Workflow Templates

Complete MCP server exposing 3 Compliance 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 Compliance API 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 Compliance 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://api.ebay.com{basePath} • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (3 total) 🔧 Listing_Violation (1 endpoints) • GET /listing_violation: Get Violation Summary Counts 🔧 Listing_Violation_Summary (1 endpoints) • GET /listing_violation_summary: This call returns listing violation counts for a seller 🔧 Suppress_Listing_Violation (1 endpoints) • POST /suppress_listing_violation: Suppress Listing Violation 🤖 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 Compliance 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.

Complete MCP server exposing all Pushbullet Tool operations to AI agents. Zero configuration needed - all 4 operations pre-built. ⚡ 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 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 • MCP Trigger: Serves as your server endpoint for AI agent requests • Tool Nodes: Pre-configured for every Pushbullet Tool operation • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Uses official n8n Pushbullet Tool tool with full error handling 📋 Available Operations (4 total) Every possible Pushbullet Tool operation is included: 🔧 Push (4 operations) • Create a push • Delete a push • Get many pushes • Update a push 🤖 AI Integration Parameter Handling: AI agents automatically provide values for: • Resource IDs and identifiers • Search queries and filters • Content and data payloads • Configuration options Response Format: Native Pushbullet Tool API responses with full data structure Error Handling: Built-in n8n error management and retry logic 💡 Usage Examples Connect this MCP server to any AI agent or workflow: • Claude Desktop: Add MCP server URL to configuration • Custom AI Apps: Use MCP URL as tool endpoint • Other n8n Workflows: Call MCP tools from any workflow • API Integration: Direct HTTP calls to MCP endpoints ✨ Benefits • Complete Coverage: Every Pushbullet Tool operation available • Zero Setup: No parameter mapping or configuration needed • AI-Ready: Built-in $fromAI() expressions for all parameters • Production Ready: Native n8n error handling and logging • Extensible: Easily modify or add custom logic > 🆓 Free for community use! Ready to deploy in under 2 minutes.

This n8n template showcases a cool feature of n8n Forms where the form itself can be defined dynamically using the form fields schema. It may be debateable how useful this template actually is since both Airtable and Baserow provide form interfaces already but still a great exercise and demonstration if ever the use-case comes around. How it works A form trigger is used to dynamically select a database/table from which to build the n8n form from. the table's schema is imported into the workflow and using the code node, is converted into the n8n form fields schema. This let's us dynamically build the fields in our n8n form when we choose to define the form using the JSON option. Once the n8n form submits, we convert the values back into our table's API schema so that we can create a new row. Note any files/attachments fields are removed as they need to be handled separately. Files are processed separately as they may first need to be stored. Once complete, the reference is saved into the newly created row. Check out the example Airtable here - https://airtable.com/appfP15Xd0aVZR9xV/shrGFgXLyQ4Jg58SU How to use The n8n form is autogenerated which means you only need provide access to the table. Using this approach, this template can be reused for any number of Airtable and/or Baserow tables. Requirements You'll need either an Airtable account or a Baserow account to use this template. Accessible n8n instance to your users Customising this workflow Not using either Airtable or Baserow? Theoretically any datastore which provides a fields schema can be used with this template. If you're feeling creative, split the table into multiple forms for a better user experience.

🧑‍💼 Who is this for? This workflow is for anyone with two YouTube channels who wants to copy playlists from one to the other — no technical skills required. Whether you're a content creator, hobbyist, educator, or just someone managing multiple channels, this workflow helps you save time and avoid the manual work of recreating playlists video by video. 🧠 What problem is this workflow solving? YouTube doesn't provide an option to transfer or duplicate playlists between accounts or channels. That means if you want the same playlists in two places, you're stuck: Creating new playlists manually Searching for each video again Copy-pasting links one by one This workflow automates the entire process for you — accurately, quickly, and with no manual work. ⚙️ What this workflow does Retrieves all playlists from a source YouTube channel (excluding private ones) For each playlist: Gets all its videos Filters out private or unavailable videos Creates a new playlist in the target channel with the same title Adds the videos to the new playlist Continues smoothly even if some videos fail to copy (e.g., if they’re restricted or deleted) 🛠️ Setup Create two YouTube OAuth2 credentials in n8n: One for your source channel One for your target channel Assign the credentials to the correct nodes as indicated in the sticky notes: Source nodes → source credentials Target nodes → target credentials Click “Test workflow” to run it. > ⚠️ Note: If you have many playlists or videos, you may hit YouTube’s API quota. You can request a quota increase in your Google Cloud Console if needed. 🧩 How to customize this workflow to your needs ✂️ Copy only specific playlists Use a Filter node after the playlist fetch to include only certain titles or IDs. 📝 Change the title of the copied playlists Modify the title in the Create playlist node (e.g., add “(Copy)” or a prefix). 🔄 Automate it regularly Replace the Manual Trigger with a Cron node if you want to run this periodically. 🧪 Test safely If you're unsure, use a secondary channel as your test target before applying changes to your main account.

This workflow automates the generation of AI-enhanced, contextualized images using FLUX Kontext, based on prompts stored in a Google Sheet. The generated images are then saved to Google Drive, and their URLs are written back to the spreadsheet for easy access. Example Image: Prompt: The girl is lying on the bed and sleeping Result: Perfect for E-commerce and Social Media This workflow is especially useful for e-commerce businesses: Generate product images with dynamic backgrounds based on the use-case or season. Create contextual marketing visuals for ads, newsletters, or product pages. Scale visual content creation without the need for manual design work. How It Works Trigger**: The workflow can be started manually (via "Test workflow") or scheduled at regular intervals (e.g., every 5 minutes) using the "Schedule Trigger" node. Data Fetch**: The "Get new image" node retrieves a row from a Google Sheet where the "RESULT" column is empty. It extracts the prompt, image URL, output format, and aspect ratio for processing. Image Generation**: The "Create Image" node sends a request to the FLUX Kontext API (fal.run) with the provided parameters to generate a new AI-contextualized image. Status Check**: The workflow waits 60 seconds ("Wait 60 sec." node) before checking the status of the image generation request via the "Get status" node. If the status is "COMPLETED," it proceeds; otherwise, it loops back to wait. Result Handling**: Once completed, the "Get Image Url" node fetches the generated image URL, which is then downloaded ("Get Image File"), uploaded to Google Drive ("Upload Image"), and the Google Sheet is updated with the result ("Update result"). Set Up Steps To configure this workflow, follow these steps: Google Sheet Setup: Create a Google Sheet with columns for PROMPT, IMAGE URL, ASPECT RATIO, OUTPUT FORMAT, and RESULT (leave this empty). Link the sheet in the "Get new image" and "Update result" nodes. API Key Configuration: Sign up at fal.ai to obtain an API key. In the "Create Image" node, set the Header Auth with: Name: Authorization Value: Key YOURAPIKEY Google Drive Setup: Specify the target folder ID in the "Upload Image" node where generated images will be saved. Schedule Trigger (Optional): Adjust the "Schedule Trigger" node to run the workflow at desired intervals (e.g., every 5 minutes). Test Execution: Run the workflow manually via the "Test workflow" node to verify all steps function correctly. Once configured, the workflow will automatically process pending prompts, generate images, and update the Google Sheet with results. Need help customizing? Contact me for consulting and support or add me on Linkedin.

Overview This comprehensive n8n workflow provides a sophisticated solution for dynamically selecting and using AI models while maintaining GDPR compliance. It leverages Requesty's European-based AI routing service to ensure data privacy and automatically updates available model options based on real-time API availability. Choose Your Integration Approach Before diving into the setup, it's crucial to understand that this workflow offers two completely independent AI integration approaches: Approach 1: Dynamic HTTP Request Workflow (Advanced) Complete infrastructure with dynamic model selection What it includes: Automatic model discovery from Requesty's API Dynamic dropdown updates in web forms Model selection persistence in Google Sheets Complex workflow orchestration with multiple phases Full control over API parameters and response handling Best for: Teams needing multiple AI models for different tasks Organizations requiring model usage auditing Users who want maximum flexibility and control Advanced n8n users comfortable with complex workflows Setup complexity: High (requires multiple components and configurations) Approach 2: Standalone AI Agent (Simple) Plug-and-play solution without complexity What it includes: Direct use of n8n's native OpenAI Chat Model node Simple configuration: just set base URL to https://router.requesty.ai/v1 Immediate GDPR compliance through European infrastructure No model discovery or selection infrastructure needed Best for: Users wanting quick GDPR-compliant AI integration Single-model use cases Simple chat interfaces Users preferring minimal configuration Setup complexity: Low (5-minute setup) Quick Start: Approach 2 (Simple AI Agent) If you want to get started quickly with GDPR-compliant AI, follow these steps: Step 1: Register with Requesty Visit https://www.requesty.ai Complete the registration process Choose "OpenAI-compatible" integration Note your API endpoint: https://router.requesty.ai/v1 Create an API key (name it "n8n Integration") Step 2: Configure n8n Add a new OpenAI credential in n8n Set the base URL to: https://router.requesty.ai/v1 Enter your Requesty API key Add an OpenAI Chat Model node to your workflow Select your Requesty credential Step 3: Test Your AI agent is now ready and GDPR-compliant! All requests will be routed through Requesty's European infrastructure. Advanced Setup: Approach 1 (Dynamic HTTP Workflow) For users who need dynamic model selection and advanced features, follow this comprehensive setup: Prerequisites n8n instance (self-hosted or cloud) Requesty API credentials Google Sheets integration Basic understanding of n8n workflows Phase 1: Requesty Account Setup 1.1 Registration Process Navigate to https://www.requesty.ai Sign up with your email address Complete the welcome process 1.2 Integration Configuration Choose Integration Type: Select "OpenAI-compatible" Note API Endpoint: https://router.requesty.ai/v1 Create API Key: Provide a descriptive name (e.g., "n8n Dynamic Workflow") Click "Create API Key" Important: Save this key securely - you'll need it for n8n configuration Phase 2: Google Sheets Preparation 2.1 Create Storage Sheet Create a new Google Sheet named "AI Model Selections" Add the following column: A1: "Selected Model" Note the Google Sheet ID from the URL 2.2 Configure Google Sheets API Enable Google Sheets API in Google Cloud Console Create service account credentials Share your sheet with the service account email Download the credentials JSON file Phase 3: n8n Workflow Configuration 3.1 Import Workflow Download the workflow JSON file Import into your n8n instance Review all nodes and connections 3.2 Configure Credentials Requesty API Credentials: Go to n8n Credentials section Create new HTTP Request credential Set authentication type to "Header Auth" Header name: "Authorization" Header value: "Bearer YOUR_REQUESTY_API_KEY" Google Sheets Credentials: Create new Google Sheets credential Upload your service account JSON file Test the connection Google Sheets Nodes: Update sheet ID in all Google Sheets nodes Verify column mappings match your sheet structure Phase 4: Troubleshooting Guide Common Issues and Solutions Models Not Loading: Verify Requesty API credentials Check network connectivity and API endpoint URL Selection Not Persisting: Verify Google Sheets credentials and write permissions Check sheet ID configuration Chat Not Responding: Verify selected model availability Check API request formatting and response processing Debug Procedures Enable debug mode and detailed logging Check node outputs and data flow Validate API calls with external tools Review n8n execution logs Conclusion The choice between approaches depends on your specific requirements: Simple AI Agent**: Perfect for straightforward AI integration with minimal setup Dynamic HTTP Workflow**: Ideal for complex requirements with multiple models and advanced features

This workflow provides a secure API endpoint to remotely trigger other n8n workflows with custom data and to retrieve information about your existing workflows. It's perfect for users who want to integrate n8n into external systems or programmatically manage their automations. example usage: I use this workflow in a Raycast extension i have build, to execute n8n workflows from within Raycast: see Github ++How it works++ Receives API Calls: A webhook listens for incoming HTTP requests (e.g., POST to trigger, GET to retrieve info). Triggers Workflows: If the request is to trigger a workflow, it dynamically identifies the target workflow ID (from query parameters) and any input data (from the request body), then executes that workflow. This means you can control any of your workflows without modifying this manager template. Retrieves Workflow Info: Similarly, if the request is to get information, it dynamically uses query parameters (workflowId, mode, includedWorkflows) to fetch details about one or more n8n workflows (e.g., specific, all, active, inactive; full or summarized data). Responds: Sends back a JSON response indicating success/failure or the requested workflow data. ++Set it up++ Configure Webhook Security: Set up "Header Auth" credentials for the main Webhook node. This is the API key your external services will use. Add n8n API Credentials: For the nodes that fetch workflow information (like "Get specific workflowid", "get all active workflows", etc.), connect your n8n API credentials. This allows the workflow to query your n8n instance. Note Your Webhook URL: Once active, n8n provides a production URL for the webhook (path: workflow-manager). Use this URL to make API calls. Understand API Parameters: To trigger: Use ?workflowId=[ID_OF_WORKFLOW_TO_RUN] and send JSON data in the request body. To get info: Use parameters like ?workflowId=[ID], ?includedWorkflows=[all/active/inactive], and ?mode=[full/summary].

How it works This template takes a YouTube video ID and identifies potentially engaging moments based on the intensity of specific timestamps 👇 Ideal for vloggers and YouTube content creators, it serves as a foundation for various automations to streamline content calendars or highlight popular moments in your videos. You can leverage it for: Automatic processes to analyze YouTube videos and create sizzle reels or clips for social media, particularly effective for microcontent strategies like those endorsed by Gary Vee. Instant alerts via Slack, Telegram, Email, or WhatsApp when significant moments occur in your videos. Utilizing transcripts of these moments with AI to refine content ideas or brainstorm chatbots in your editorial workflow. Example response from the Workflow-as-an-API A GET request to {your instance URL}/webhook/youtube-engaging-moments-extractor?ytID=IZsQqarWXtYy returns 👇 The workflow generates multiple moments; the screenshot above shows a truncated version. Not all videos contain timestamp intensity data, the workflow handles this case as well 👇 How to use Import the template into your n8n workspace or self-hosted instance, then activate the workflow. Open the Webhook trigger node and copy the Production URL. In a web browser or any tool capable of consuming HTTP Requests (e.g., native code, Bubble app, n8n workflow, another automation tool, Postman, etc.), pass along the URL parameter ?ytID={youtube video ID} when invoking the API endpoint. Your URL should resemble something like https://acme.app.n8n.cloud/webhook/youtube-engaging-moments-extractor?ytID=IZsQqarWXtYy. Keep in mind This workflow relies on an unofficial YouTube API graciously hosted for free by the folks at lemnoslife.com. It's not recommended for high-volume production usecases.

This workflow is a modification of the previous template on how to create an SQL agent with LangChain and SQLite. The key difference – the agent has access only to the database schema, not to the actual data. To achieve this, SQL queries are made outside the AI Agent node, and the results are never passed back to the agent. This approach allows the agent to generate SQL queries based on the structure of tables and their relationships, without having to access the actual data. This makes the process more secure and efficient, especially in cases where data confidentiality is crucial. 🚀 Setup To get started with this workflow, you’ll need to set up a free MySQL server and import your database (check Step 1 and 2 in this tutorial). Of course, you can switch MySQL to another SQL database such as PostgreSQL, the principle remains the same. The key is to download the schema once and save it locally to avoid repeated remote connections. Run the top part of the workflow once to download and store the MySQL chinook database schema file on the server. With this approach, we avoid the need to repeatedly connect to a remote db4free database and fetch the schema every time. As a result, we reach greater processing speed and efficiency. 🗣️ Chat with your data Start a chat: send a message in the chat window. The workflow loads the locally saved MySQL database schema, without having the ability to touch the actual data. The file contains the full structure of your MySQL database for analysis. The Langchain AI Agent receives the schema, your input and begins to work. The AI Agent generates SQL queries and brief comments based solely on the schema and the user’s message. An IF node checks whether the AI Agent has generated a query. When: Yes: the AI Agent passes the SQL query to the next MySQL node for execution. No: You get a direct answer from the Agent without further action. The workflow formats the results of the SQL query, ensuring they are convenient to read and easy to understand. Once formatted, you get both the Agent answer and the query result in the chat window. 🌟 Example queries Try these sample queries to see the schema-driven AI Agent in action: Would you please list me all customers from Germany? What are the music genres in the database? What tables are available in the database? Please describe the relationships between tables. - In this example, the AI Agent does not need to create the SQL query. And if you prefer to keep the data private, you can manually execute the generated SQL query in your own environment using any database client or tool you trust 🗄️ 💭 The AI Agent memory node does not store the actual data as we run SQL-queries outside the agent. It contains the database schema, user questions and the initial Agent reply. Actual SQL query results are passed to the chat window, but the values are not stored in the Agent memory.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Who's it for Content creators, marketers, and researchers who need to monitor multiple RSS feeds and get AI-generated summaries without manual work. How it works This workflow automatically monitors RSS feeds, filters new articles from the last X days, checks for duplicates, and generates structured AI summaries. It fetches full article content, converts HTML to markdown, and uses Gemini AI to create consistent summaries with quick takeaways, key points, and practical insights. All data is saved to Google Sheets for easy access and sharing. The system processes RSS feeds in batches, ensuring no duplicate articles are processed twice by checking existing URLs in your Google Sheets. Each new article gets a comprehensive AI summary that includes the main message, key takeaways, important points, and practical applications. Requirements Google Sheets access OpenRouter API key for Gemini AI model or other language model RSS feed URLs to monitor How to set up Copy the template Google Sheet, add your RSS feeds in the "RSS FEEDS" tab, configure Google Sheets and OpenRouter credentials in n8n, and adjust the time filter in the Settings node. The workflow can run manually or on schedule every hour. How to customize Modify AI prompts for different summary styles, change the time filter duration, add more data fields to Google Sheets, or switch to a different AI model in the LLM Chat Model node.

Complete MCP server exposing 4 Seller Service Metrics API 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 Seller Service Metrics API 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 Seller Service Metrics API 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://api.ebay.com{basePath} • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (4 total) 🔧 Customer_Service_Metric (1 endpoints) • GET /customer_service_metric/{customer_service_metric_type}/{evaluation_type}: Get {Evaluation Type} 🔧 Seller_Standards_Profile (2 endpoints) • GET /seller_standards_profile: Get Seller Standards Profile • GET /seller_standards_profile/{program}/{cycle}: This call retrieves a single standards profile for the associated seller 🔧 Traffic_Report (1 endpoints) • GET /traffic_report: Retrieve Listing Traffic Report 🤖 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 Seller Service Metrics API 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.

This workflow sends a message to a Discord channel when a new row is added or a row is updated in a Google Sheet. The message will send all data rows in the Google Sheet. Prerequisites Discord account and Discord credentials. Google account and Google credentials. How it works Using a code node, we can use the obtained Google Sheet data to create a custom message that will be sent to Discord. The message will be sent to the Discord channel specified in the Discord node. Setup This workflow requires that you set up a Discord webhook and have an existing Google Sheet with data. See how to set up a Discord webhook here.