Browse 12,955 Workflows

By Oneclick AI Squad

In this guide, we’ll walk you through setting up an AI-driven workflow that automatically processes highly-rated food photos from a Google Sheet, generates AI-powered captions, shares them to Pinterest, and updates the sheet to reflect the posts. Ready to automate your food photo sharing? Let’s dive in! What’s the Goal? Automatically detect and process highly-rated food photos (4 stars or above) from a Google Sheet. Use AI to generate engaging and relevant captions. Share the photos with captions to Pinterest via the Pinterest API. Update the Google Sheet to mark photos as posted. Enable scheduled automation for consistent posting. By the end, you’ll have a self-running system that shares your best food photos effortlessly. Why Does It Matter? Manual photo sharing is time-consuming and inconsistent. Here’s why this workflow is a game changer: Zero Human Error**: AI ensures consistent captions and posting accuracy. Time-Saving Automation**: Automatically handle photo sharing, boosting efficiency. Scheduled Posting**: Maintain a regular presence on Pinterest without manual effort. Focus on Creativity**: Free your team from repetitive posting tasks. Think of it as your tireless social media assistant that keeps your Pinterest feed vibrant. How It Works Here’s the step-by-step magic behind the automation: Step 1: Trigger the Workflow Detect new photos to post using the Daily Post Scheduler node (e.g., once daily). Initiate the workflow at a scheduled time to check for new food photos. Step 2: Fetch Food Photos from Sheet Retrieve rows from the Google Sheet that contain food photo metadata like image URLs, ratings, and status. Step 3: Filter 4+ Star Dishes Filter only those food entries with high ratings (4 stars or above) and unposted status. Step 4: AI Caption Generator Use AI (e.g., GPT/OpenAI) to create engaging and relevant captions for selected food photos. Step 5: Upload to Pinterest Automatically post the food photo with the generated caption to Pinterest via the Pinterest API. Step 6: Mark as Posted in Sheet Update the Google Sheet to reflect that the photo has been successfully shared. How to Use the Workflow? Importing a workflow in n8n is a straightforward process that allows you to use pre-built workflows to save time. Below is a step-by-step guide to importing the Automated Food Photo Sharing workflow in n8n. Steps to Import a Workflow in n8n Obtain the Workflow JSON Source the Workflow: Workflows are shared as JSON files or code snippets, e.g., from the n8n community, a colleague, or exported from another n8n instance. Format: Ensure you have the workflow in JSON format, either as a file (e.g., workflow.json) or copied text. Access the n8n Workflow Editor Log in to n8n (via n8n Cloud or self-hosted instance). Navigate to the Workflows tab in the n8n dashboard. Click Add Workflow to create a blank workflow. Import the Workflow Option 1: Import via JSON Code (Clipboard): Click the three dots (⋯) in the top-right corner to open the menu. Select Import from Clipboard. Paste the JSON code into the text box. Click Import to load the workflow. Option 2: Import via JSON File: Click the three dots (⋯) in the top-right corner. Select Import from File. Choose the .json file from your computer. Click Open to import. Setup Notes Google Sheet Columns**: Ensure your Google Sheet includes the following columns: Image URL, Rating (numeric, e.g., 1-5), Feedback (text), Pin Title, Pin Description, Destination URL, Board ID, and Status (e.g., "Pending" or "Posted"). Google Sheets Credentials**: Configure OAuth2 settings in the Fetch Food Photos node with your Google Sheet ID and credentials. AI Model**: Set up the AI Caption Generator node with OpenAI credentials (e.g., API key). Pinterest API**: Authorize the Upload to Pinterest node with Pinterest API credentials (e.g., Bearer Token) and obtain the Board ID. Scheduling**: Adjust the Daily Post Scheduler node to your preferred posting time (e.g., daily at 9 AM). An n8n automation workflow template by Oneclick AI Squad.

By plemeo

Who’s it for Marketing, growth, and automation teams that need to ship polished bilingual newsletters—complete with images, optional video, and multi-channel distribution—without writing a line of code. How it works / What it does A Webhook receives a plain-language request (e.g. “Create a newsletter about XYZ and store it in SharePoint”). The Webhook can be changed to another trigger, fr example Manual Trigger. An AI Agent (OpenAI GPT-4o) drafts German & English newsletter copy following a strict JSON schema. SharePoint fetches an HTML template; a Code node injects AI copy into the placeholders. Optional creative assets: Pollinations AI → image (resolution check & frame overlay) FAL AI → short video + Lyria2 audio → merged via FFmpeg API A Gmail Approval step sends the draft to an approver; the flow continues only on “Approve”. Depending on user intent the workflow: Emails the newsletter to HubSpot contacts, and/or Saves HTML (+ JPG/Video URL) to a SharePoint library. How to set up Import the template Open the yellow sticky note and follow the checklist Create credentials for OpenAI, HubSpot (App Token), Microsoft 365, Gmail, and FAL AI. Enter them in n8n Credential Manager (never in the HTTP node). Edit the Configuration Settings Set node with your ENV_* variables. Activate the workflow, copy the production Webhook URL, and trigger it with JSON body { "text": "…" } or with a desired trigger. Requirements OpenAI account Microsoft 365 tenant (SharePoint & Outlook) HubSpot App Token FAL AI API Key (video & audio generation) How to customize Want a different language? Tweak the AI prompt inside AI Agent. Want a different storage provider? Change the SharePoint nodes to Google Drive or Dropbox. Skip video generation: set include_video to false in the incoming prompt. Change the distribution logic by adjusting the WF Result Switch node. Add more channels (e.g. Slack) by inserting additional branches after the Switch and modifying the intent determination at the beginning of the workflow. An n8n automation workflow template by plemeo.

By Oneclick AI Squad

This automated n8n workflow monitors ingredient price changes from external APIs or manual sources, analyzes historical trends, and provides smart buying recommendations. The system tracks price fluctuations in a PostgreSQL database, generates actionable insights, and sends alerts via email and Slack to help restaurants optimize their purchasing decisions. What is Price Trend Analysis? Price trend analysis uses historical price data to identify patterns and predict optimal buying opportunities. The system analyzes price movements over time and generates recommendations on when to buy ingredients based on current trends and historical patterns. Good to Know Price data accuracy depends on the reliability of external API sources Historical data improves recommendation accuracy over time (recommended minimum 30 days) PostgreSQL database provides robust data storage and complex trend analysis capabilities Real-time alerts help capture optimal buying opportunities Dashboard provides visual insights into price trends and recommendations How It Works Daily Price Check - Triggers the workflow daily to monitor price changes Fetch API Prices - Retrieves the latest prices from an external ingredient pricing API Setup Database - Ensures database tables are ready before inserting new data Store Price Data - Saves current prices to the PostgreSQL database for tracking Calculate Trends - Analyzes historical prices to detect patterns and price movements Generate Recommendations - Suggests actions based on price trends (buy/wait/stock up) Store Recommendations - Saves recommendations for future reporting Get Dashboard Data - Gathers necessary data for dashboard generation Generate Dashboard HTML - Builds an HTML dashboard to visualize insights Send Email Report - Emails the dashboard report to stakeholders Send Slack Alert - Sends key alerts or recommendations to Slack channels Database Structure The workflow uses PostgreSQL with two main tables: price_history - Historical price tracking with columns: id (Primary Key) ingredient (VARCHAR 100) - Name of the ingredient price (DECIMAL 10,2) - Current price value unit (VARCHAR 50) - Unit of measurement (kg, lbs, etc.) supplier (VARCHAR 100) - Source supplier name timestamp (TIMESTAMP) - When the price was recorded created_at (TIMESTAMP) - Record creation time buying_recommendations - AI-generated buying suggestions with columns: id (Primary Key) ingredient (VARCHAR 100) - Ingredient name current_price (DECIMAL 10,2) - Latest price price_change_percent (DECIMAL 5,2) - Percentage change from previous price trend (VARCHAR 20) - Price trend direction (INCREASING/DECREASING/STABLE) recommendation (VARCHAR 50) - Buying action (BUY_NOW/WAIT/STOCK_UP) urgency (VARCHAR 20) - Urgency level (HIGH/MEDIUM/LOW) reason (TEXT) - Explanation for the recommendation generated_at (TIMESTAMP) - When recommendation was created Price Trend Analysis The system analyzes historical price data over the last 30 days to calculate percentage changes, identify trends (INCREASING/DECREASING/STABLE), and generate actionable buying recommendations based on price patterns and movement history. How to Use Import the workflow into n8n Configure PostgreSQL database connection credentials Set up external ingredient pricing API access Configure email credentials for dashboard reports Set up Slack webhook or bot credentials for alerts Run the Setup Database node to create required tables and indexes Test with sample ingredient data to verify price tracking and recommendations Adjust trend analysis parameters based on your purchasing patterns Monitor recommendations and refine thresholds based on actual buying decisions Requirements PostgreSQL database access External ingredient pricing API credentials Email service credentials (Gmail, SMTP, etc.) Slack webhook URL or bot credentials Historical price data for initial trend analysis Customizing This Workflow Modify the Calculate Trends node to adjust the analysis period (currently 30 days) or add seasonal adjustments. Customize the recommendation logic to match your restaurant's buying patterns, budget constraints, or supplier agreements. Add additional data sources like weather forecasts or market reports for more sophisticated predictions. An n8n automation workflow template by Oneclick AI Squad.

By Marth - Business Automation

How it works This workflow runs on a daily schedule. It starts by scraping real estate-related queries from Google using Apify. The organic search results are parsed and summarized into a single text block. That text is then sent to an AI model (GPT-4o) which extracts the top 3 pain points faced by real estate agents based on current online sentiment. The workflow compares today's insights with yesterday's data stored in Airtable to detect recurring or new pain points. Finally, it sends a summary notification via Telegram and stores the current day's insights into Airtable for trend tracking. How to set up Clone or import the workflow into your n8n instance. Get an Apify API token and insert it into the HTTP Request node. Create an Airtable base with a table containing two fields: "Date" (text) and "Summary" (long text). Copy the Base ID and Table ID into the Airtable nodes. Connect your Telegram bot and replace the chat ID in the Telegram node. Set up OpenAI credentials with GPT-4o or GPT-4o-mini for the LLM node. Run once manually to test, then activate the schedule trigger to run daily. (Optional) Extend the flow to generate cold outreach emails based on pain points, or sync to Notion/CRM. An n8n automation workflow template by Marth - Business Automation.

By Oneclick AI Squad

This automated n8n workflow performs weekly forecasting of restaurant sales and raw material requirements using historical data from Google Sheets and AI predictions powered by Google Gemini. The forecast is then emailed to stakeholders for efficient planning and waste reduction. What is Google Gemini AI? Google Gemini is an advanced AI model that analyzes historical sales data, seasonal patterns, and market trends to generate accurate forecasts for restaurant sales and inventory requirements, helping optimize purchasing decisions and reduce waste. Good to Know Google Gemini AI forecasting accuracy improves over time with more historical data Weekly forecasting provides better strategic planning compared to daily predictions Google Sheets access must be properly authorized to avoid data sync issues Email notifications ensure timely review of weekly forecasts by stakeholders The system analyzes trends and predicts upcoming needs for efficient planning and waste reduction How It Works Trigger Weekly Forecast - Automatically starts the workflow every week at a scheduled time Load Historical Sales Data - Pulls weekly sales and material usage data from Google Sheets Format Input for AI Agent - Transforms raw data into a structured format suitable for the AI Agent Generate Forecast with AI - Uses Gemini AI to analyze trends and predict upcoming needs Interpret AI Forecast Output - Parses the AI's response into readable, usable JSON format Log Forecast to Google Sheets - Stores the new forecast data back into a Google Sheet Email Forecast Summary - Sends a summary of the forecast via Gmail for stakeholder review Data Sources The workflow utilizes Google Sheets as the primary data source: Historical Sales Data Sheet - Contains weekly sales and inventory data with columns: Week/Date (date) Menu Item (text) Sales Quantity (number) Revenue (currency) Raw Material Used (number) Inventory Level (number) Category (text) Forecast Output Sheet - Contains AI-generated predictions with columns: Forecast Week (date) Menu Item (text) Predicted Sales (number) Recommended Inventory (number) Material Requirements (number) Confidence Level (percentage) Notes (text) How to Use Import the workflow into n8n Configure Google Sheets API access and authorize the application Set up Gmail credentials for forecast report delivery Create the required Google Sheets with the specified column structures Configure Google Gemini AI API credentials Test with sample historical sales data to verify predictions and email delivery Adjust forecasting parameters based on your restaurant's specific needs Monitor and refine the system based on actual vs. predicted results Requirements Google Sheets API access Gmail API credentials Google Gemini AI API credentials Historical sales and inventory data for initial training Customizing This Workflow Modify the Generate Forecast with AI node to focus on specific menu categories, seasonal adjustments, or local market conditions. Adjust the email summary format to match your restaurant's reporting preferences and add additional data sources like supplier information, weather data, or special events calendar for more accurate predictions. An n8n automation workflow template by Oneclick AI Squad.

By Oneclick AI Squad

This automated n8n workflow performs daily forecasting of sales and raw material needs for a restaurant. By analyzing historical data and predicting future usage with AI, businesses can minimize food waste, optimize inventory, and improve operational efficiency. The forecast is stored in Google Sheets and sent via email for easy review by staff and management. What is AI Forecast Generator? The AI Forecast Generator is a machine learning component that analyzes historical sales data, weather patterns, and seasonal trends to predict future food demand and recommend optimal inventory levels to minimize waste. Good to Know AI forecasting accuracy improves over time with more historical data Weather and seasonal factors significantly impact food demand predictions Google Sheets access must be properly authorized to avoid data sync issues Email notifications help ensure timely review of daily forecasts The system works with two main data sources: historical food wastage data and predicted low-waste food requirements How It Works Daily Trigger - Initiates the workflow every day to perform food waste prediction Fetch Historical Sales Data - Reads past food usage & sales data from Google Sheets to understand trends Format Data for AI Forecasting - Cleans and organizes raw data into a structured format for AI processing AI Forecast Generator - Uses Gemini AI to forecast food demand and recommend waste reduction strategies Clean & Structure AI Output - Parses AI response into structured and actionable format for reporting Log Forecast to Google Sheets - Stores AI-generated forecast back into Google Sheets for historical tracking Create Email Summary - Creates a concise, human-friendly summary of the forecast findings Send Email Forecast Report - Delivers the forecast report via email to decision makers and management Data Sources The workflow utilizes two Google Sheets: Food Wastage Data Sheet - Contains historical data with columns: Date (date) Food Item (text) Quantity Wasted (number) Cost Impact (currency) Category (text) Reason for Waste (text) Predicted Food Data Sheet - Contains AI predictions with columns: Date (date) Food Item (text) Predicted Demand (number) Recommended Order Quantity (number) Waste Risk Level (text) Optimization Notes (text) How to Use Import the workflow into n8n Configure Google Sheets API access and authorize the application Set up email credentials for forecast report delivery Create the two required Google Sheets with the specified column structures Configure the AI model credentials (Gemini API key) Test with sample historical data to verify predictions and email delivery Adjust forecasting parameters based on your restaurant's specific needs Monitor and refine the system based on actual vs. predicted results Requirements Google Sheets API access Email service credentials (Gmail, SMTP, etc.) AI model API credentials (Gemini AI) Historical food wastage data for initial training Customizing This Workflow Modify the AI Forecast Generator prompts to focus on specific food categories, seasonal adjustments, or local market conditions. Adjust the email summary format to match your restaurant's reporting preferences and add additional data sources like supplier information or menu planning data. An n8n automation workflow template by Oneclick AI Squad.

By Alex Huy

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Description This n8n workflow automatically scrapes Airbnb listings from a specified location and saves the data to a Google Sheet. It performs pagination to collect listings across multiple pages, extracts detailed information for each property, and organizes the data in a structured format for easy analysis. How it Works The workflow operates through these high-level steps: Search Initialization: Starts with an Airbnb search for a specific location (London) with defined check-in/check-out dates and guest count Pagination Loop: Automatically processes multiple pages of search results using cursor-based pagination Data Extraction: Parses listing information including names, prices, ratings, reviews, and URLs Detail Enhancement: Fetches additional details for each listing (house rules, highlights, descriptions, amenities) Data Storage: Saves all collected data to a Google Sheet with proper formatting Loop Control: Continues until reaching the page limit (2 pages) or no more results are available Setup Steps Prerequisites n8n instance with MCP (Model Context Protocol) support Google Sheets API credentials configured Airbnb MCP client properly set up Configuration Steps Configure MCP Client Set up the Airbnb MCP client with credential ID: Ensure the client has access to airbnb_search and airbnb_listing_details tools Google Sheets Setup Create a Google Sheet with ID: 15IOJquaQ8CBtFilmFTuW8UFijux10NwSVzStyNJ1MsA Configure Google Sheets OAuth2 credentials (ID: 6YhBlgb8cXMN3Ra2) Ensure the sheet has these column headers: "id, name, url, price_per_night, total_price, price_details beds_rooms, rating, reviews, badge, location houseRules, highlights, description, amenities" Search Parameters Location: "London" (can be modified in the "Airbnb Search" node) Adults: 7 Children: 1 Check-in: "2025-08-14" Check-out: "2025-08-17" Page limit: 2 (can be adjusted in the "If1" condition node) Execution Use the manual trigger "When clicking 'Execute workflow'" to start the process Monitor the workflow execution through the n8n interface Check the Google Sheet for populated data after completion Key Features Automatic Pagination: Processes multiple pages without manual intervention Comprehensive Data: Extracts both basic listing info and detailed property information Error Handling: Includes JSON parsing error handling and data validation Batch Processing: Uses split batches for efficient processing of individual listings Real-time Updates: Appends new data to existing Google Sheet records Output Data Structure Each listing contains: Basic info: ID, name, URL, pricing details, room/bed count Ratings: Average rating and review count Location: Latitude and longitude coordinates Enhanced details: House rules, highlights, descriptions, amenities Metadata: Page number, check-in/out dates, badges. An n8n automation workflow template by Alex Huy.

By Yaron Been

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. This workflow automatically monitors customer support forums and Q&A platforms to extract valuable customer insights and pain points. It saves you time by eliminating the need to manually browse through forum discussions and provides structured analysis of customer questions, answers, and recurring issues. Overview This workflow automatically scrapes customer support forums like Stack Exchange and SuperUser to find questions and discussions related to specific topics or brands. It uses AI to analyze forum content, extract customer pain points, and identify recurring issues, then sends structured insights directly to your product team via email. Tools Used n8n**: The automation platform that orchestrates the workflow Bright Data**: For scraping forum pages and Q&A platforms without being blocked OpenAI**: AI agent for intelligent forum content analysis and insight extraction Gmail**: For sending automated insight reports to your team How to Install Import the Workflow: Download the .json file and import it into your n8n instance Configure Bright Data: Add your Bright Data credentials to the MCP Client node Set Up OpenAI: Configure your OpenAI API credentials Configure Gmail: Connect your Gmail account for sending team notifications Customize: Set target forum URLs and define the topics or brands to monitor Use Cases Product Teams**: Identify customer pain points and feature requests from forum discussions Customer Support**: Monitor common issues and questions customers are asking Market Research**: Understand customer needs and challenges in your industry Competitive Analysis**: Track how customers discuss competitor products and services Connect with Me Website**: https://www.nofluff.online YouTube**: https://www.youtube.com/@YaronBeen/videos LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Get Bright Data**: https://get.brightdata.com/1tndi4600b25 (Using this link supports my free workflows with a small commission) #n8n #automation #forummonitoring #customersupport #brightdata #webscraping #customerinsights #n8nworkflow #workflow #nocode #forumautomation #customerresearch #supportmonitoring #painpointanalysis #communitymonitoring #forumanalysis #customerfeedback #productinsights #supportforums #stackexchange #customervoice #userresearch #productfeedback #techsupport #communitylistening #customerexperience #supportanalysis #forumdata #qandamonitoring #customerpainpoints. An n8n automation workflow template by Yaron Been.

By phil

This workflow is your ultimate solution for reliable image retrieval from any web source, including those heavily protected. It operates with a smart, cost-effective strategy: it first attempts to fetch the image using a Classic Image Getter node (a standard, free HTTP request). In approximately 80% of cases, this method will be sufficient. However, for the remaining instances where you encounter IP blocking, CAPTCHAs, rate limiting, or other advanced anti-bot measures, the workflow seamlessly switches to a robust BrightData Web Unblocker service as a fallback. It leverages BrightData’s Image Unblocker to retrieve these blocked images. This template is indispensable for anyone needing consistent and complete access to web images, ensuring you get the data you need without unnecessary overhead. Why Use This Image Scrapper Workflow? Maximum Success Rate**: Retrieves images even from the most challenging or protected websites. Cost-Optimized Strategy**: Prioritizes free, standard HTTP requests, only incurring costs when advanced unblocking is truly necessary. Automated Resilience**: Intelligently handles failed direct attempts by automatically engaging the BrightData failover via the "Unlock Image" node. Versatile Image Scraping**: Perfect for market research, content aggregation, or data enrichment that demands reliable image access. How It Works When clicking ‘Execute workflow’: The workflow is initiated manually, allowing for easy testing and integration into larger processes. image: A Set node defines the target image URL. This can be easily configured to accept dynamic URLs from preceding nodes. Classic Image Getter: This HTTP Request node performs a direct image download. It's the primary, free, and efficient method for readily accessible images. Unlock Image (BrightData Web Unblocker): Configured as an error handler and failover, this HTTP Request node activates only if the "Classic Image Getter" encounters an error. It then routes the image URL through BrightData's Web Unblocker, designed to bypass advanced protective measures and successfully retrieve the image data. 🔑 Prerequisites To enable the advanced capabilities of this workflow, specifically the BrightData Web Unblocker functionality, you will need a BrightData account and a correctly configured Web Unblocker zone. Setting Up Your BrightData Web Unblocker: BrightData Account: Ensure you have an active account with BrightData. If you don't, you can sign up on their website. Create a Web Unblocker Zone: Log in to your BrightData dashboard. Navigate to the "Proxy & Scraping Infrastructure" section, then "Zones." Click "Add new zone." Select "Web Unblocker" as the product type. Give your zone a clear name (e.g., n8n-image-unlocker). Confirm the creation of the zone. Retrieve API Key: Once your Web Unblocker zone is active, go to its settings. Locate your API Key (often referred to as "password" for proxy access) within the "Access Parameters" or "Credentials" section. Configure in n8n: In the Unlock Image HTTP Request node within this workflow, update the Authorization header. Replace "Bearer yourkey" with "Bearer YOUR_BRIGHTDATA_API_KEY". Important: For production workflows, it's highly recommended to use n8n credentials to store your BrightData API key securely, rather than hardcoding it directly in the node. This template uses a placeholder for demonstration purposes. Crucially, ensure that the zone parameter in the Unlock Image node matches the exact Zone ID you created in your BrightData account. You will need to replace the placeholder web_unlocker with your actual BrightData zone ID. Phil | Inforeole | Linkedin 🇫🇷 Contactez nous pour automatiser vos processus. An n8n automation workflow template by phil.

By scrapeless official

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Brief Overview This automation template helps you track the latest job listings from the Y Combinator Jobs page. By using Scrapeless to scrape job listings, n8n to orchestrate the workflow, and Google Sheets to store the results, you can build a zero-code job tracking solution that runs automatically every 6 hours. How It Works Trigger on a Schedule: Every 6 hours, the workflow kicks off automatically. Scrape Job Listings: Scrapeless crawls the Y Combinator Jobs page and returns structured Markdown data. Extract & Parse Content: JavaScript nodes process the Markdown to extract job titles and links. Flatten Data: Each job becomes a single row with its title and link. Save to Google Sheets: New job listings are appended to your Google Sheet for easy viewing and sharing. Features No-code, automated job listing scraper. Scrapes and structures the latest Y Combinator job posts. Saves data directly to Google Sheets. Easy to schedule and run without manual effort. Extensible: Add Telegram, Slack, or email notifications easily in n8n. Requirements Scrapeless API Key: Scrapeless Service request credentials. Log in to the Scrapeless Dashboard Then click "Setting" on the left -> select "API Key Management" -> click "Create API Key". Finally, click the API Key you created to copy it. n8n Instance: Self-hosted or n8n.cloud account. Google Account: For Google Sheets API access. Target Site: This template is designed for the Y Combinator Jobs page but can be modified for other job boards. Installation Deploy n8n on your preferred platform. Import this workflow JSON file into your n8n workspace. Create and add your Scrapeless API Key in n8n’s credential manager. Connect your Google Sheets account in n8n. Update the target Google Sheet document URL and sheet name. Usage This automated job finder agent is ideal for: | Industry / Role | Use Case | |-------------------------------|--------------------------------------------------------------------------------------------| | Job Seekers | Automatically track newly posted startup jobs without manually visiting job boards. | | Recruitment Agencies | Monitor YC job postings and build a candidate-job matching system. | | Startup Founders / CTOs | Stay aware of which startups are hiring, for networking and market insights. | | Tech Media & Bloggers | Aggregate new job listings for newsletters, blogs, or social media sharing. | | HR & Talent Acquisition Teams | Monitor competitors’ hiring activity. | | Automation Enthusiasts | Example use case for learning web scraping + automation + data storage. | Output. An n8n automation workflow template by scrapeless official.

By Sebastian/OptiLever

Fundamental Analysis, Stock Analysis, and AI Integration in the Fundamental Analysis Tool Overview of the Tool The Fundamental Analysis Tool is an automated workflow designed to evaluate a stock’s fundamentals using financial data and AI-driven insights. Built in the n8n automation platform, it: Collects financial data for a user-specified stock from AlphaVantage. Processes and structures this data for analysis. Analyzes the data using the Mistral AI model to provide expert-level insights. Generates a visually appealing HTML report with charts and delivers it via email. The tool is triggered by a form where users input a stock symbol (e.g., "NVDA" for NVIDIA) and their email address. From there, it follows a three-stage process: data retrieval, data processing, and AI analysis with report generation. 1. Fundamental Analysis: The Foundation Fundamental analysis involves evaluating a company’s intrinsic value by examining its financial health, competitive position, and market environment. This tool performs fundamental analysis by: Data Retrieval Data Types**: Six types of data are retrieved via HTTP requests: Overview: General company details (e.g., sector, industry, market cap). Income Statement: Revenue, net income, and profitability metrics. Balance Sheet: Assets, liabilities, and equity. Cash Flow: Operating, investing, and financing cash flows. Earnings Calendar: Upcoming earnings events. Earnings: Historical earnings data (annual and quarterly). Key Metrics Analyzed The tool structures this data into 8 categories critical to fundamental analysis, as defined in the "Code1" node: Economic Moats & Competitive Advantage: Assesses sustainable advantages (e.g., R&D spending, gross profit). Financial Health & Profitability: Examines ROE, debt levels, and dividend yield. Valuation & Market Sentiment: Evaluates P/E ratio, PEG ratio, and book value. Management & Capital Allocation: Reviews market cap justification and cash allocation (e.g., R&D, buybacks). Industry & Risk Exposure: Analyzes revenue cyclicality and geopolitical risks. Key Metrics to Probe: Investigates net income trends and gross margins. Red Flags: Identifies risks like inventory issues or stock dilution. Final Checklist: Summarizes pricing power and risk/reward potential. These categories cover the core pillars of fundamental analysis, ensuring a holistic evaluation of the stock’s intrinsic value and risks. 2. Stock Analysis: Tailored Insights The tool performs stock-specific analysis by focusing on the user-provided stock symbol. Here’s how it tailors the process: Input and Customization Form Submission**: Users enter a stock symbol (e.g., "NVDA") and email via the "On Form Submission" node. Dynamic Data Fetching**: The "Set Variables" node passes the stock symbol to the API calls, ensuring the analysis is specific to the chosen stock. Processing for Relevance Data Filtering: The workflow limits historical data to the **last 5 years (via the "Limit" node), focusing on recent trends. Merging and Cleaning**: The "Merge" and "Code2" nodes combine and refine the data, removing irrelevant fields (e.g., quarterly reports) and aggregating annual reports for consistency. Output The final report is titled with the stock’s name (e.g., "Fundamental Analysis - NVIDIA"), ensuring the analysis is clearly tied to the user’s chosen stock. This stock-specific approach makes the tool practical for investors analyzing individual companies rather than broad market trends. 3. AI Integration: Expert-Level Insights The integration of AI (via the Mistral model or others) is what sets this tool apart, automating complex analysis and report generation. Here’s how AI is woven into the workflow: Data Preparation for AI Structuring**: The "Code1" node organizes the raw data into a JSON schema aligned with the eight fundamental analysis categories. This structured data is fed into the AI for analysis. AI Analysis Node: "Basic LLM Chain" uses the **Mistral AI model. Prompt**: The AI is instructed to act as an "expert financial advisor with 50 years of experience" and answer specific questions for each category, such as: Economic Moats: "What sustainable competitive advantages protect the company’s margins?" Financial Health: "Is ROE driven by leverage or true profitability?" Red Flags: "Are supply chain issues a concern?" Output**: The AI generates a JSON response with detailed insights, e.g.: { "Economic Moats & Competitive Advantage": "NVIDIA’s leadership in GPU technology and strong R&D investment...", "Financial Health & Profitability": "ROE of 25% is exceptional, driven by profitability rather than leverage...", ... } Validation**: An "Auto-fixing Output Parser" ensures the output adheres to the expected JSON schema, retrying if necessary. Report Enhancement HTML Generation**: The "HTML" node creates an initial report with placeholders for the AI’s insights and Google Charts for visualizations (e.g., ROE trends, revenue growth). AI-Driven Refinement**: The "Basic LLM Chain1" node uses Mistral again to enhance the HTML, adding: Styled tables (e.g., financial ratios). Charts (e.g., bar charts for valuation, line charts for revenue). Visual indicators (e.g., ✅ for positive trends, ⚠️ for risks). Mobile-responsive design with modern fonts (Inter or Roboto). This dual AI approach—one for analysis, one for presentation—ensures the output is both insightful and user-friendly. Strengths and Limitations Strengths Comprehensive**: Covers all key aspects of fundamental analysis. AI-Powered**: Automates expert-level insights and report design. User-Friendly**: Delivers an interactive, visual report via email. Limitations Data Dependency**: Relies on public data, so data quality and timeliness matter. AI Constraints**: Insights depend on AI’s capabilities; it may miss nuanced human judgment. Disclaimer**: The tool notes it’s not investment advice, so users should consult advisors. An n8n automation workflow template by Sebastian/OptiLever.

By Malik Hashir

Purpose & Audience This n8n workflow template is crafted for cryptocurrency traders, analysts, and enthusiasts who want to automate professional-grade market update alerts for BTCUSD and ETHUSD pairs. By integrating multiple trusted news sources with advanced AI-driven sentiment analysis, the agent delivers concise, actionable, and richly formatted updates directly to your Discord or chat platform. Stay informed on price action, market drivers, technical setups, and sentiment shifts—without spending hours sifting through data. What It Does Aggregates real-time news and analysis from leading crypto and financial platforms focused on BTCUSD and ETHUSD. Filters and processes the latest headlines to ensure relevance and timeliness. Uses a powerful language model (Google Gemini or OpenAI) to generate sentiment scores (bullish, bearish, neutral) and contextual summaries. Produces structured, easy-to-digest market update alerts with key trading insights and technical levels. Sends formatted alerts to your preferred Discord channel or chat group on your chosen schedule. Who Is It For? Crypto traders seeking timely, AI-powered market intelligence to inform entry and exit decisions. Analysts and portfolio managers needing automated sentiment summaries and trade ideas. Crypto communities, prop firms, and brokers wanting to enrich their channels with professional market commentary. Anyone looking for a hands-off, plug-and-play solution to monitor BTCUSD and ETHUSD market dynamics. Setup Once, Use Forever Deploy the workflow once and reuse it indefinitely. Easily duplicate and customize for additional pairs or channels. No coding needed, no recurring fees, and full control over update frequency and delivery. How to Set Up Configure the currency pair filters for BTCUSD and ETHUSD within the workflow. Connect your AI model credentials (Google Gemini or OpenAI) and Discord webhook or chat API. Set your preferred alert schedule (daily, multiple times per day, or weekly). Activate the workflow and start receiving professional crypto market alerts automatically. Output Alert Format & Key Sections Each alert is carefully structured with clear visually appealing sections to enhance readability and quick decision-making: Topline Snapshot – 📈📊💡 Concise summary of recent price action and overall sentiment. Market Drivers – 🌍⚖️📉📈 Key macroeconomic, regulatory, and ETF flow updates impacting BTC or ETH. Technical Setup – 📏🔍📐🧭 Critical support/resistance levels, chart patterns, and technical bias. Sentiment Scoreboard – 🟢🔴🟡⚖️ AI-derived sentiment on themes such as ETF flows, network activity, and market positioning. Trade Ideas – 💰🎯📌📉📈 Actionable scenarios for breakouts, reversals, and range plays with entry/stop targets. Key Headlines – 📰🗞️📆🧠 Selected impactful news headlines relevant to the current market context. Summary & Watchpoints – 🧠🔎⏳📅📍 Final analysis, critical price levels to monitor, and upcoming events. An n8n automation workflow template by Malik Hashir.

By Malik Hashir

Purpose & Audience Forex Market AI Analyst is an advanced n8n workflow template designed for Forex traders, analysts, prop firms, brokers, and trading communities who need real-time, actionable market intelligence. By combining multi-source news aggregation and AI-powered sentiment analysis, this workflow delivers both quick alerts and comprehensive sentiment reports for any currency pair—directly to your Discord or chat platform. Stay ahead of market shifts and reduce manual research with automated, context-rich updates. What It Does Aggregates breaking news and analysis from top Forex and macroeconomic sources for your selected currency pair. Filters news by recency and relevance, ensuring only the most current and impactful headlines are included. Analyzes market sentiment (bullish, bearish, or neutral) using a state-of-the-art language model (LLM). Summarizes key themes, technical levels, and economic drivers in a clear, structured format with visual cues. Delivers updates to your chosen Discord channel or chat group, with two distinct modes: Quick Alerts: Fast, headline-focused updates for daily trading. Full Reports: Detailed, multi-section sentiment breakdowns for weekly or in-depth review. Customizable date filters let you control how recent news must be for inclusion in sentiment analysis. Who Is It For? Forex traders seeking an edge with instant, unbiased market sentiment. Analysts and prop firms needing reliable, automated news curation and structured reporting. Brokers and trading communities looking to enrich their channels with high-quality, automated market insights. Anyone who wants a “set it and forget it” solution for monitoring any FX pair—no coding required. Setup Once, Use Forever Deploy the workflow once and use it for a lifetime. Duplicate for as many currency pairs as you need—customize the news sources or filters as you wish. No recurring fees, no complex setup, and you control the update frequency and delivery channels. How to Set Up Select your currency pair and adjust the news filter settings as desired. Connect your AI model (Google Gemini or OpenAI) and Discord (or other chat) credentials. Choose your alert mode: quick daily alerts, full weekly reports, or both. Set your preferred schedule for updates. Go live: Receive real-time, actionable FX news and sentiment in your Discord or chat, automatically. Forex Market AI Analyst—the all-in-one workflow for automated Forex news, sentiment, and technical updates. Perfect for traders, analysts, teams, and anyone who values timely, structured market intelligence. An n8n automation workflow template by Malik Hashir.

By Sebastian/OptiLever

Overview of the Workflow The automation process consists of four main steps: Get Longform: Retrieve the long-form video data (e.g., from Google Sheets). Analyze Longform: Use Clap to analyze the video and generate short clips. Produce Shorts: Export the generated clips. Publish Shorts: Update the status in Google Sheets and publish the clips to social media platforms. Each step is handled by specific nodes in n8n, a no-code automation tool, making the entire process accessible even if you’re not tech-savvy. The workflow is visually represented in the provided n8n screenshot, with nodes connected to show the flow of data and actions. Step 1: Get Longform Purpose Start the automation and retrieve the long-form video data. Tips Test the node with a sample row to ensure it retrieves the correct data. Use a consistent sheet structure to avoid errors in future runs. Why It Matters This step ensures the automation starts automatically and pulls the correct video for processing, saving you from manual intervention. Step 2: Analyze Longform Purpose Use Clap to analyze the long-form video and generate short clips. Tips Pin the Get Shorts Details Node**: Right-click and pin it to retain data for testing across sessions. Test with a Sample Video**: Run the workflow with a short video to verify Clap’s output. Why It Matters Clap’s AI identifies key moments and generates clips, saving hours of manual editing. The wait and status nodes ensure the workflow progresses only when ready. Step 3: Produce Shorts Purpose Export the generated clips for publishing. Tips Preview the clips after export to ensure quality. Adjust wait times based on export duration observed during testing. Why It Matters This step finalizes the clips, making them ready for publishing, with wait nodes preventing premature progression. Step 4: Publish Shorts Purpose Update the video’s status in Google Sheets and publish the clips to social media. Tips Add an If Node**: Before updating, check if the status is already "done" to skip processed videos. Organize Clips**: Use Google Sheets columns (e.g., "TikTok," "YouTube") to assign clips to platforms. Why It Matters This step automates publishing across multiple platforms and keeps your workflow organized by updating statuses. Additional Tips for Efficiency No-Code Simplicity**: n8n’s drag-and-drop interface requires no coding—adjust nodes visually to suit your needs. Handle Processing Times**: Use wait nodes to manage delays in analysis and export steps. Monetization Ideas**: Offer this automation as a service to businesses or creators. Submit clips to platforms like "Wop" for earnings based on views. Testing**: Run the workflow with a sample video, pinning nodes to retain data for debugging. Benefits of AI Automation Time Savings**: Automate clipping and publishing, freeing you for creative tasks. Scalability**: Produce 100+ shorts from one video, boosting reach. Consistency**: Maintain a regular posting schedule effortlessly. Cost-Effective**: Reduce reliance on manual editing or expensive tools. This workflow leverages n8n and Klap to streamline short-form content creation, making it ideal for content creators looking to maximize their long-form videos. If you need further clarification or help with specific nodes, let me know!. An n8n automation workflow template by Sebastian/OptiLever.

By David Ashby

Complete MCP server exposing 2 NPR Station Finder 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 Station Finder 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 Station Finder 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://station.api.npr.org • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (2 total) 🔧 V3 (2 endpoints) • GET /v3/stations: Get Station 1 • GET /v3/stations/{stationId}: Retrieve metadata for the station with the given numeric ID 🤖 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 Station Finder 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. An n8n automation workflow template by David Ashby.

By David Ashby

Complete MCP server exposing 2 Mobility 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 Mobility 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 Mobility 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://developer.o2.cz/mobility/sandbox/api • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (2 total) 🔧 Info (1 endpoints) • GET /info: Retrieve Application Info 🔧 Transit (1 endpoints) • GET /transit/{from}/{to}: Transit between basic residential units 🤖 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 Mobility 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. An n8n automation workflow template by David Ashby.

By David Ashby

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. An n8n automation workflow template by David Ashby.

By David Ashby

Complete MCP server exposing 1 Article Search 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 Article Search 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 Article Search 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 http://api.nytimes.com/svc/search/v2 • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (1 total) 🔧 Articlesearch.Json (1 endpoints) • GET /articlesearch.json: Search Articles 🤖 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 Article Search 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. An n8n automation workflow template by David Ashby.

By David Ashby

Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator? Join the community Complete MCP server exposing 6 Swagger2OpenAPI Converter API operations to AI agents. ⚡ Quick Setup Import this workflow into your n8n instance Credentials Add Swagger2OpenAPI Converter 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 Swagger2OpenAPI Converter 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://mermade.org.uk/api/v1 • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (6 total) 🔧 Badge (1 endpoints) • GET /badge: Redirect to Badge SVG 🔧 Convert (2 endpoints) • GET /convert: Convert Swagger in Body • POST /convert: Convert a Swagger 2.0 definition passed in the body to OpenAPI 3.0.x 🔧 Status (1 endpoints) • GET /status: Check API Status 🔧 Validate (2 endpoints) • GET /validate: Validate OpenAPI in Body • POST /validate: Validate an OpenAPI 3.0.x definition supplied in the body of the request 🤖 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 Swagger2OpenAPI Converter 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. An n8n automation workflow template by David Ashby.

By David Ashby

Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator? Join the community Complete MCP server exposing 15 Pinecone API operations to AI agents. ⚡ Quick Setup Import this workflow into your n8n instance Credentials Add Pinecone 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 Pinecone 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://controller.{environment}.pinecone.io • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (15 total) 🔧 Collections (4 endpoints) • GET /collections: Describe Collection • POST /collections: Create collection • DELETE /collections/{collectionName}: Delete Collection • GET /collections/{collectionName}: Describe collection 🔧 Describe_Index_Stats (1 endpoints) • POST /describe_index_stats: Retrieve Index Stats 🔧 Indexes (5 endpoints) • GET /indexes: Configure Index • POST /indexes: Create index • DELETE /indexes/{indexName}: Delete Index • GET /indexes/{indexName}: Describe index • PATCH /indexes/{indexName}: Configure index 🔧 Query (1 endpoints) • POST /query: Execute Query 🔧 Vectors (4 endpoints) • POST /vectors/delete: Delete Vectors • POST /vectors/fetch: Fetch Vectors • POST /vectors/update: Update Vectors • POST /vectors/upsert: Upsert Vectors 🤖 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 Pinecone 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. An n8n automation workflow template by David Ashby.

By David Ashby

Complete MCP server exposing 2 topupsapi 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 topupsapi 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 topupsapi 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://polls.apiblueprint.org • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (2 total) 🔧 Questions (2 endpoints) • GET /questions: Create Question 1 • POST /questions: Create a New Question 🤖 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 topupsapi 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. An n8n automation workflow template by David Ashby.

By David Ashby

Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator? Join the community Complete MCP server exposing 6 Starwars Translations API operations to AI agents. ⚡ Quick Setup Import this workflow into your n8n instance Credentials Add Starwars Translations 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 Starwars Translations 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.funtranslations.com • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (6 total) 🔧 Translate (6 endpoints) • GET /translate/cheunh: Translate to Cheunh • GET /translate/gungan: Translate to Gungan • GET /translate/huttese: Translate to Huttese • GET /translate/mandalorian: Translate to Mandalorian • GET /translate/sith: Translate to Sith • GET /translate/yoda: Translate to Yoda 🤖 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 Starwars Translations 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. An n8n automation workflow template by David Ashby.

By David Ashby

Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator? Join the community Complete MCP server exposing 8 YouTube Reporting API operations to AI agents. ⚡ Quick Setup Import this workflow into your n8n instance Credentials Add YouTube Reporting 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 YouTube Reporting 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://youtubereporting.googleapis.com/ • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (8 total) 🔧 V1 (8 endpoints) • GET /v1/jobs: Retrieve Report Metadata • POST /v1/jobs: Creates a job and returns it. • DELETE /v1/jobs/{jobId}: Deletes a job. • GET /v1/jobs/{jobId}: Gets a job. • GET /v1/jobs/{jobId}/reports: Lists reports created by a specific job. Returns NOT_FOUND if the job does no... • GET /v1/jobs/{jobId}/reports/{reportId}: Gets the metadata of a specific report. • GET /v1/media/{resourceName}: Method for media download. Download is supported on the URI `/v1/media/{+name... • GET /v1/reportTypes: List Report Types 🤖 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 YouTube Reporting 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. An n8n automation workflow template by David Ashby.

By David Ashby

Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator? Join the community Complete MCP server exposing 16 U.S. EPA Enforcement and Compliance History Online (ECHO) - Resource Conservation and Recovery Act API operations to AI agents. ⚡ Quick Setup Import this workflow into your n8n instance Credentials Add U.S. EPA Enforcement and Compliance History Online (ECHO) - Resource Conservation and Recovery Act 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 U.S. EPA Enforcement and Compliance History Online (ECHO) - Resource Conservation and Recovery Act 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://echodata.epa.gov/echo • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (16 total) 🔧 Rcra_Rest_Services.Get_Download (2 endpoints) • GET /rcra_rest_services.get_download: Request RCRA Data Download • POST /rcra_rest_services.get_download: Resource Conservation and Recovery Act (RCRA) Download Data Service 🔧 Rcra_Rest_Services.Get_Facilities (2 endpoints) • GET /rcra_rest_services.get_facilities: Request RCRA Facility Search • POST /rcra_rest_services.get_facilities: Resource Conservation and Recovery Act (RCRA) Facility Search Service 🔧 Rcra_Rest_Services.Get_Facility_Info (2 endpoints) • GET /rcra_rest_services.get_facility_info: Request RCRA Facility Details • POST /rcra_rest_services.get_facility_info: Resource Conservation and Recovery Act (RCRA) Facility Enhanced Search Service 🔧 Rcra_Rest_Services.Get_Geojson (2 endpoints) • GET /rcra_rest_services.get_geojson: Request RCRA GeoJSON Data • POST /rcra_rest_services.get_geojson: Resource Conservation and Recovery Act (RCRA) GeoJSON Service 🔧 Rcra_Rest_Services.Get_Info_Clusters (2 endpoints) • GET /rcra_rest_services.get_info_clusters: Request RCRA Info Clusters • POST /rcra_rest_services.get_info_clusters: Resource Conservation and Recovery Act (RCRA) Info Clusters Service 🔧 Rcra_Rest_Services.Get_Map (2 endpoints) • GET /rcra_rest_services.get_map: Request RCRA Map Data • POST /rcra_rest_services.get_map: Resource Conservation and Recovery Act (RCRA) Map Service 🔧 Rcra_Rest_Services.Get_Qid (2 endpoints) • GET /rcra_rest_services.get_qid: Request RCRA Paginated Results • POST /rcra_rest_services.get_qid: Resource Conservation and Recovery Act (RCRA) Paginated Results Service 🔧 Rcra_Rest_Services.Metadata (2 endpoints) • GET /rcra_rest_services.metadata: Request RCRA Metadata • POST /rcra_rest_services.metadata: Resource Conservation and Recovery Act (RCRA) Metadata Service 🤖 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 U.S. EPA Enforcement and Compliance History Online (ECHO) - Resource Conservation and Recovery Act 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. An n8n automation workflow template by David Ashby.

By Md. Nazmul Islam Rumman

AI-Powered MCQ Quiz Generator from YouTube Videos Transform any YouTube video into an interactive MCQ quiz automatically! This workflow uses Google Gemini AI to analyze video content and generate comprehensive multiple-choice questions with automatic grading - perfect for educators, trainers, and content creators. Who is this For This workflow is perfect for: Educators** creating quizzes from educational YouTube content Corporate Trainers** developing assessments from training videos Content Creators** engaging their audience with interactive quizzes Students** testing their knowledge on video lectures Online Course Creators** building assessments from video content Features AI Video Analysis**: Google Gemini 2.5 Flash analyzes entire YouTube videos (up to 50 minutes) Dynamic Question Generation**: Creates up to 90 MCQ questions with 3 options each Automatic Form Creation**: Generates Google Forms with quiz functionality Smart Grading**: Built-in correct answer identification and scoring Error Handling**: Robust error management with user feedback How It Works User Input via n8n Web Form: Form Name (Quiz Title) Email Address YouTube Video URL Number of Questions (1-90) AI Processing Pipeline: Google Gemini analyzes the YouTube video content AI extracts key concepts and generates relevant questions Structured output parser formats questions into JSON Google Forms Integration: Automatically creates a new Google Form Adds all generated questions with multiple choice options Configures quiz settings with correct answers and scoring Completion & Access: User receives direct link to the generated quiz Form ready for immediate use or sharing Video Demo: See this youtube Video to explore "how it works". Set Up Steps Import the Workflow Create a new workflow in n8n Import the JSON file by clicking "three dots" (upper right corner) > "Import from file..." Configure Google Gemini API Get your Google AI Studio API key from Google AI Studio On “HTTP Request to Gemini” node replace the “API_KEY” from url with your API key. Create a "Google Gemini (PaLM) API" credential in n8n Add your API key to the credential Connect the credential to the "Google Gemini Chat Model" node Set Up Google Forms Integration Enable Google Forms API in Google Cloud Console Create a "Google OAuth2 API" credential in n8n Authorize the credential with Forms permissions Connect the credential to both HTTP Request nodes (“Create a Google Form” node and “Create MCQ Quizzes” node) Configure Form Trigger The workflow includes a built-in form trigger No additional setup needed - the form URL will be generated automatically Customize form fields if needed in the “Input YouTube URL" node Test the Workflow Activate the workflow Submit the form to generate a test quiz Verify the Google Form is created successfully Pre-requisites Necessary Accounts:** Google Account (for Forms API access) Google AI Studio Account (for Gemini API access) n8n Instance (cloud or self-hosted) API Access:** Google Forms API enabled Google drive API enabled Google Generative AI API access Valid API keys and OAuth credentials N8N Requirements:** n8n version 1.95.2 or higher LangChain nodes package installed Internet access for API calls Customization Guidance Question Generation Prompts: Modify the prompt in "Set Prompt and model" node for different question styles Adjust difficulty levels or focus areas Change question format (True/False, Fill-in-blanks, etc.) Form Customization: Update form title and description templates Add additional input fields (difficulty level, subject area) Customize success/error messages Advanced Features You Can Add: Email Notifications: Send quiz links via email Analytics Integration: Track quiz performance and completion rates Multi-language Support: Generate quizzes in different languages Question Bank Storage: Save generated questions to a database Batch Processing: Generate multiple quizzes from a YouTube playlist Error Handling Enhancements: Add retry logic for API failures Implement fallback question generation Create detailed error logging Technical Specifications Video Length**: Up to 50 minutes supported Question Limit**: 1-90 questions per quiz Processing Time**: 2-10 minutes depending on video length Supported Formats**: YouTube videos (public and unlisted) Output Format**: Google Forms with automatic grading Limitations & Considerations YouTube video must be publicly accessible or unlisted Processing time increases with video length and question count API rate limits may apply for high-volume usage Some complex visual content may not be fully analyzed Ready to Transform Videos into Quizzes? This workflow streamlines the entire process from video analysis to quiz deployment. Perfect for educators and trainers looking to create engaging assessments from video content quickly and efficiently. An n8n automation workflow template by Md. Nazmul Islam Rumman.

By David Ashby

Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator? Join the community Complete MCP server exposing 16 U.S. EPA Enforcement and Compliance History Online (ECHO) - Clean Air Act API operations to AI agents. ⚡ Quick Setup Import this workflow into your n8n instance Credentials Add U.S. EPA Enforcement and Compliance History Online (ECHO) - Clean Air Act 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 U.S. EPA Enforcement and Compliance History Online (ECHO) - Clean Air Act 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://echodata.epa.gov/echo • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (16 total) 🔧 Air_Rest_Services.Get_Download (2 endpoints) • GET /air_rest_services.get_download: Request Air Quality Data • POST /air_rest_services.get_download: Clean Air Act Download Data Service 🔧 Air_Rest_Services.Get_Facilities (2 endpoints) • GET /air_rest_services.get_facilities: Query Air Quality Facilities • POST /air_rest_services.get_facilities: Clean Air Act Facility Search 🔧 Air_Rest_Services.Get_Facility_Info (2 endpoints) • GET /air_rest_services.get_facility_info: Request Facility Details • POST /air_rest_services.get_facility_info: Clean Air Act Facility Enhanced Search 🔧 Air_Rest_Services.Get_Geojson (2 endpoints) • GET /air_rest_services.get_geojson: Request Air Quality GeoJSON • POST /air_rest_services.get_geojson: Clean Air Act GeoJSON Service 🔧 Air_Rest_Services.Get_Info_Clusters (2 endpoints) • GET /air_rest_services.get_info_clusters: Request Info Clusters Data • POST /air_rest_services.get_info_clusters: Clean Air Act Info Clusters Service 🔧 Air_Rest_Services.Get_Map (2 endpoints) • GET /air_rest_services.get_map: Request Air Quality Map • POST /air_rest_services.get_map: Clean Air Act Map Service 🔧 Air_Rest_Services.Get_Qid (2 endpoints) • GET /air_rest_services.get_qid: Query by Query ID • POST /air_rest_services.get_qid: Clean Air Act Search by Query ID 🔧 Air_Rest_Services.Metadata (2 endpoints) • GET /air_rest_services.metadata: Request Air Quality Metadata • POST /air_rest_services.metadata: Clean Air Act Metadata Service 🤖 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 U.S. EPA Enforcement and Compliance History Online (ECHO) - Clean Air Act 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. An n8n automation workflow template by David Ashby.

By David Ashby

⚠️ ADVANCED USE ONLY - U.S. EPA Enforcement and Compliance History Online (ECHO) - Clean Water Act (CWA) Rest Services MCP Server (36 operations) 🚨 This workflow is for advanced users only! Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator? Join the community This MCP server contains 36 operations which is significantly more than the recommended maximum of tools for most AI clients. 🔍 Recommended Alternative for basic use cases Seek a simplified MCP server that utilizes the official n8n tool implementation for U.S. EPA Enforcement and Compliance History Online (ECHO) - Clean Water Act (CWA) Rest Services if available, or an MCP server with only common operations as it will be more efficient and easier to manage. 🛠️ Advanced Usage Requirements BEFORE adding this MCP server to your client: Disable or delete unused nodes - Review sections and disable/delete those you don't need AFTER adding the MCP server to your client: 1.Selective tool enabling - Instead of enabling all tools (default), manually select only the specific tools you need for that Workflow's MCP client. Monitor performance - Too many tools can slow down AI responses 💡 Pro Tips Keep maximum 40 enabled tools - Most AI clients perform better with fewer tools Group related operations and only enable one group at a time Use the overview note to understand what each operation group does Ping me on discord if your business needs this implemented professionally ⚡ Quick Setup Import this workflow into your n8n instance Credentials Add U.S. EPA Enforcement and Compliance History Online (ECHO) - Clean Water Act (CWA) Rest Services 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 U.S. EPA Enforcement and Compliance History Online (ECHO) - Clean Water Act (CWA) Rest Services 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://echodata.epa.gov/echo • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (36 total) 🔧 Cwa_Rest_Services.Get_Download (2 endpoints) • GET /cwa_rest_services.get_download: Submit CWA Download Data • POST /cwa_rest_services.get_download: Clean Water Act (CWA) Download Data Service 🔧 Cwa_Rest_Services.Get_Facilities (2 endpoints) • GET /cwa_rest_services.get_facilities: Submit CWA Facility Search • POST /cwa_rest_services.get_facilities: Clean Water Act (CWA) Facility Search Service 🔧 Cwa_Rest_Services.Get_Facility_Info (2 endpoints) • GET /cwa_rest_services.get_facility_info: Submit CWA Facility Details • POST /cwa_rest_services.get_facility_info: Clean Water Act (CWA) Facility Enhanced Search Service 🔧 Cwa_Rest_Services.Get_Geojson (2 endpoints) • GET /cwa_rest_services.get_geojson: Submit CWA GeoJSON Data • POST /cwa_rest_services.get_geojson: Clean Water Act (CWA) GeoJSON Service 🔧 Cwa_Rest_Services.Get_Info_Clusters (2 endpoints) • GET /cwa_rest_services.get_info_clusters: Submit CWA Info Clusters • POST /cwa_rest_services.get_info_clusters: Clean Water Act (CWA) Info Clusters Service 🔧 Cwa_Rest_Services.Get_Map (2 endpoints) • GET /cwa_rest_services.get_map: Submit CWA Map Data • POST /cwa_rest_services.get_map: Clean Water Act (CWA) Map Service 🔧 Cwa_Rest_Services.Get_Qid (2 endpoints) • GET /cwa_rest_services.get_qid: Submit CWA Paginated Results • POST /cwa_rest_services.get_qid: Clean Water Act (CWA) Paginated Results Service 🔧 Cwa_Rest_Services.Metadata (2 endpoints) • GET /cwa_rest_services.metadata: Submit CWA Metadata • POST /cwa_rest_services.metadata: Clean Water Act (CWA) Metadata Service 🔧 Rest_Lookups.Bp_Tribes (2 endpoints) • GET /rest_lookups.bp_tribes: Submit BP Tribes Data • POST /rest_lookups.bp_tribes: ECHO BP Tribes Lookup Service 🔧 Rest_Lookups.Cwa_Parameters (2 endpoints) • GET /rest_lookups.cwa_parameters: Submit CWA Parameters • POST /rest_lookups.cwa_parameters: ECHO CWA Parameter Lookup Service 🔧 Rest_Lookups.Cwa_Pollutants (2 endpoints) • GET /rest_lookups.cwa_pollutants: Submit CWA Pollutants • POST /rest_lookups.cwa_pollutants: ECHO CWA Pollutants Lookup Service 🔧 Rest_Lookups.Federal_Agencies (2 endpoints) • GET /rest_lookups.federal_agencies: Submit Federal Agencies • POST /rest_lookups.federal_agencies: ECHO Federal Agency Lookup Service 🔧 Rest_Lookups.Icis_Inspection_Types (2 endpoints) • GET /rest_lookups.icis_inspection_types: Submit ICIS Inspection Types • POST /rest_lookups.icis_inspection_types: ECHO ICIS NPDES Inspection Types Lookup Service 🔧 Rest_Lookups.Icis_Law_Sections (2 endpoints) • GET /rest_lookups.icis_law_sections: Submit ICIS Law Sections • POST /rest_lookups.icis_law_sections: ECHO ICIS NPDES Law Sections Lookup Service 🔧 Rest_Lookups.Naics_Codes (2 endpoints) • GET /rest_lookups.naics_codes: Submit NAICS Codes • POST /rest_lookups.naics_codes: ECHO NAICS Codes Lookup Service 🔧 Rest_Lookups.Npdes_Parameters (2 endpoints) • GET /rest_lookups.npdes_parameters: Submit NPDES Parameters • POST /rest_lookups.npdes_parameters: ECHO NPDES Parameters Lookup Service 🔧 Rest_Lookups.Wbd_Code_Lu (2 endpoints) • GET /rest_lookups.wbd_code_lu: Submit WBD Codes • POST /rest_lookups.wbd_code_lu: ECHO WBD Code Lookup Service 🔧 Rest_Lookups.Wbd_Name_Lu (2 endpoints) • GET /rest_lookups.wbd_name_lu: Submit WBD Names • POST /rest_lookups.wbd_name_lu: ECHO WBD Name Lookup Service 🤖 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 U.S. EPA Enforcement and Compliance History Online (ECHO) - Clean Water Act (CWA) Rest Services 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. An n8n automation workflow template by David Ashby.

By Oneclick AI Squad

Overview This solution ensures the secure backup and version control of your self-hosted n8n workflows by storing them in a GitLab repository. It compares current workflows with their GitLab counterparts, updates files when differences are detected, and organizes them in user-specific folders (e.g., repo -> username -> workflow.json). Backups are triggered manually or weekly, with a success notification sent via email. Operational Process Manual Backup Trigger**: Initiates the backup process on demand. Scheduled Weekly Backup**: Automatically triggers the backup every week. Fetch N8N Workflows**: Retrieves all workflows from n8n using the API (getAll:workflow). Prepare Backup Metadata**: Generates metadata, including user details for folder organization. Process Each Workflow**: Handles each workflow individually for processing. Format Workflow for GitLab**: Structures workflows with proper versioning for GitLab compatibility. Rate Limit Control**: Manages API rate limits to ensure smooth operation. Create to GitLab Repository**: Saves workflows to GitLab; creates a new file if it doesn’t exist. Check Backup Status**: Verifies if the file exists; if true, proceeds to update; if false, loops back. Update Backup Summary**: Updates the existing file in GitLab with the latest version. Log Backup Results**: Records the outcome of the backup process. Send Email**: Sends a confirmation email: "Hello, The scheduled backup of all n8n workflows has been completed successfully. All workflows have been committed to the GitLab repository without any errors. Regards, n8n Automation Bot" Implementation Guide Import this solution into your n8n instance. Configure GitLab API credentials and specify the target repository. Set up n8n API access to enable workflow retrieval. Customize the Prepare Backup Metadata node to map users to folders as needed. Test the process using the Manual Backup Trigger to confirm GitLab integration. Schedule weekly backups via the Scheduled Weekly Backup node (recommended for Fridays). Requirements GitLab API credentials with write access n8n API access for workflow retrieval A configured GitLab repository Customization Options Adjust the Prepare Backup Metadata node to include additional user fields. Modify the Rate Limit Control node to accommodate varying API limits. Tailor the Send Email node to include custom notification details. An n8n automation workflow template by Oneclick AI Squad.

By David Ashby

Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator? Join the community Complete MCP server exposing 16 Lyft API operations to AI agents. ⚡ Quick Setup Import this workflow into your n8n instance Credentials Add Lyft 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 Lyft 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.lyft.com/v1 • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (16 total) 🔧 Cost (1 endpoints) • GET /cost: Retrieve Cost Estimate 🔧 Drivers (1 endpoints) • GET /drivers: List Nearby Drivers 🔧 Eta (1 endpoints) • GET /eta: Retrieve Pickup ETA 🔧 Profile (1 endpoints) • GET /profile: Retrieve User Profile 🔧 Rides (7 endpoints) • GET /rides: Update Sandbox Ride Status • POST /rides: Request a Lyft • GET /rides/{id}: Get the ride detail of a given ride ID • POST /rides/{id}/cancel: Cancel a ongoing requested ride • PUT /rides/{id}/destination: Update the destination of the ride • PUT /rides/{id}/rating: Add the passenger's rating, feedback, and tip • GET /rides/{id}/receipt: Get the receipt of the rides. 🔧 Ridetypes (1 endpoints) • GET /ridetypes: Update Driver Availability 🔧 Sandbox (4 endpoints) • PUT /sandbox/primetime: Set Prime Time Percentage • PUT /sandbox/rides/{id}: Propagate ride through ride status • PUT /sandbox/ridetypes: Preset types of rides for sandbox • PUT /sandbox/ridetypes/{ride_type}: Driver availability for processing ride request 🤖 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 Lyft 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. An n8n automation workflow template by David Ashby.

By David Ashby

Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator? Join the community Complete MCP server exposing 23 Listen API: Podcast Search, Directory, and Insights API operations to AI agents. ⚡ Quick Setup Import this workflow into your n8n instance Credentials Add Listen API: Podcast Search, Directory, and Insights 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 Listen API: Podcast Search, Directory, and Insights 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://listen-api.listennotes.com/api/v2 • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (23 total) 🔧 Best_Podcasts (1 endpoints) • GET /best_podcasts: Delete Podcast by ID 🔧 Curated_Podcasts (2 endpoints) • GET /curated_podcasts: Fetch Curated Podcast List by ID • GET /curated_podcasts/{id}: Fetch a curated list of podcasts by id 🔧 Episodes (3 endpoints) • POST /episodes: Fetch Episode Recommendations • GET /episodes/{id}: Fetch detailed meta data for an episode by id • GET /episodes/{id}/recommendations: Fetch recommendations for an episode 🔧 Genres (1 endpoints) • GET /genres: Fetch Podcast Genres 🔧 Just_Listen (1 endpoints) • GET /just_listen: Fetch Random Podcast Episode 🔧 Languages (1 endpoints) • GET /languages: Fetch Supported Languages 🔧 Playlists (2 endpoints) • GET /playlists: Fetch Playlist Details by ID • GET /playlists/{id}: Fetch a playlist's info and items (i.e., episodes or podcasts). 🔧 Podcasts (6 endpoints) • POST /podcasts: Fetch Podcast Audience Data • POST /podcasts/submit: Submit a podcast to Listen Notes database • DELETE /podcasts/{id}: Request to delete a podcast • GET /podcasts/{id}: Fetch detailed meta data and episodes for a podcast by id • GET /podcasts/{id}/audience: Fetch audience demographics for a podcast • GET /podcasts/{id}/recommendations: Fetch recommendations for a podcast 🔧 Regions (1 endpoints) • GET /regions: Fetch Supported Regions 🔧 Related_Searches (1 endpoints) • GET /related_searches: Fetch Related Search Terms 🔧 Search (1 endpoints) • GET /search: Full-Text Search 🔧 Spellcheck (1 endpoints) • GET /spellcheck: Spell Check Search Term 🔧 Trending_Searches (1 endpoints) • GET /trending_searches: Fetch Trending Search Terms 🔧 Typeahead (1 endpoints) • GET /typeahead: Typeahead Search 🤖 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 Listen API: Podcast Search, Directory, and Insights 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. An n8n automation workflow template by David Ashby.

By David Ashby

Complete MCP server exposing 3 IPQualityScore 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 IPQualityScore 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 IPQualityScore 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://ipqualityscore.com/api • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (3 total) 🔧 Json (3 endpoints) • GET /json/email/{YOUR_API_KEY_HERE}/{USER_EMAIL_HERE}: Email Validation • GET /json/phone/{YOUR_API_KEY_HERE}/{USER_PHONE_HERE}: Phone Validation • GET /json/url/{YOUR_API_KEY_HERE}/{URL_HERE}: Malicious URL Scanner 🤖 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 IPQualityScore 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. An n8n automation workflow template by David Ashby.

By David Ashby

Complete MCP server exposing 1 IP2Proxy Proxy Detection 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 IP2Proxy Proxy Detection 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 IP2Proxy Proxy Detection 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.ip2proxy.com • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (1 total) 🔧 General (1 endpoints) • GET /: Check Proxy IP 🤖 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 IP2Proxy Proxy Detection 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. An n8n automation workflow template by David Ashby.

By David Ashby

Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator? Join the community Complete MCP server exposing 8 Metadata API operations to AI agents. ⚡ Quick Setup Import this workflow into your n8n instance Credentials Add Metadata 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 Metadata 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 (8 total) 🔧 Country (1 endpoints) • GET /country/{countryCode}/sales_tax_jurisdiction: Get Sales Tax Jurisdiction 🔧 Marketplace (7 endpoints) • GET /marketplace/{marketplace_id}/get_automotive_parts_compatibility_policies: Get Get Automotive Parts Compatibility Policies • GET /marketplace/{marketplace_id}/get_extended_producer_responsibility_policies: Get Get Extended Producer Responsibility Policies • GET /marketplace/{marketplace_id}/get_hazardous_materials_labels: Get Get Hazardous Materials Labels • GET /marketplace/{marketplace_id}/get_item_condition_policies: Get Get Item Condition Policies • GET /marketplace/{marketplace_id}/get_listing_structure_policies: Get Get Listing Structure Policies • GET /marketplace/{marketplace_id}/get_negotiated_price_policies: Get Get Negotiated Price Policies • GET /marketplace/{marketplace_id}/get_return_policies: Get Get Return Policies 🤖 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 Metadata 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. An n8n automation workflow template by David Ashby.

By David Ashby

Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator? Join the community Complete MCP server exposing 23 Feed API operations to AI agents. ⚡ Quick Setup Import this workflow into your n8n instance Credentials Add Feed 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 Feed 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 (23 total) 🔧 Customer_Service_Metric_Task (3 endpoints) • GET /customer_service_metric_task: Get Customer Service Metric Task • POST /customer_service_metric_task: Create/Search Customer Service Metric Task • GET /customer_service_metric_task/{task_id}: Get {Task Id} 🔧 Inventory_Task (3 endpoints) • GET /inventory_task: Get Inventory Task • POST /inventory_task: Create/Search Inventory Task • GET /inventory_task/{task_id}: Get {Task Id} 🔧 Order_Task (3 endpoints) • GET /order_task: Get Order Task • POST /order_task: Create/Search Order Task • GET /order_task/{task_id}: This method retrieves the task details and status of the specified task 🔧 Schedule (6 endpoints) • GET /schedule: Get Schedule Template • POST /schedule: Create/Search Schedule • DELETE /schedule/{schedule_id}: This method deletes an existing schedule • GET /schedule/{schedule_id}: This method retrieves schedule details and status of the specified schedule • PUT /schedule/{schedule_id}: This method updates an existing schedule • GET /schedule/{schedule_id}/download_result_file: This method downloads the latest result file generated by the schedule 🔧 Schedule_Template (2 endpoints) • GET /schedule_template: Get Schedule Template • GET /schedule_template/{schedule_template_id}: This method retrieves the details of the specified template 🔧 Task (6 endpoints) • GET /task: Upload Task File • POST /task: This method creates an upload task or a download task without filter criteria • GET /task/{task_id}: This method retrieves the details and status of the specified task • GET /task/{task_id}/download_input_file: Get Download Input File • GET /task/{task_id}/download_result_file: Get Download Result File • POST /task/{task_id}/upload_file: Create/Search Upload File 🤖 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 Feed 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. An n8n automation workflow template by David Ashby.

By David Ashby

Complete MCP server exposing 1 Recommendation 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 Recommendation 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 Recommendation 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 (1 total) 🔧 Find (1 endpoints) • POST /find: Get Promoted Listings 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 Recommendation 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. An n8n automation workflow template by David Ashby.

By David Ashby

Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator? Join the community Complete MCP server exposing 15 Fulfillment API operations to AI agents. ⚡ Quick Setup Import this workflow into your n8n instance Credentials Add Fulfillment 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 Fulfillment 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 (15 total) 🔧 Order (6 endpoints) • GET /order: Retrieve Fulfillment Details • GET /order/{orderId}: Get {Orderid} • GET /order/{orderId}/shipping_fulfillment: Get Shipping Fulfillment • POST /order/{orderId}/shipping_fulfillment: Create/Search Shipping Fulfillment • GET /order/{orderId}/shipping_fulfillment/{fulfillmentId}: Get {Fulfillmentid} • POST /order/{order_id}/issue_refund: Issue Refund 🔧 Payment_Dispute (8 endpoints) • GET /payment_dispute/{payment_dispute_id}: Get Payment Dispute Details • POST /payment_dispute/{payment_dispute_id}/accept: Accept Payment Dispute • GET /payment_dispute/{payment_dispute_id}/activity: Get Payment Dispute Activity • POST /payment_dispute/{payment_dispute_id}/add_evidence: Add an Evidence File • POST /payment_dispute/{payment_dispute_id}/contest: Contest Payment Dispute • GET /payment_dispute/{payment_dispute_id}/fetch_evidence_content: Get Payment Dispute Evidence File • POST /payment_dispute/{payment_dispute_id}/update_evidence: Update evidence • POST /payment_dispute/{payment_dispute_id}/upload_evidence_file: Upload an Evidence File 🔧 Payment_Dispute_Summary (1 endpoints) • GET /payment_dispute_summary: Search Payment Disputes 🤖 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 Fulfillment 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. An n8n automation workflow template by David Ashby.

By David Ashby

Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator? Join the community Complete MCP server exposing 6 Logistics API operations to AI agents. ⚡ Quick Setup Import this workflow into your n8n instance Credentials Add Logistics 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 Logistics 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 (6 total) 🔧 Shipment (4 endpoints) • POST /shipment/create_from_shipping_quote: Create Shipment from Quote • GET /shipment/{shipmentId}: This method retrieves the shipment details for the specified shipment ID • POST /shipment/{shipmentId}/cancel: Create/Search Cancel • GET /shipment/{shipmentId}/download_label_file: Get Download Label File 🔧 Shipping_Quote (2 endpoints) • POST /shipping_quote: Retrieve Shipping Quote • GET /shipping_quote/{shippingQuoteId}: Get {Shippingquoteid} 🤖 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 Logistics 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. An n8n automation workflow template by David Ashby.

By David Ashby

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. An n8n automation workflow template by David Ashby.

By David Ashby

Complete MCP server exposing 2 Analytics 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 Analytics 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 Analytics 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 (2 total) 🔧 Rate_Limit (1 endpoints) • GET /rate_limit/: Retrieve Application Rate Limits 🔧 User_Rate_Limit (1 endpoints) • GET /user_rate_limit/: Retrieve User Rate Limits 🤖 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 Analytics 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. An n8n automation workflow template by David Ashby.

By David Ashby

Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator? Join the community Complete MCP server exposing 4 Item Feed Service API operations to AI agents. ⚡ Quick Setup Import this workflow into your n8n instance Credentials Add Item Feed 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 Item Feed 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://api.ebay.com/buy/feed/v1_beta • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (4 total) 🔧 Item (1 endpoints) • GET /item: Download Hourly Snapshot Feed 🔧 Item_Group (1 endpoints) • GET /item_group: Get Item Group 🔧 Item_Priority (1 endpoints) • GET /item_priority: Get Item Priority 🔧 Item_Snapshot (1 endpoints) • GET /item_snapshot: Get Item Snapshot 🤖 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 Item Feed 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. An n8n automation workflow template by David Ashby.

By David Ashby

Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator? Join the community Complete MCP server exposing 8 Taxonomy API operations to AI agents. ⚡ Quick Setup Import this workflow into your n8n instance Credentials Add Taxonomy 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 Taxonomy 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/commerce/taxonomy/v1 • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (8 total) 🔧 Category_Tree (7 endpoints) • GET /category_tree/{category_tree_id}: Get a Category Tree • GET /category_tree/{category_tree_id}/fetch_item_aspects: Get Aspects for All Leaf Categories in a Marketplace • GET /category_tree/{category_tree_id}/get_category_subtree: Get a Category Subtree • GET /category_tree/{category_tree_id}/get_category_suggestions: Get Suggested Categories • GET /category_tree/{category_tree_id}/get_compatibility_properties: Get Compatibility Properties • GET /category_tree/{category_tree_id}/get_compatibility_property_values: Get Compatibility Property Values • GET /category_tree/{category_tree_id}/get_item_aspects_for_category: Get Get Item Aspects For Category 🔧 Get_Default_Category_Tree_Id (1 endpoints) • GET /get_default_category_tree_id: Fetch Default Category Tree ID 🤖 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 Taxonomy 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. An n8n automation workflow template by David Ashby.

By David Ashby

Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator? Join the community Complete MCP server exposing 4 Deal API operations to AI agents. ⚡ Quick Setup Import this workflow into your n8n instance Credentials Add Deal 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 Deal 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) 🔧 Deal_Item (1 endpoints) • GET /deal_item: List Deal Items 🔧 Event (2 endpoints) • GET /event: List Event Items • GET /event/{event_id}: This method retrieves the details for an eBay event 🔧 Event_Item (1 endpoints) • GET /event_item: This method returns a paginated set of event items 🤖 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 Deal 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. An n8n automation workflow template by David Ashby.

By David Ashby

Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator? Join the community Complete MCP server exposing 11 Browse API operations to AI agents. ⚡ Quick Setup Import this workflow into your n8n instance Credentials Add Browse 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 Browse 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/buy/browse/v1 • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (11 total) 🔧 Item (5 endpoints) • GET /item/: Check Item Compatibility • GET /item/get_item_by_legacy_id: Get Get Item By Legacy Id • GET /item/get_items_by_item_group: This method retrieves the details of the individual items in an item group • GET /item/{item_id}: Get {Item Id} • POST /item/{item_id}/check_compatibility: This method checks if a product is compatible with the specified item 🔧 Item_Summary (2 endpoints) • GET /item_summary/search: Search Items by Image • POST /item_summary/search_by_image: This is an Experimental method 🔧 Shopping_Cart (4 endpoints) • GET /shopping_cart/: Update Cart Item Quantity • POST /shopping_cart/add_item: This is an Experimental method • POST /shopping_cart/remove_item: This is an experimental method • POST /shopping_cart/update_quantity: This is an experimental method 🤖 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 Browse 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. An n8n automation workflow template by David Ashby.

By David Ashby

Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator? Join the community Let’s activate your org’s memory: 📝 Set Storage Open "Edit Fields" → Replace sheet_id with your Google Doc URL. 🤖 Connect Discord Update discord_server_id, channel_id, and user_id (for DMs). 🧠 Route Commands Call these from other workflows: save_memory: Log new data. retrieve_memory: Fetch history. send_memories_to_gmail: Discord summaries. 🤖 Custom Formatting Edit the AI Agent prompt to style Discord messages. 💡 Pro Tip: Pair this with an MCP Server for full issue→memory→action automation! 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. An n8n automation workflow template by David Ashby.

By David Ashby

Complete MCP server exposing 11 hashlookup CIRCL 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 hashlookup CIRCL 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 hashlookup CIRCL 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://hashlookup.circl.lu • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (11 total) 🔧 Bulk (2 endpoints) • POST /bulk/md5: Bulk Search MD5 Hashes • POST /bulk/sha1: Bulk Search SHA1 Hashes 🔧 Children (1 endpoints) • GET /children/{sha1}/{count}/{cursor}: Return children from a given SHA1. A number of element to return and an offset must be given. If not set it will be the 100 first elements. A cursor must be given to paginate over. The starting cursor is 0. 🔧 Info (1 endpoints) • GET /info: Get Database Info 🔧 Lookup (3 endpoints) • GET /lookup/md5/{md5}: Lookup MD5. • GET /lookup/sha1/{sha1}: Lookup SHA-1. • GET /lookup/sha256/{sha256}: Lookup SHA-256. 🔧 Parents (1 endpoints) • GET /parents/{sha1}/{count}/{cursor}: Return parents from a given SHA1. A number of element to return and an offset must be given. If not set it will be the 100 first elements. A cursor must be given to paginate over. The starting cursor is 0. 🔧 Session (2 endpoints) • GET /session/create/{name}: Create a session key to keep search context. The session is attached to a name. After the session is created, the header hashlookup_session can be set to the session name. • GET /session/get/{name}: Return set of matching and non-matching hashes from a session. 🔧 Stats (1 endpoints) • GET /stats/top: Get Top Queries 🤖 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 hashlookup CIRCL 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. An n8n automation workflow template by David Ashby.

By David Ashby

Complete MCP server exposing 2 CarbonDoomsDay 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 CarbonDoomsDay 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 CarbonDoomsDay 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.carbondoomsday.com/api • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (2 total) 🔧 Co2 (2 endpoints) • GET /co2/: Get CO2 Measurement by Date • GET /co2/{date}/: CO2 measurements from the Mauna Loa observatory. This data is made available through the good work of the people at the Mauna Loa observatory. Their release notes say: These data are made freely available to the public and the scientific community in the belief that their wide dissemination will lead to greater understanding and new scientific insights. We currently scrape the following sources: [co2_mlo_weekly.csv] [co2_mlo_surface-insitu_1_ccgg_DailyData.txt] [weekly_mlo.csv] We have daily CO2 measurements as far back as 1958. Learn about using pagination via [the 3rd party documentation]. [co2_mlo_weekly.csv]: https://www.esrl.noaa.gov/gmd/webdata/ccgg/trends/co2_mlo_weekly.csv [co2_mlo_surface-insitu_1_ccgg_DailyData.txt]: ftp://aftp.cmdl.noaa.gov/data/trace_gases/co2/in-situ/surface/mlo/co2_mlo_surface-insitu_1_ccgg_DailyData.txt [weekly_mlo.csv]: http://scrippsco2.ucsd.edu/sites/default/files/data/in_situ_co2/weekly_mlo.csv [the 3rd party documentation]: http://www.django-rest-framework.org/api-guide/pagination/#pagenumberpagination 🤖 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 CarbonDoomsDay 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. An n8n automation workflow template by David Ashby.

By David Ashby

Complete MCP server exposing 2 BIN Lookup 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 BIN Lookup 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 BIN Lookup 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.bintable.com/v1 • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (2 total) 🔧 Balance (1 endpoints) • GET /balance: Check Balance 🔧 {Bin} (1 endpoints) • GET /{bin}: Lookup for bin 🤖 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 BIN Lookup 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. An n8n automation workflow template by David Ashby.

By David Ashby

Complete MCP server exposing 4 BikeWise API v2 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 BikeWise API v2 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 BikeWise API v2 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://bikewise.org/api • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (4 total) 🔧 V2 (4 endpoints) • GET /v2/incidents: Paginated incidents matching parameters • GET /v2/incidents/{id}: GET /v2/incidents/{id} • GET /v2/locations: Unpaginated geojson response • GET /v2/locations/markers: Unpaginated geojson response with simplestyled markers 🤖 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 BikeWise API v2 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. An n8n automation workflow template by David Ashby.