Workflow Templates

🌐 Web Server Monitor & Alert System This automation pings web servers at regular intervals, logs their status, and sends email alerts if a server goes down. It’s perfect for maintaining visibility over server uptime — without complex monitoring tools. 🧠 How It Works This workflow performs minute-by-minute checks on all listed servers in a Google Sheet and: ✅ Logs all reachable servers in an “Alive” log. 🔻 Sends an email alert if a server is unreachable. 📄 Logs failed servers in a “Down” sheet with timestamps. 🧩 Key Components ⏰ 1. Schedule Trigger Runs the workflow every minute for real-time monitoring. 📄 2. Web Servers List (Google Sheets) Pulls server IPs or hostnames from a Google Sheet named Server_List. Each row = one server to monitor. This makes adding/removing servers effortless — just update the sheet. 🌐 3. Servers Alive Check (HTTP Request) Performs an HTTP GET request to each server (e.g., http://your-server.com). If the request fails, it automatically triggers the error path (handled via continueOnFail). ✅ 4. Web Server Alive Log (Google Sheets) Records successful pings in Server_Status_Alive with: Timestamp Server IP Status = Alive This log can be used for uptime reports or audits. 📧 5. Server Down Notification (Gmail) If a server fails, this node sends an email to the admin. It includes: Server address Timestamp Suggested action 📄 6. Web Server Down Log (Google Sheets) Logs failed pings in a separate sheet for historical tracking and debugging. ✅ Main Advantages Live Server Monitoring Stay informed about server health in near real-time. No-Code Configuration Add/remove servers from the Google Sheet — no need to touch the workflow. Email Alerts on Failure Proactively notifies you before users report the issue. Audit-Ready Logging Maintains logs for both healthy and failed checks for documentation or reporting. Flexible & Scalable Monitor 1 or 100 servers with the same template — just scale the list. ⚙️ Setup Steps 🔑 Prerequisites Google Sheet with server list (column name = “Server”) Gmail OAuth2 Connection for alerts n8n Instance running regularly 🛠 Configuration Google Sheets Sheet 1 (Server_List): Your list of servers. Sheet 2 (Server_Status_Alive): Log for reachable servers. Sheet 3 (Server_Status_Down): Log for unreachable servers. Gmail Integration Connect your Gmail account in the Server Down Notification node. Edit recipient email and message content as needed. HTTP Check Adjust the HTTP request URL template if using port numbers or paths (e.g., http://{{Server}}:8080/status). Schedule Default is every 1 minute. Change via Schedule Trigger if needed. 🧪 Testing Input a reachable server (e.g., example.com) and an unreachable IP. Run the workflow manually or wait for the next scheduled run. Check: Alive log updates correctly. Down log records failures. Email alert is received. 🚀 Deployment Activate the workflow, and it will quietly run in the background, notifying you of any server downtime instantly while keeping logs for future review.

This workflow template demonstrates how to create an AI-powered agent that provides users with current weather information and Wikipedia summaries. By integrating n8n with Ollama's local Large Language Models (LLMs), this template offers a seamless and privacy-conscious solution for real-time data retrieval and summarization. Who is this for? Developers and Enthusiasts: Individuals interested in building AI-driven workflows without relying on external APIs. Privacy-Conscious Users: Those who prefer processing data locally to maintain control over their information. Educators and Students: Learners seeking hands-on experience with AI integrations and workflow automation. What problem does this workflow solve? Accessing up-to-date weather information and concise Wikipedia summaries typically requires multiple API calls to external services, which can raise privacy concerns and incur costs. This workflow addresses these issues by utilizing Ollama's self-hosted LLMs within n8n, enabling users to retrieve and process information locally. What this workflow does: User Input Capture: Begins with a chat interface where users can input queries. AI Processing: The input is sent to an AI Agent node configured with Ollama's LLMs, which interprets the query and determines the required actions. Weather Retrieval: For weather-related queries, the workflow fetches current weather data from a specified source. Wikipedia Summarization: For queries seeking information, it retrieves relevant Wikipedia content and generates concise summaries. Setup: Install Required Tools: Ollama: Install and run Ollama to manage local LLMs. Configure n8n Workflow: Import the provided workflow template into your n8n instance. Set up the AI Agent node to connect with Ollama's API. Ensure nodes responsible for fetching weather data and Wikipedia content are correctly configured. Run the Workflow: Start the workflow and interact with the chat interface to test various queries. How to customize this workflow to your needs: Automate Triggers: Set up scheduled triggers to provide users with regular updates, such as daily weather forecasts or featured Wikipedia articles.

Intro This template is for project managers, team leads, or anyone who wants to automatically remind teammates of tasks due today—no manual copy‑and‑paste required. How it works Schedule Trigger runs every morning at 8 AM. Google Sheets node reads your “Tasks” sheet. If node filters rows where Due Date = today. Summarize (ChatGPT HTTP Request) generates a friendly reminder per person. Message a model sends the prompt to your ChatGPT Assistant and returns the AI response. Send a message (Gmail) emails each assignee their personalized reminder. Required Google Sheet Structure | Column Name | Type | Example | Notes | |-------------|--------|---------------------------|-------------------------| | Name | string | Alice Johnson | Person to remind | | Email | string | user@example.com | Recipient email address | | Task | string | Submit quarterly report | Task description | | Due Date | date | 2025‑07‑29 | Format: YYYY‑MM‑DD | Detailed Setup Steps Google Sheets Create your sheet with the columns above. In n8n → Credentials, add Google Sheets API (do not include real sheet IDs in the name). ChatGPT Assistant In the OpenAI Dashboard → Assistants, click Create Assistant. Choose a model (e.g., gpt-4), copy the Assistant ID. In n8n → Credentials → OpenAI, add your API Key and Assistant ID. Gmail In n8n → Credentials → Gmail (OAuth2 or SMTP), connect your account without embedding your real address in the credential name. Import & Configure Export this workflow’s JSON (three‑dot menu → Export). Paste it under Template Code in the Creator form. In each node, select your Google Sheets, OpenAI, and Gmail credentials. Sticky Notes A note on the Schedule node: “Set your desired run time.” A note on the ChatGPT node: “Customizes reminder text.” A note on the Gmail node: “Sends reminder email.” Customization Guidance Change schedule: edit the Cron expression in **Schedule Trigger. Adjust tone**: modify the system prompt in your ChatGPT Assistant. Email format: update **Subject and Body in the Gmail node. Batch processing: insert a **SplitInBatches node before Summarize for large sheets. Troubleshooting Ensure your Google Sheet is shared with the connected service account. Verify Due Date format (YYYY‑MM‑DD). If ChatGPT fails, check your API key and quota. Security & Best Practices Do not** hard‑code API keys, sheet IDs, or real emails. Use n8n Credentials or environment variables only. Remove any private information before submitting.

⚠️ Note: This template requires a community node and works only on self-hosted n8n installations. It uses the Typhoon OCR Python package and custom command execution. Make sure to install required dependencies locally. Who is this for? This template is for developers, operations teams, and automation builders in Thailand (or any Thai-speaking environment) who regularly process PDFs or scanned documents in Thai and want to extract structured text into a Google Sheet. It is ideal for: Local government document processing Thai-language enterprise paperwork AI automation pipelines requiring Thai OCR What problem does this solve? Typhoon OCR is one of the most accurate OCR tools for Thai text. However, integrating it into an end-to-end workflow usually requires manual scripting and data wrangling. This template solves that by: Running Typhoon OCR on PDF files Using AI to extract structured data fields Automatically storing results in Google Sheets What this workflow does Trigger: Run manually or from any automation source Read Files: Load local PDF files from a doc/ folder Execute Command: Run Typhoon OCR on each file using a Python command LLM Extraction: Send the OCR markdown to an AI model (e.g., GPT-4 or OpenRouter) to extract fields Code Node: Parse the LLM output as JSON Google Sheets: Append structured data into a spreadsheet Setup 1. Install Requirements Python 3.10+ typhoon-ocr: pip install typhoon-ocr Install Poppler and add to system PATH (needed for pdftoppm, pdfinfo) 2. Create folders Create a folder called doc in the same directory where n8n runs (or mount it via Docker) 3. Google Sheet Create a Google Sheet with the following column headers: | book\_id | date | subject | detail | signed\_by | signed\_by2 | contact | download\_url | | -------- | ---- | ------- | ------ | ---------- | ----------- | ------- | ------------- | You can use this example Google Sheet as a reference. 4. API Key Export your TYPHOON_OCR_API_KEY and OPENAI_API_KEY in your environment (or set inside the command string in Execute Command node). How to customize this workflow Replace the LLM provider in the Basic LLM Chain node (currently supports OpenRouter) Change output fields to match your data structure (adjust the prompt and Google Sheet headers) Add trigger nodes (e.g., Dropbox Upload, Webhook) to automate input About Typhoon OCR Typhoon is a multilingual LLM and toolkit optimized for Thai NLP. It includes typhoon-ocr, a Python OCR library designed for Thai-centric documents. It is open-source, highly accurate, and works well in automation pipelines. Perfect for government paperwork, PDF reports, and multilingual documents in Southeast Asia.

Google Sheets UI for Workflow Control This n8n template provides a practical and efficient way to manage your n8n workflows using Google Sheets as a user-friendly interface. It demonstrates how to leverage a simple spreadsheet to control inputs, capture outputs, and track the processing status of individual data rows, offering a clear and visual overview of your automation tasks. Purpose of This Template: The primary purpose of this template is to illustrate how Google Sheets can serve as a dynamic UI for your n8n automations. It's designed for n8n users who need: A structured method to feed specific data into their workflows. The ability to selectively trigger workflow execution based on data status. A centralized place to view and store workflow outputs alongside original inputs. A simple, no-code solution for managing workflow data without building custom applications. Setup Instructions: To use this template, follow these steps: Create a Google Sheet: Set up a new Google Sheet (see the template here) with three columns: Color, Status, and Number. Populate the Color column with some sample data (e.g., color names) and set the Status for the rows you want to process to READY. Import the n8n Workflow: Import this n8n template into your n8n instance. Configure Google Sheets Nodes: For the first Google Sheets node (Read operation), ensure it's connected to your newly created Google Sheet and configured to read rows where the Status column is READY. You will need to authenticate your Google Sheets account. For the second Google Sheets node (Update operation), ensure it's also connected to the same Google Sheet. The node should automatically map the row_number, Number, and Status fields from the preceding nodes. Execute the Workflow: Run the workflow. Observe how it reads READY rows, processes them (calculates string length), and updates the Number and Status columns in your Google Sheet to DONE. Control Execution: To process new data, simply add new rows to your Google Sheet and set their Status to READY. Rerunning the workflow will then only process these new entries. For more details and context on this approach, you can refer to the related blog post here.

This n8n template lets you instantly serve batches of inspirational quotes via a webhook using the free ZenQuotes API. It’s perfect for developers, content creators, community managers, or educators who want to add dynamic, uplifting content to websites, chatbots, or internal tools—without writing custom backend code. 🔧 How it works A Webhook node listens for incoming HTTP requests on your chosen path. Get Random Quote from ZenQuotes sends an HTTP Request to https://zenquotes.io/api/random?count=5 and retrieves five random quotes. Format data uses a Set node to combine each quote (q) and author (a) into a single string: "“quote” – author". Send response returns a JSON array of objects { quote, author } back to the caller. 👤 Who is it for? This workflow is ideal for: Developers building motivational Slack or Discord bots. Website owners adding on-demand quote widgets. Educators or trainers sharing daily inspiration via webhooks. Anyone learning webhook handling and API integration in n8n. 🗂️ Response Structure Your webhook response will be a JSON array, for example: [ { "quote": "Life is what happens when you're busy making other plans.", "author": "John Lennon" }, { "quote": "Be yourself; everyone else is already taken.", "author": "Oscar Wilde" } ] ⚙️ Setup Instructions Import the workflow JSON into your n8n instance. In the Webhook node, set your desired path (e.g., /inspire). (Optional) Change the count parameter in the HTTP Request node to fetch more or fewer quotes. Activate the workflow. Test by sending an HTTP GET or POST to https://<your-n8n-domain>/webhook/<path>.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. How it works This workflow demonstrates how to create a resilient AI Agent that automatically falls back to a different language model if the primary one fails. This is useful for handling API errors, rate limits, or model outages without interrupting your process. State Initialization: The Agent Variables node initializes a fail_count to 0. This counter tracks how many models have been attempted. Dynamic Model Selection: The Fallback Models (a LangChain Code node) acts as a router. It receives a list of all connected AI models and, based on the current fail_count, selects which one to use for this attempt (0 for the first model, 1 for the second, etc.). Agent Execution: The AI Agent node attempts to run your prompt using the model selected by the router. The Fallback Loop: On Success: The workflow completes successfully. On Error: If the AI Agent node fails, its "On Error" output is triggered. This path loops back to the Agent Variables node, which increments the fail_count by 1. The process then repeats, causing the Fallback Models router to select the next model in the list. Final Failure: If all connected models are tried and fail, the workflow will stop with an error. Set up steps Setup time: ~3-5 minutes Configure Credentials: Ensure you have the necessary credentials (e.g., for OpenAI, Google AI) configured in your n8n instance. Define Your Model Chain: Add the AI model nodes you want to use to the canvas (e.g., OpenAI, Google Gemini, Anthropic). Connect them to the Fallback Models node. Important: The order in which you connect the models determines the fallback order. The model nodes first created/connected will be tried first. Set Your Prompt: Open the AI Agent node and enter the prompt you want to execute. Test: Run the workflow. To test the fallback logic, you can temporarily disable the First Model node or configure it with invalid credentials to force an error.

This N8N workflow automates the process of sharing files from Google Drive. It includes OAuth2 authentication, batch processing, public link generation, and access status modification for efficient file handling. Suitable for users seeking to streamline their Google Drive file sharing process. sutiable for bulk actions, tested on 4.2K files folder working like charm. How It Works Initialize Workflow: The process begins with a Manual Trigger, allowing the user to start the workflow at their convenience. Folder ID Specification: A 'Set Folder ID' node where the user can enter the desired Google Drive Folder ID. List Files from Google Drive: The 'Google Drive' node lists all files within the specified folder using OAuth2 authentication. Batch Processing: The 'Loop Over Items' node processes the files in batches for efficiency. Generate Public Links: The 'Generate Download Links' node creates downloadable links for each file. Change File Access: The 'Change Status' node alters the file status to make them publicly accessible. Merge and Output: A 'Merge' node consolidates the data, preparing it for further actions or output. Set Up Steps Estimated Time**: The setup should take approximately 10-15 minutes. Initial Setup**: You'll need to provide OAuth2 credentials for Google Drive and specify a folder ID. Customization**: Adjust the batch size and file access permissions according to your needs. Detailed Descriptions**: For specific configuration details, refer to the sticky notes within the workflow. Example Item output { "link": "https://drive.google.com/u/3/uc?id=1hojqPfXchNTY8YRTNkxSo-8txK9re-V4&export=download&confirm=t&authuser=0", "name": "firefox_rNjA0ybKu7.png", "kind": "drive#permission", "id": "anyoneWithLink", "type": "anyone", "role": "reader", "allowFileDiscovery": false } You can store the output data with any data store node you want, for example save them into Excel Sheet or Airtable etc... Keywords: n8n workflow, Google Drive integration, file sharing automation, batch file processing, public link generation, OAuth2 authentication, workflow automation

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. This workflow automates the process of scraping product data from e-commerce websites and using it to fine-tune a custom OpenAI GPT model for generating high-quality marketing copy and product descriptions. Main Use Cases Fine-tune OpenAI models with real product data from hundreds of supported e-commerce websites for marketing content generation. Create custom AI models specialized in writing compelling product descriptions across different industries and platforms. Automate the entire pipeline from data collection to model training using Bright Data's extensive scraper library. Generate marketing copy using your custom-trained model via an interactive chat interface. How it works The workflow operates in two main phases: model training and model usage, organized into these stages: Data Collection & Processing Manually triggered to start the fine-tuning process. Uses Bright Data's web scraper to extract product information from any supported e-commerce platform (Amazon, eBay, Shopify stores, Walmart, Target, and hundreds of other websites). Collects product titles, brands, features, descriptions, ratings, and availability status from your chosen platform. Easily customizable to scrape from different websites by simply changing the dataset configuration and product URLs. Training Data Preparation A Code node processes the scraped product data to create training examples in OpenAI's required JSONL format. For each product, generates a complete training example with: System message defining the AI's role as a marketing assistant. User prompt containing specific product details (title, brand, features, original description snippet). Assistant response providing an ideal marketing description template. Compiles all training examples into a single JSONL file ready for OpenAI fine-tuning. Model Fine-Tuning Uploads the training file to OpenAI using the OpenAI File Upload node. Initiates a fine-tuning job via HTTP Request to OpenAI's fine-tuning API using the GPT-4o-mini model as the base. The fine-tuning process runs on OpenAI's servers to create your custom model. Interactive Chat Interface Provides a chat trigger that allows real-time interaction with your fine-tuned model. An AI Agent node connects to your custom-trained OpenAI model. Users can chat with the model to generate product descriptions, marketing copy, or other content based on the training. Custom Model Integration The OpenAI Chat Model node is configured to use your specific fine-tuned model ID. Delivers responses trained on your product data for consistent, high-quality marketing content. Summary Flow: Manual Trigger → Scrape E-commerce Products (Bright Data) → Process & Format Training Data (Code) → Upload Training File (OpenAI) → Start Fine-Tuning Job (HTTP Request) | Parallel: Chat Trigger → AI Agent → Custom Fine-Tuned Model Response Benefits: Fully automated pipeline from raw product data to trained AI model. Works with hundreds of different e-commerce websites through Bright Data's extensive scraper library. Creates specialized models trained on real e-commerce data for authentic marketing copy across various industries. Scalable solution that can be adapted to different product categories, niches, or websites. Interactive chat interface for immediate access to your custom-trained model. Cost-effective fine-tuning using OpenAI's most efficient model (GPT-4o-mini). Easily customizable with different websites, product URLs, training prompts, and model configurations. Setup Requirements: Bright Data API credentials for web scraping (supports hundreds of e-commerce websites). OpenAI API key with fine-tuning access. Replace placeholder credential IDs and model IDs with your actual values. Customize the product URLs list and Bright Data dataset for your specific website and use case. The workflow can be adapted for any e-commerce platform supported by Bright Data's scraping infrastructure.

Daily YouTrack In-Progress Tasks Summary to Discord by Assignee Keep your team in sync with a daily summary of tasks currently In Progress in YouTrack — automatically posted to your Discord channel. This workflow queries issues, filters them by status, groups them by assignee and priority, and sends a formatted message to Discord. It's perfect for teams that need a lightweight, automated stand-up report. > 📝 This workflow uses Discord as an example. You can easily replace the messaging integration with Slack, Mattermost, MS Teams, or any other platform that supports incoming webhooks. Use Case Remote development teams using YouTrack + Discord Replacing daily stand-up meetings with async updates Project managers needing quick visibility into active tasks Features Scheduled** daily execution (default: weekdays at 09:00) Status filter**: only issues marked as In Progress Grouping** by assignee and priority Custom mapping** for user mentions (YouTrack → Discord) Clean Markdown output** for Discord, with direct task links Setup Instructions YouTrack Configuration Get a permanent token: Go to your YouTrack profile → Account Security → Authentication Create a new permanent token with "Read Issue" permissions Copy the token value Set the base API URL: Format: https://yourdomain.youtrack.cloud/api/issues Replace yourdomain with your actual YouTrack instance Identify custom field IDs: Method 1: Go to YouTrack → Administration → Custom Fields → find your "Status" field and note its ID Method 2: Use API call GET /api/admin/customFieldSettings/customFields to list all field IDs Method 3: Inspect a task's API response and look for field IDs in the customFields array Example Status field ID: 105-0 or 142-1 Discord Configuration Create a webhook URL in your Discord server: Server Settings → Integrations → Webhooks → New Webhook Choose target channel and copy the webhook URL Extract webhook ID from URL (numbers after /webhooks/) Environment Variables & Placeholders | Placeholder | Description | |-------------|-------------| | {{API_URL}} | Your YouTrack API base URL | | {{TOKEN}} | YouTrack permanent token | | {{FIELD_ID}} | ID of the "Status" custom field | | {{QUERY_FIELDS}} | Fields to fetch (e.g., summary, id) | | {{PROJECT_LINK}} | Link to your YouTrack project | | {{USER_X}} | YouTrack usernames | | {{DISCORD_ID_X}} | Discord mentions or usernames | | {{NAME_X}} | Display names | | {{WEBHOOK_ID}} | Discord webhook ID | | {{DISCORD_CHANNEL}} | Discord channel name | | {{CREDENTIAL_ID}} | Your credential ID in n8n | Testing the Workflow Test YouTrack connection: Execute the "HTTP Request YT" node individually Verify that issues are returned from your YouTrack instance Check if the Status field ID is correctly filtering tasks Verify filtering: Run the "Filter fields" node Confirm only "In Progress" tasks pass through Check message formatting: Execute the "Discord message" node Review the generated message content and formatting Test Discord delivery: Run the complete workflow manually Verify the message appears in your Discord channel Schedule verification: Enable the workflow Test weekend skip functionality by temporarily changing dates Customization Tips Language**: All labels/messages are in English — customize if needed User mapping**: Adjust assignee → Discord mention logic in the message builder Priorities**: Update the priorityMap to reflect your own naming structure Schedule**: Modify the trigger time in the Schedule Trigger node Alternative platforms**: Swap out the Discord webhook for another messaging service if preferred

Real-time Weather Forecasts with MCP Tools This n8n workflow demonstrates how to integrate real-time weather intelligence into any automation using the Model Context Protocol (MCP). Get current conditions and 5-day forecasts with natural language queries like "What's the weather like in Miami?" or "Will it rain next Tuesday in Seattle?" - all powered by live weather data and AI. Good to know No API keys required - uses hosted MCP weather server with built-in WorldWeatherOnline integration Provides current conditions and detailed 5-day forecasts Natural language queries work for any location worldwide Powered by WorldWeatherOnline - the world's most accurate weather system Fully preconfigured and ready to run out-of-the-box Enterprise-ready with error handling and rate limiting How it works Natural Language Input**: Receives weather queries via webhook, chat, email, or voice AI Agent Processing**: n8n Agent node interprets requests and determines: Location extraction from natural language Weather data type needed (current or 5-day forecast) Response formatting preferences MCP Weather Tool**: Live hosted server provides: Real-time current conditions (temperature, humidity, wind, conditions) 5-day detailed forecasts with daily highs/lows Weather descriptions and condition codes Powered by WorldWeatherOnline's premium data Intelligent Responses**: AI formats weather data into: Conversational natural language responses Structured data for downstream automation Action-triggering data for workflows How to use Import the workflow into n8n from the template Add your preferred AI model API key to the Agent node Customize the system prompt for your specific use case Connect to your preferred input/output channels Run and start querying weather with natural language Use Cases Smart Home Automation**: "Turn on sprinklers if no rain forecast for 3 days" Travel Planning**: "Check weather for my Paris trip next week" Event Management**: "Will outdoor wedding conditions be good Saturday?" Agriculture/Farming**: "Check 5-day forecast for planting schedule" Logistics**: "Delay shipping if severe weather forecast in delivery zone" Personal Assistant**: "Should I wear a jacket today in Chicago?" Sports/Recreation**: "Surf conditions and wind forecast for weekend" Construction**: "Safe working conditions for outdoor project this week" Requirements n8n instance (cloud or self-hosted) AI model provider account (OpenAI, Anthropic, Google, etc.) Internet connection for MCP weather server access Optional: Webhook endpoints for external integrations Customizing this workflow Location Intelligence**: Add geocoding for address-to-coordinates conversion Data Storage**: Save weather history to databases for trend analysis Dashboard Integration**: Connect to Grafana, Tableau, or custom visualizations Voice Integration**: Add speech-to-text for voice weather queries Scheduling**: Set up automated daily/weekly weather briefings Conditional Logic**: Trigger different actions based on weather conditions Sample Input/Output Natural Language Queries: "What's the weather like in Miami?" "Will it rain next Tuesday in Seattle?" "5-day forecast for London" "Temperature in Tokyo tomorrow" "Weather conditions for outdoor event Saturday" Rich Responses: { "location": "Miami, FL", "current": { "temperature": "78°F", "condition": "Partly Cloudy", "humidity": "65%", "wind": "10 mph SE" }, "forecast": { "today": "High 82°F, Low 71°F, 20% rain", "tomorrow": "High 85°F, Low 73°F, Sunny" }, "ai_summary": "Perfect beach weather in Miami today! Partly cloudy with comfortable temperatures and light winds." } Why This Workflow is Unique Zero Setup Weather Data**: No API key management - MCP server handles everything World-Class Accuracy**: Powered by WorldWeatherOnline's premium weather data AI-Powered Intelligence**: Natural language understanding of complex weather queries Enterprise Ready**: Built-in error handling, rate limiting, and reliability Global Coverage**: Worldwide weather data with location intelligence Action-Oriented**: Designed for automation decisions, not just information display Transform your automations with intelligent weather awareness powered by the world's most accurate weather system! 🧪 Setup Steps ✅ The Agent node is already configured: The system prompt is included The tool endpoint is pre-set All you need to do is: Add your AI model API key to the existing Agent credential Hit run and you're done ✅ 🔗 Full project link: Github: weathertrax-mcp-agent-demo

Who is this workflow for? If you're using Meta Ads to generate new leads to your sales pipeline, this workflow is for you! 🙌🏻 What this workflow does Triggers every time you have a new calendar event on a chosen Google Acount Filter only events with the same name of your "Schedule a demo" event Formats and send event to Meta Conversion API What events can I send? Any event you'd like! It's preconfigured with the "Schedule" event, but you can change to "Purchase", "InitiateCheckout", "Lead" and custom events. Setup Guide Connect Google OAuth2 to n8n Get your PIXEL ID and Access Token from Meta Set your configuration node with Pixel ID, Access Token, source_url and event_name Requirements Meta Access Token + Pixel ID (via Meta Conversion API): Documentation Google Access (via OAuth2): Documentation This free template was created by pdforge. Feel free to contact us via the founder Linkedin, if you have any questions! 👋🏻