AI-powered lead enrichment with Bright Data MCP and Google Sheets
📌 HubSpot Lead Enrichment with Bright Data MCP This template enables natural-language-driven automation using Bright Data's MCP tools, triggered directly by new leads in HubSpot. It dynamically extracts and executes the right tool based on lead context—powered by AI and configurable in N8N. --- ❓ What Problem Does This Solve? Manual lead enrichment is slow, inconsistent, and drains valuable time. This solution automates the process using a no-code workflow that connects HubSpot, Bright Data MCP, and an AI agent—without requiring scripts or technical skills. Perfect for marketing, sales, and RevOps teams. --- 🧰 Prerequisites To use this template, you’ll need: A self-hosted or cloud instance of N8N A Bright Data MCP API token A valid OpenAI API key (or compatible AI model) A HubSpot account Either a Private App token or OAuth credentials for HubSpot Basic familiarity with N8N workflows --- ⚙️ Setup Instructions Set Up Authentication in HubSpot 🔐 Option 1: Use a Private App Token (Simple Setup) Log in to your HubSpot account. Navigate to Settings → Integrations → Private Apps. Create a new Private App with the following scopes: crm.objects.contacts.read crm.objects.contacts.write crm.schemas.contacts.read crm.objects.companies.read (optional) Copy the Access Token. In N8N, create a credential for HubSpot App Token and paste the app token in the field. Go back to Hubspot Private App settings to setup a webhook. Copy the url in your workflow's Webhook node and paste it here. 🔁 Option 2: Use OAuth (Advanced + Secure) In HubSpot, go to Settings → Integrations → Apps → Create App. Set your Redirect URL to match your N8N OAuth2 redirect path. Choose scopes like: crm.objects.companies.read crm.objects.contacts.read crm.objects.deals.read crm.schemas.companies.read crm.schemas.contacts.read crm.schemas.deals.read crm.objects.contacts.write (conditionally required) Note the Client ID and Client Secret. Copy the App ID and the developer API key In N8N, create a credential for HubSpot Developer API and paste those info from previous step. Attach these credentials to the HubSpot node in N8N. --- Setup and obtain API token and other necessary information from Bright Data In your Bright Data account, obtain the following information: API token Web Unlocker zone name (optional) Browser API username and password string separated by colon (optional) Host SSE server from STDIO command The methods below will allow you to receive SSE (Server-Sent Events) from Bright Data MCP via a local Supergateway or Smithery Method 1: Run Supergateway in a separate web service (Recommended) This method will work for both cloud version and self-hosted N8N. Signup to any cloud services of your choice (DigitalOcean, Heroku, Hetzner, Render, etc.). For NPM based installation: Create a new web service. Choose Node.js as runtime environment and setup a custom server without repository. In your server’s settings to define environment variables or .env file, add: `APITOKEN=yourbrightdataapitoken WEBUNLOCKERZONE=optionalzonename BROWSERAUTH=optionalbrowser_auth` Paste the following text as a start command: npx -y supergateway --stdio "npx -y @brightdata/mcp" --port 8000 --baseUrl http://localhost:8000 --ssePath /sse --messagePath /message Deploy it and copy the web server URL, then append /sse into it. Your SSE server should now be accessible at: https://yourserverurl/sse For Docker based installation: Create a new web service. Choose Docker as the runtime environment. Set up your Docker environment by pulling the necessary images or creating a custom Dockerfile. In your server’s settings to define environment variables or .env file, add: `APITOKEN=yourbrightdataapitoken WEBUNLOCKERZONE=optionalzonename BROWSERZONE=optionalbrowserzonename` Use the following Docker command to run Supergateway: `docker run -it --rm -p 8000:8000 supercorp/supergateway \ --stdio "npx -y @brightdata/mcp /" \ --port 8000` Deploy it and copy the web server URL, then append /sse into it. Your SSE server should now be accessible at: https://yourserverurl/sse For more installation guides, please refer to https://github.com/supercorp-ai/supergateway.git. Method 2: Run Supergateway in the same web service as the N8N instance This method will only work for self-hosted N8N. a. Set Required Environment Variables In your server's settings to define environment variables or .env file, add: APITOKEN=yourbrightdataapitoken WEBUNLOCKERZONE=optionalzonename BROWSERZONE=optionalbrowserzonename b. Run Supergateway in Background bash npx -y supergateway --stdio "npx -y @brightdata/mcp" --port 8000 --baseUrl http://localhost:8000 --ssePath /sse --messagePath /message Use the command above to execute it through the cloud shell or set it as a pre-deploy command. Your SSE server should now be accessible at: http://localhost:8000/sse For more installation guides, please refer to https://github.com/supercorp-ai/supergateway.git. Method 3: Configure via Smithery.ai (Easiest) If you don't want additional setup and want to test it right away, follow these instructions: Visit https://smithery.ai/server/@luminati-io/brightdata-mcp/tools to: Signup (if you are new to Smithery) Create an API key Define environment variables via a profile Retrieve your SSE server HTTP URL Connect Google Sheets to N8N Ensure your Google Sheet: Contains columns like rowid, firstname, last_name, email, and status. Is shared with your N8N service account (or connected via OAuth) In N8N: Add a Google Sheets Trigger node Set it to watch for new rows in your lead sheet --- Import and Configure the N8N Workflow Import the provided JSON workflow into N8N Update nodes with your credentials: Hubspot: Add your API key or connect it via OAuth. Google Sheets Trigger: Link to your actual sheet OpenAI Node: Add your API key Bright Data Tool Execution: Add Bright Data token and SSE URL --- 🔄 How It Works New contact in Hubspot or a new row is added to the Google Sheet N8N triggers the workflow AI agent classifies the task (e.g., “Find LinkedIn”, “Get company info”) The relevant MCP tool is called Results are appended back to the sheet or routed to another destination Rerun the specific record by specifying status "needs more enrichment", or leaving it blank. --- 🧩 Use Cases B2B Lead Enrichment – Add missing fields (title, domain, social profiles) Email Intelligence – Validate and enrich based on email Market Research – Pull company or contact data on demand CRM Auto-fill – Push enriched leads to tools like HubSpot or Salesforce --- 🛠️ Customization Prompt Tuning – Adjust how the AI interprets input data Column Mapping – Customize which fields to pull from the sheet Tool Logic – Add retries, fallback tools, or confidence-based routing Destination Output – Integrate with CRMs, Slack, or webhook endpoints --- ✅ Summary This template turns a Google Sheet into an AI-powered lead enrichment engine. By combining Bright Data’s tools with a natural language AI agent, your team can automate repetitive tasks and scale lead ops—without writing code. Just add a row, and let the workflow do the rest.
By Cyril Nicko GasparMulti platform content generator from YouTube using AI & RSS
This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Multi Platform Content Generator from YouTube using AI & RSS This workflow automates content generation by monitoring YouTube channels, extracting transcripts via AI, and creating platform-optimized content for LinkedIn, X/Twitter, Threads, and Instagram. Ideal for creators, marketers, and social media managers aiming to scale content production with minimal effort. ✨ Key Features 🔔 Automated YouTube Monitoring via RSS feed 🧠 AI-Powered Transcript Extraction using Supadata API ✍️ Multi-Platform Content Generation with OpenRouter AI 🎯 Platform Optimization based on tone and character limits 📬 Telegram Notification for easy preview 📊 Centralized Data Management via Google Sheets > 🗂️ All video data, summaries, and generated content are tracked and stored in a single, centralized Google Sheets template > This ensures full visibility, easy access, and smooth collaboration across your team. --- ⚙️ Workflow Components 🧭 Channel Monitoring Schedule Trigger: Initiates workflow periodically Google Sheets (Read): Pulls YouTube channel URLs HTTP Request + HTML Parser: Extracts channel IDs from URLs RSS Reader: Fetches latest video metadata 🧾 Content Processing Supadata API: Extracts transcript from YouTube video OpenRouter AI: Summarizes transcript + generates content per platform Conditional Check: Prevents duplicate content by checking existing records 📤 Multi-Platform Output LinkedIn: Story-driven format (≤ 1300 characters) X/Twitter: Short, punchy copy (≤ 280 characters) Threads: Friendly, conversational Instagram: Short captions for visual posts 🗃️ Data Management Google Sheets (Write): Stores video metadata + generated posts Telegram Bot: Sends content preview ID Tracking: Avoids reprocessing using video ID --- 🔐 Required Credentials Google Sheets OAuth2 Supadata API OpenRouter API Telegram Bot Token & Chat ID --- 🎁 Benefits ⌛ Save Time: Automates transcript + content generation 🔊 Consistent Tone: Adjust AI prompts for brand voice 📡 Multi-Platform Ready: One video → multiple formats 📂 Centralized Logs via Google Sheets: Easily track, audit, and collaborate 🚀 Scalable: Handle many channels with ease
By Budi SJDiscover company data by name with uProc
Do you want to discover company-related information to enrich a signup process? This workflow enriches any company by name using the uProc Get Company by Name tool. This tool combines Google Maps and emails research on the internet to return results. You get no results if the company has no presence on Google Maps. You need to add your credentials (Email and API Key - real -) located at Integration section to n8n. You can replace node "Create Company Item" with any other supported service returning Company names and countries, like Hubspot, Google Sheets, MySQL, or Typeform. You can set up the uProc node with several parameters: country: the country name you want to use. name: the name of the company you need to locate. Every "uProc" node returns the next fields per every located company: name: Contains the company's given name. email: Contains the company's given email. cif: Contains company's cif number. address: Contains company's formatted address. city: Contains the city location of the company. state: Contains province location of the company. county: Contains state location of the company country: Contains country location of the company zipcode: Contains zipcode code of the company phone: Contains phone number of the company website: Contains website of the company latitude: Contains latitude of the company longitude: Contains longitude of the company Next, you can save results to a CRM or Google Sheets, and prepare returned email or phone to launch an email or telemarketing campaign.
By Miquel ColomerAutomate job search & curation with JSearch API & Google Sheets
How it works This workflow automates the job curation process by retrieving pending job search inputs from a spreadsheet, querying the JSearch API for relevant job listings, and writing the curated results back to another sheet. It is designed to streamline job discovery and reduce manual data entry. Step-by-step Trigger & Input The workflow starts on a defined schedule (e.g., once per day). It reads a row from the Job Scraper sheet where the status is marked as "Pending". The selected row includes fields like Position and Location, which are used to build the search query. Job Search & Processing Sends a search request to the JSearch API using the Position and Location from the spreadsheet. Parses the API response and extracts individual job listings. Filters out empty, irrelevant, or invalid entries to ensure clean and relevant job data. Output & Status Update Writes valid job listings to the Job Listing output sheet with fields such as job title, company name, location, and more. Updates the original row in the source sheet to mark it as Scraped, ensuring it will not be processed again in future runs. Benefits Reduces manual effort in job research and listing. Ensures only valid, structured data is stored and used. Prevents duplicate processing with automatic status updates. Simple to expand by adding more job sources or filters.
By Avkash KakdiyaJira project management automation with Google Gemini & MCP server
Jira MCP Server Integration with n8n Overview Transform your Jira project management with the power of AI and automation! This n8n workflow template demonstrates how to create a seamless integration between chat interfaces, AI processing, and Jira Software using MCP (Model Context Protocol) server architecture. What This Workflow Does Chat-Driven Automation: Trigger Jira operations through simple chat messages AI-Powered Issue Creation: Automatically generate detailed Jira issues with descriptions and acceptance criteria Complete Jira Management: Get issue status, changelogs, comments, and perform full CRUD operations Memory Integration: Maintain context across conversations for smarter automations Zero Manual Entry: Eliminate repetitive data entry and human errors Key Features ✅ Natural Language Processing: Use Google Gemini to understand and process chat requests ✅ MCP Server Integration: Secure, efficient communication with Jira APIs ✅ Comprehensive Jira Operations: Create, read, update, delete issues and comments ✅ Smart Memory: Context-aware conversations for better automation ✅ Multi-Action Workflow: Handle multiple Jira operations from a single trigger Demo Video 🎥 Watch the Complete Demo: Automate Jira Issue Creation with n8n & AI | MCP Server Integration Prerequisites Before setting up this workflow, ensure you have: n8n instance (cloud or self-hosted) Jira Software account with appropriate permissions Google Gemini API credentials MCP Server configured and accessible Basic understanding of n8n workflows Setup Guide Step 1: Import the Workflow Copy the workflow JSON from this template In your n8n instance, click Import > From Text Paste the JSON and click Import Step 2: Configure Google Gemini Open the Google Gemini Chat Model node Add your Google Gemini API credentials Configure the model parameters: Model: gemini-pro (recommended) Temperature: 0.7 for balanced creativity Max tokens: As per your requirements Step 3: Set Up MCP Server Connection Configure the MCP Client node: Server URL: Your MCP server endpoint Authentication: Add required credentials Timeout: Set appropriate timeout values Ensure your MCP server supports Jira operations: Issue creation and retrieval Comment management Status updates Changelog access Step 4: Configure Jira Integration Set up Jira credentials in n8n: Go to Credentials > Add Credential Select Jira Software API Add your Jira instance URL, email, and API token Configure each Jira node: Get Issue Status: Set project key and filters Create Issue: Define issue type and required fields Manage Comments: Set permissions and content rules Step 5: Memory Configuration Configure the Simple Memory node: Set memory key for conversation context Define memory retention duration Configure memory scope (user/session level) Step 6: Chat Trigger Setup Configure the When Chat Message Received trigger: Set up webhook URL or chat platform integration Define message filters if needed Test the trigger with sample messages Usage Examples Creating a Jira Issue Chat Input: Can you create an issue in Jira for Login Page with detailed description and acceptance criteria? Expected Output: New Jira issue created with structured description Automatically generated acceptance criteria Proper labeling and categorization Getting Issue Status Chat Input: What's the status of issue PROJ-123? Expected Output: Current issue status Last updated information Assigned user details Managing Comments Chat Input: Add a comment to issue PROJ-123: "Ready for testing in staging environment" Expected Output: Comment added to specified issue Notification sent to relevant team members Customization Options Extending Jira Operations Add more Jira operations (transitions, watchers, attachments) Implement custom field handling Create multi-project workflows AI Enhancement Fine-tune Gemini prompts for better issue descriptions Add custom validation rules Implement approval workflows Integration Expansion Connect to Slack, Discord, or Teams Add email notifications Integrate with time tracking tools Troubleshooting Common Issues MCP Server Connection Failed Verify server URL and credentials Check network connectivity Ensure MCP server is running and accessible Jira API Errors Validate Jira credentials and permissions Check project access rights Verify issue type and field configurations AI Response Issues Review Gemini API quotas and limits Adjust prompt engineering for better results Check model parameters and settings Performance Tips Optimize memory usage for long conversations Implement rate limiting for API calls Use error handling and retry mechanisms Monitor workflow execution times Best Practices Security: Store all credentials securely using n8n's credential system Testing: Test each node individually before running the complete workflow Monitoring: Set up alerts for workflow failures and API limits Documentation: Keep track of custom configurations and modifications Backup: Regular backup of workflow configurations and credentials Happy Automating! 🚀 This workflow template is designed to boost productivity and eliminate manual Jira management tasks. Customize it according to your team's specific needs and processes.
By Rohit DabraGoogle Sheets UI for n8n Workflow
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.
By Viktor KlepikovskyiAutomate Dutch Public Procurement Data Collection with TenderNed
TenderNed Public Procurement What This Workflow Does This workflow automates the collection of public procurement data from TenderNed (the official Dutch tender platform). It: Fetches the latest tender publications from the TenderNed API Retrieves detailed information in both XML and JSON formats for each tender Parses and extracts key information like organization names, titles, descriptions, and reference numbers Filters results based on your custom criteria Stores the data in a database for easy querying and analysis Setup Instructions This template comes with sticky notes providing step-by-step instructions in Dutch and various query options you can customize. Prerequisites TenderNed API Access - Register at TenderNed for API credentials Configuration Steps Set up TenderNed credentials: Add HTTP Basic Auth credentials with your TenderNed API username and password Apply these credentials to the three HTTP Request nodes: "Tenderned Publicaties" "Haal XML Details" "Haal JSON Details" Customize filters: Modify the "Filter op ..." node to match your specific requirements Examples: specific organizations, contract values, regions, etc. How It Works Step 1: Trigger The workflow can be triggered either manually for testing or automatically on a daily schedule. Step 2: Fetch Publications Makes an API call to TenderNed to retrieve a list of recent publications (up to 100 per request). Step 3: Process & Split Extracts the tender array from the response and splits it into individual items for processing. Step 4: Fetch Details For each tender, the workflow makes two parallel API calls: XML endpoint - Retrieves the complete tender documentation in XML format JSON endpoint - Fetches metadata including reference numbers and keywords Step 5: Parse & Merge Parses the XML data and merges it with the JSON metadata and batch information into a single data structure. Step 6: Extract Fields Maps the raw API data to clean, structured fields including: Publication ID and date Organization name Tender title and description Reference numbers (kenmerk, TED number) Step 7: Filter Applies your custom filter criteria to focus on relevant tenders only. Step 8: Store Inserts the processed data into your database for storage and future analysis. Customization Tips Modify API Parameters In the "Tenderned Publicaties" node, you can adjust: offset: Starting position for pagination size: Number of results per request (max 100) Add query parameters for date ranges, status filters, etc. Add More Fields Extend the "Splits Alle Velden" node to extract additional fields from the XML/JSON data, such as: Contract value estimates Deadline dates CPV codes (procurement classification) Contact information Integrate Notifications Add a Slack, Email, or Discord node after the filter to get notified about new matching tenders. Incremental Updates Modify the workflow to only fetch new tenders by: Storing the last execution timestamp Adding date filters to the API query Only processing publications newer than the last run Troubleshooting No data returned? Verify your TenderNed API credentials are correct Check that you have setup youre filter proper Need help setting this up or interested in a complete tender analysis solution? Get in touch 🔗 LinkedIn – Wessel Bulte
By Wessel BulteConvert RSS feeds into LinkedIn & X posts with GPT-4o & AI images & approval
🚀 Overview Stop letting your valuable blog content collect dust! This automation is your 24/7 content repurposing engine. It monitors any blog or news feed you choose. The moment a new article goes live, it instantly creates engaging, platform-aware posts for LinkedIn and X (Twitter), generates a custom image with AI, and packages it all up for your review. You get the final say with a simple approval email, ensuring your brand voice stays perfectly on point. --- 😩 The Problem You've invested hours writing the perfect blog post. But the work isn't over. Now you have to manually copy, paste, and rewrite that content for different social platforms. You need a professional tone for LinkedIn, something short and snappy for X, and you have to find or create visuals for both. This tedious, repetitive task drains your creative energy and leads to your social media presence becoming stale or inconsistent. --- ✨ The Solution This workflow acts as your automated content marketing assistant, elegantly solving the problem. Here’s the magic in action: New Blog Post Detected: The workflow constantly watches a specific RSS feed (e.g., your company blog). AI Content Generation: When a new post appears, the AI (powered by OpenAI GPT-4o-mini) reads the article, performs a quick web search for context (via SerpAPI), and drafts optimized posts for LinkedIn and X. It also generates a brand-new image based on the article’s topic. Secure Image Hosting: The generated image is uploaded to your Imgbb account for a shareable link. Approval Workflow: A notification is sent directly to your email inbox with the generated text and image for review. One-Click Go-Live: If approved, the workflow automatically publishes the content to the correct social media profiles. --- ⚙️ Setup Instructions What You'll Need RSS Feed URL (e.g., https://yourwebsite.com/feed) OpenAI API key (for content + image generation) SerpAPI API key (optional, for extra context) Imgbb API key (for image hosting) Connected Accounts: Gmail, LinkedIn, and X (Twitter) Steps Upload: Import the JSON file into n8n. Connect: Link your credentials for Gmail, LinkedIn, X, OpenAI, SerpAPI, and add Imgbb with “Header Auth.” Configure: Add your RSS URL to the RSS Feed Trigger node. Update the “Send To” field in both Gmail User for Approval nodes with your own email. Activate: Switch on the workflow and let automation handle the heavy lifting. --- 🎨 Customizations Expand to More Networks: Duplicate an existing branch to add Facebook or Instagram. Add Smart Filters: Only generate posts for articles containing specific keywords (e.g., “AI” or “Case Study”).
By Abdellah Homrani