data logging Customize the LoopNet URL - Update the URL to target specific CRE markets or property types Adjust schedule - Modify the trigger timing based on your market monitoring needs Keep detailed configuration notes in sticky notes inside your workflow

Description Automate B2B order invoicing by fetching orders from Airtable, validating paid B2B entries, creating Stripe customers and invoices, finalizing invoices, and logging structured invoice data into Google Sheets. This workflow ensures seamless B2B billing, centralized record-keeping, and reduces manual errors in financial operations. ⚡💳📊 What This Template Does Triggers hourly to check for new B2B orders. ⏱️ Fetches order data from Airtable (Orders table). 📥 Filters only paid orders with “B2B” tag. ✅ Creates a corresponding Stripe customer from order details. 👤 Processes order line items for invoicing. 📦 Creates a Stripe invoice with due date and payment terms. 🧾 Finalizes the invoice automatically. ✔️ Formats invoice details (totals, due dates, customer info, links). 🔄 Logs structured invoice data into Google Sheets for tracking. 📊 Key Benefits Fully automates B2B invoicing workflow from orders to finalized invoices. 🔄 Ensures all invoices are linked, structured, and logged in Sheets. 🧾 Reduces manual effort and eliminates data entry errors. ⚡ Maintains centralized invoice tracking for finance teams. 📂 Creates a consistent billing flow integrated with Stripe. 💳 Features Hourly Trigger – Continuously monitors Airtable for new/updated orders. Airtable Integration – Fetches order details automatically. Conditional Filter – Processes only “B2B” paid orders. Stripe Customer Creation – Automatically creates customers in Stripe. Line Item Processor – Handles Shopify/Order line items or test data. Stripe Invoice Creation – Generates draft invoices with due dates. Invoice Finalization – Auto-finalizes and prepares invoices for payment. Data Formatter – Structures invoice info (totals, links, dates, status). Google Sheets Integration – Logs all invoice data for reporting. Requirements n8n instance (cloud or self-hosted). Airtable Personal Access Token with read access to Orders table. Stripe API credentials with customer + invoice permissions. Google Sheets OAuth2 credentials with read/write access. Target Audience Finance/ops teams handling B2B customer invoicing. 💼 SaaS or eCommerce businesses with B2B order flows. 🛍️ Startups needing automated billing + centralized reporting. 🚀 Teams tracking Stripe invoices inside Google Sheets. 📊 Step-by-Step Setup Instructions Connect Airtable credentials and replace with your base/table IDs. 🔑 Configure Stripe API credentials for invoice + customer creation. 💳 Link Google Sheets credentials and update the target sheet ID. 📊 Adjust order filtering conditions (tags, payment status) as needed. ⚙️ Test with sample data to validate invoices are created + logged. ✅

This automation template turns any RSS feed into ready-to-publish social content using AI. It continuously ingests articles, scores their quality and relevance, crafts platform-native posts (Twitter/X threads and LinkedIn posts), routes items for review or archiving, logs everything to Google Sheets, and can publish automatically to X, Threads, and LinkedIn. Note: This workflow uses OpenAI models for analysis and content generation and integrates with Upload-Post for multi-platform publishing and Google Sheets for tracking. Costs depend on token usage and posting volume.* Who Is This For? Content Teams & Solo Creators:** Ship consistent, high-signal posts without manual rewriting. Newsletters & Media Brands:** Turn breaking stories into shareable, platform-native content. Agencies:** Scale curation across clients with review and auto-publish paths. Founders & PMMs:** Maintain a steady public presence with minimal effort. What Problem Does This Workflow Solve? Manual curation and rewriting of news is slow and inconsistent. This workflow: Scores Articles:** Filters noise with AI quality/relevance scoring. Auto-Writes Posts:** Generates concise X threads and business-ready LinkedIn copy. Routes Intelligently:** Sends good items to publish/review and archives the rest. Logs Everything:** Keeps a structured history in Google Sheets for analytics. How It Works RSS Polling: Monitors your chosen feed(s) on a schedule. Scoring AI: Rates quality and relevance; extracts summary, key topics, and angle. Parse & Enrich: Normalizes AI output and merges with article metadata. Quality Gates: Directs items to “publish/review” or “archive.” Content Generation: Produces an X thread and a LinkedIn post with clear hooks and insights. Publishing: Uploads to X, Threads, and LinkedIn via Upload-Post (optional). Sheets Logging: Writes summaries, scores, and outputs to Google Sheets. Setup OpenAI API: Add your OpenAI credentials (models like gpt-4.1/gpt-4o). Upload-Post Credentials: Connect the Upload-Post integration and target pages (e.g., LinkedIn org ID). Google Sheets: Add OAuth credentials and point “Store Content”/“New for Review”/“Archive” to your sheets. RSS Feed URL: Replace the sample feed with your preferred sources. Thresholds & Routing: Adjust quality/relevance filters to your standards. Publishing Mode: Toggle platforms (X, Threads, LinkedIn) and decide auto vs. review-first. Requirements Accounts:** n8n, OpenAI, Upload-Post, Google account (Sheets). API Keys:** OpenAI token, Upload-Post credentials, Google Sheets OAuth. Feeds:** One or more RSS URLs for your niche. Features AI Triage:** Quality/relevance scoring to prioritize high-value stories. Platform-Native Output:** Hooked X threads and professional LinkedIn posts. Review or Auto-Publish:** Safe gating before posting live. Analytics-Ready Logs:** Structured entries in Google Sheets. Modular & Extensible:** Swap feeds, add Slack/Discord alerts, or plug into CMS/Notion. Stay top-of-mind: convert fresh news into compelling, on-brand social content—automatically.