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Automate Twitter content with trend analysis using OpenAI GPT & MCP

Dayong HuangDayong Huang
610 views
2/3/2026
BackupTransformationData ExtractionAPICRM
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How it works

This template creates a fully automated Twitter content system that discovers trending topics, analyzes why they're trending using AI, and posts intelligent commentary about them.

The workflow uses MCP (Model Context Protocol) with the twitter154 MCP server from MCPHub to connect with Twitter APIs and leverages OpenAI GPT models to generate brand-safe, engaging content about current trends.

Key Features:

  • 🔍 Smart Trend Discovery: Automatically finds US trending topics with engagement scoring
  • 🤖 AI-Powered Analysis: Uses GPT to explain "why it's trending" in 30-60 words
  • 📊 Duplicate Prevention: MySQL database tracks posted trends with 3-day cooldowns
  • 🛡️ Brand Safety: Filters out NSFW content and low-quality hashtags
  • ⚡ Rate Limiting: Built-in delays to respect API limits
  • 🐦 Powered by twitter154: Uses the robust "Old Bird" MCP server for comprehensive Twitter data access

Set up steps

Setup time: ~10 minutes

Prerequisites:

  • OpenAI API key for GPT models
  • Twitter API access for posting
  • MySQL database for trend tracking
  • MCP server access: twitter154 from aigeon-ai via MCPHub

Configuration:

  1. Set up MCP integration with twitter154 server endpoint: https://api.mcphub.com/mcp/aigeon-ai-twitter154
  2. Configure credentials for OpenAI, Twitter, and MySQL connections
  3. Set up authentication for the twitter154 MCP server (Header Auth required)
  4. Create MySQL table for keyword registry (schema provided in workflow)
  5. Test the workflow with manual execution before enabling automation
  6. Set schedule for automatic trend discovery (recommended: every 2-4 hours)

MCP Server Features Used:

  • Search Tweets: Core functionality for trend analysis
  • Get Trends Near Location: Discovers trending topics by geographic region
  • AI Tools: Leverages sentiment analysis and topic classification capabilities

Customization Options:

  • Modify trend scoring criteria in the AI agent prompts
  • Adjust cooldown periods in database queries
  • Change target locale from US to other regions (WOEID configuration)
  • Customize tweet formatting and content style
  • Configure different MCP server endpoints if needed

Perfect for: Social media managers, content creators, and businesses wanting to stay current with trending topics while maintaining consistent, intelligent posting schedules.

Powered by: The twitter154 MCP server ("The Old Bird") provides robust access to Twitter data including tweets, user information, trends, and AI-powered text analysis tools.

n8n Workflow: Automate Twitter Content with Trend Analysis using OpenAI GPT & MCP

This n8n workflow automates the process of generating Twitter content based on trend analysis, leveraging OpenAI's GPT models and the Model Context Protocol (MCP) for dynamic content creation. It's designed to help users maintain an active and relevant presence on Twitter by automatically crafting tweets that resonate with current trends.

What it does

This workflow performs the following key steps:

  1. Schedules Execution: The workflow is triggered on a predefined schedule, ensuring regular content generation.
  2. Fetches Trend Data (Implied): Although not explicitly shown in the provided JSON, the "Function" node likely prepares or processes data, potentially trend information, for the AI agent.
  3. Generates Content with AI Agent: An "AI Agent" (powered by LangChain and OpenAI Chat Model) analyzes the input data (trends) and generates relevant and engaging Twitter content.
  4. Utilizes MCP Client Tool: The "MCP Client Tool" is integrated, suggesting interaction with a Model Context Protocol for enhanced context or dynamic model interaction during content generation.
  5. Posts to X (Twitter): The generated content is then automatically posted as a tweet to the configured X (Twitter) account.
  6. Introduces Delay: A "Wait" node introduces a pause, potentially to space out tweets or adhere to API rate limits.
  7. Logs to MySQL (Implied): A "MySQL" node is present, suggesting that the workflow logs generated content, posting status, or other relevant data to a MySQL database for record-keeping or further analysis.
  8. Notifies via Slack: In case of issues or for successful operation notifications, a "Slack" node is used to send messages to a designated Slack channel.
  9. Loops for Multiple Items: The "Loop Over Items (Split in Batches)" node indicates that the workflow can process multiple trend items or content pieces in batches, allowing for scalable content generation.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance.
  • OpenAI API Key: For the "OpenAI Chat Model" to generate content.
  • X (Twitter) Account: With API access configured for posting tweets.
  • MySQL Database: Access credentials for a MySQL database to store logs or data.
  • Slack Account: To receive notifications.
  • Model Context Protocol (MCP) Client: Configuration for the MCP Client Tool, if it interacts with an external MCP service.

Setup/Usage

  1. Import the workflow: Download the provided JSON and import it into your n8n instance.
  2. Configure Credentials:
    • Set up your OpenAI credentials.
    • Configure your X (Twitter) credentials.
    • Provide your MySQL database connection details.
    • Set up your Slack API token and channel.
    • Configure any necessary credentials for the MCP Client Tool.
  3. Customize Nodes:
    • Schedule Trigger: Adjust the schedule to your desired frequency for content generation.
    • Function / Code Nodes: Modify the JavaScript code within the "Function" and "Code" nodes to fetch specific trend data or preprocess information as needed for your use case.
    • AI Agent / OpenAI Chat Model: Fine-tune the prompts and parameters within these nodes to guide the AI in generating the desired type of Twitter content.
    • X (Twitter): Customize the tweet structure and content using expressions from previous nodes.
    • MySQL: Adjust the SQL queries to match your database schema for logging.
    • Slack: Customize the notification messages.
  4. Activate the workflow: Once configured, activate the workflow to start automating your Twitter content.

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