TrendRadar: Install and Use This Cross-Platform AI Trend Aggregator for Smarter Insights

In an era where a single viral tweet can crash a stock or launch a global movement, “FOMO” (Fear Of Missing Out) isn’t just a social anxiety—it’s a business risk. For developers, marketers, and analysts, the challenge isn’t finding information; it’s filtering the signal from the noise across dozens of fragmented platforms.

Enter TrendRadar, an open-source, cross-platform trend aggregator that is rapidly gaining traction in the developer community. Unlike standard RSS feeds or expensive enterprise monitoring suites, TrendRadar combines the flexibility of open-source code with the power of MCP (Model Context Protocol)—a cutting-edge standard that allows AI models to interface directly with external data contexts.

In this guide, we’ll walk you through exactly how to install TrendRadar, configure it to monitor over 35 platforms (including Twitter/X, Reddit, and specialized news sites), and leverage its AI capabilities to turn raw data into actionable insights.

What is TrendRadar?

TrendRadar is a Python-based, open-source tool designed to automate the collection and analysis of trending topics. While it started as a simple scraper, it has evolved into a sophisticated intelligence engine capable of monitoring 35+ different sources, ranging from social media giants like Twitter and Reddit to regional heavyweights like Douyin, Weibo, and specialized financial news outlets.

Key Features at a Glance:

• Cross-Platform Aggregation: Monitors dozens of sites simultaneously, normalizing data into a single, readable format.
• MCP-Based AI Analysis: Leverages the Model Context Protocol to act as a server for AI clients (like Claude Desktop or Cline). This allows you to “chat” with your trend data using natural language.
• Real-Time Push Notifications: Sends alerts via Telegram, Slack, Discord, Email, Enterprise WeChat, Feishu, or simple webhooks.
• Docker & Serverless Ready: Runs on your local machine, a cheap VPS, or even for free using GitHub Actions.
• Smart Filtering: Uses keyword weights and “stop words” to ignore noise and focus on your specific niche (e.g., “Crypto,” “AI,” or “SaaS”).

Why “MCP” Matters:

The integration of MCP (Model Context Protocol) is the game-changer here. Instead of just giving you a static list of links, TrendRadar allows LLMs (Large Language Models) to “read” the live trend data as context. You can ask, “What is the sentiment regarding Bitcoin across Reddit and Twitter right now?” and the AI processes the aggregated data to give you a synthesized answer.

Step-by-Step Installation Guide

TrendRadar is flexible. You can run it locally via Python, containerize it with Docker, or deploy it to the cloud. Below, we cover the two most common methods: Docker (easiest for beginners) and Local Python (best for customization).

System Requirements

• OS: Windows, macOS, or Linux (Ubuntu/Debian recommended for servers).
• Runtime: Python 3.8+ or Docker Engine.
• Hardware: Minimal—runs comfortably on 512MB RAM.

Method 1: The “Quick Start” with Docker (Recommended)

If you have Docker installed, you can get TrendRadar up and running in under 2 minutes without worrying about Python dependencies.

1. Create a configuration directory:
Create a folder on your computer (e.g., trendradar_data) to store your config files so they persist after restarts.

2. Create the docker-compose.yml file:
Inside that folder, create a file named docker-compose.yml and paste the following:

version: '3'
services:
  trendradar:
    image: sansan0/trendradar:latest
    container_name: trend-radar
    volumes:
      - ./config:/app/config   # Maps local config to container
      - ./output:/app/output   # Maps output files (HTML reports)
    environment:
      - TELEGRAM_BOT_TOKEN=your_token_here
      - TELEGRAM_CHAT_ID=your_chat_id_here
      - RUN_MODE=cron          # Runs periodically
      - CRON_SCHEDULE="*/60 * * * *" # Runs every hour
    restart: always

3. Run the container:
Open your terminal in that folder and run:

docker-compose up -d

Method 2: Local Python Installation (For Developers)

Use this method if you want to modify the code or contribute to the project.

1. Clone the Repository:

git clone https://github.com/sansan0/TrendRadar.git
cd TrendRadar

2. Install Dependencies:
Ensure you have Python 3.8+ installed.

pip install -r requirements.txt

(Note: If you encounter permission errors, try adding --user to the command or use a virtual environment: python -m venv venv && source venv/bin/activate).

3. Configure Your Settings:
Duplicate the example config file:

cp config/config_example.yaml config/config.yaml

Open config.yaml in your text editor (VS Code, Notepad++). This is where you will enable/disable specific platforms and add your API keys.

4. Run the Application:
To perform a one-time check:

python main.py

Getting Started with Usage

Once installed, TrendRadar operates based on the “Mode” you select. Let’s look at how to use it effectively.

1. Basic Usage: Monitoring a Keyword

Let’s say you are a digital marketer tracking the launch of a new AI tool.

1. Open your config.yaml.
2. Locate the Keywords section.
3. Add your target terms:

keywords:
  - "Generative AI"
  - "GPT-5"
  - "Open Source"
filter_keywords:
  - "scam"
  - "crypto bot"  # Exclude spammy results

4. Run python main.py. TrendRadar will scrape the enabled platforms, filter for your keywords, and generate an HTML report in the output/ folder.

2. Advanced Feature: The AI Analysis (MCP)

This is where TrendRadar shines. By running TrendRadar as an MCP Server, you can connect it to AI interfaces like Claude Desktop or Cursor.

Step-by-Step MCP Setup:

1. Ensure you have the mcp python package installed (pip install mcp).
2. Run the MCP server script provided in the repo (usually python server.py or similar—check the latest README for the specific entry point).
3. Configure your AI Client (e.g., Claude Desktop) to recognize the server. Add this to your Claude config:

{
  "mcpServers": {
    "trendradar": {
      "command": "python",
      "args": ["/path/to/TrendRadar/mcp_server.py"]
    }
  }
}

4. The Magic: Now, open Claude and ask: “Check the latest trends from TrendRadar. summarized what users on Hacker News and Reddit are saying about ‘Python 3.14’ today.”

The AI will query your local TrendRadar instance, pull the raw data, and synthesize a natural language report for you.

3. Real-World Scenario: Crisis Management

Scenario: A PR manager needs to know if a brand crisis is spreading from Twitter to other platforms.

• Setup: Enable twitter, reddit, and google_news in the config.
• Action: Set RUN_MODE to incremental (checks for new items only) and the interval to 15 minutes.
• Output: You receive a Telegram push notification the moment the brand name enters the “Top 10” trending list on any supported platform.

Tips for Optimization and Best Practices

To get the most out of this AI trend analysis tool, keep these tips in mind:

• Manage API Limits: Some platforms (like Twitter/X) have strict API limits. TrendRadar often uses web scraping as a fallback, but increasing the scraping interval (e.g., every 60 mins instead of 5) ensures you don’t get IP-banned.
• Use “Stop Words”: The internet is noisy. Aggressively populate your filter_keywords list in the config to block celebrity gossip or politics if you are strictly focused on tech trends.
• Leverage GitHub Actions: If you don’t have a server, use the “Zero-Code” deployment option via GitHub Actions (detailed in the repo’s Wiki). This allows the scraper to run on GitHub’s cloud for free and email you the results daily.
• Keep Dependencies Fresh: Open-source scrapers break often when platforms change their UI. Run git pull and pip install --upgrade -r requirements.txt weekly to get the latest fixes from the maintainer (sansan0).

Conclusion

TrendRadar represents a shift in how we consume information. Instead of doom-scrolling through infinite feeds, we can now use open-source automation to bring the feeds to us—filtered, analyzed, and prioritized. Whether you are a developer looking to integrate trend data into your app or a marketer seeking an edge, TrendRadar’s MCP integration makes it a formidable tool in your arsenal.