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Trendradar

TrendRadar is a全网hotspot aggregation and intelligent push tool that supports multi - platform news monitoring, keyword screening, and AI intelligent analysis. It can be quickly deployed through GitHub Actions or Docker to achieve personalized hotspot tracking.
Research and dataArtificial intelligence chatbots#Hotspot aggregation#Intelligent push#AI analysis#Multi - platform monitoring.Python
2 points
4.6K
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Overview

Installation

Content Details

Alternatives

What is the TrendRadar MCP Server?

The TrendRadar MCP Server is an AI - enhanced module of the TrendRadar hotspot tracking project. Based on the MCP (Model Context Protocol), it transforms the news data accumulated locally by you (stored in the `output` folder) into an intelligent dialogue interface. You can use natural language to ask questions through AI clients that support MCP (such as Claude Desktop, Cursor, Cherry Studio, etc.), and let the AI help you analyze news trends, find specific information, generate summary reports, etc.

How to use the TrendRadar MCP Server?

Using the MCP Server requires two prerequisites: 1. There is news data generated by the local TrendRadar project running (`output` directory). 2. Install and configure an AI client that supports MCP. After the configuration is completed, you can query and analyze news in the AI chat interface as if you were having a conversation with a person. For example: "Query the hotspots on Zhihu yesterday." or "Analyze the trend of the 'AI' topic in the recent week."

Applicable scenarios

The TrendRadar MCP Server is very suitable for users who need to deeply mine news data, such as: - **Content creators**: Quickly find news materials on specific topics and analyze topic popularity. - **Investors/analysts**: Track the public opinion changes of specific companies or industries and conduct trend analysis. - **Researchers**: Conduct cross - platform data comparison, sentiment analysis, or historical association retrieval. - **Ordinary users**: Use the most natural way (conversation) to understand the news they care about without learning complex query syntax.

Main functions

Basic query and retrieval
Provide a variety of query tools to flexibly retrieve news data by date, platform, and keywords, and quickly locate the required information.
Intelligent trend analysis
Deeply analyze the life cycle, popularity change, explosion detection, and trend prediction of topics to help you understand the laws behind the news.
Data insight and comparison
Conduct cross - platform activity statistics and keyword co - occurrence analysis to compare the focus of different media from a macro perspective.
Sentiment analysis and summary
Analyze the sentiment tendency of news content and intelligently generate summary reports for specified dates or topics.
Advanced search and association
Support searching for similar news, retrieving historically relevant news, and multi - mode search to help discover potential connections between information.
System management and synchronization
Provide tools for obtaining system configuration, status checking, manually triggering data crawling, and synchronizing data from remote storage to the local.
Advantages
🤖 **Natural language interaction**: No need to learn complex commands. You can query and analyze data by speaking.
🔍 **Deep analysis ability**: Provide trend tracking, sentiment analysis, and cross - platform comparison functions far beyond ordinary search.
🔄 **Seamless integration**: Support a variety of mainstream AI clients (Claude, Cursor, Cherry Studio, etc.) with flexible configuration.
📊 **Data - driven**: All analyses are based on your local real news data, and the results are accurate and reliable.
⚡ **Efficient and convenient**: Let the AI handle the cumbersome data screening and sorting work, greatly improving the information processing efficiency.
Limitations
📁 **Dependent on local data**: It can only analyze the historical news data stored in the `output` directory and cannot query real - time network information.
⏳ **Data accumulation takes time**: You need to run the main TrendRadar program for a period of time to accumulate enough data for effective analysis.
🔧 **Requires additional configuration**: You need to configure the MCP Server connection in the AI client, which has a certain threshold for beginners.
💻 **Client compatibility**: The functional experience is affected by the support level of the selected AI client for the MCP protocol.

How to use

Prepare data
Ensure that you have successfully deployed and run the main TrendRadar project, and news data has been accumulated in the `output` directory. The project comes with test data from November 1st to 15th, 2025, which can be used for a quick experience.
Select and configure the client
According to your preference, select an AI client that supports MCP (Cherry Studio is recommended as it has a graphical configuration interface). Add the TrendRadar MCP Server to the configuration according to the client's documentation. Usually, you need to specify the server type (stdio or http) and the project path.
Start and verify
Save the configuration and restart your AI client. In the client's tool list or chat interface, you should see a toolset named 'trendradar'. You can try a simple query to verify if the connection is successful.
Start conversational analysis
After the configuration is successful, you can use natural language to put forward various analysis requirements as if you were having a conversation with an assistant. The system will automatically call the corresponding tools to process your request.

Usage examples

Case 1: Tracking industry dynamics (Investors)
As an investor who pays attention to the technology industry, you want to understand the recent public opinion attention and change trend in the field of 'Artificial Intelligence chips'.
Case 2: Finding creative materials (Self - media people)
You plan to write an article about 'Urban cycling' and need to collect recent relevant hot news and netizens' discussion perspectives.
Case 3: Daily briefing generation (Managers)
You need to quickly understand the key news related to your company's business that happened the previous day every morning.
Case 4: In - depth public opinion analysis (PR personnel)
Your company recently launched a new product and needs to comprehensively evaluate its public opinion response and sentiment tendency on various media platforms.

Frequently Asked Questions

Can the MCP Server query real - time news?
Should I choose the STDIO mode or the HTTP mode?
Why does the AI reply that there is no data or the tool call fails after I ask a question?
Which AI clients are supported?
What is the difference between this function and the push notification of TrendRadar itself?

Related resources

TrendRadar main project repository
Get the complete code, deployment guide, and update log of the TrendRadar hotspot tracking project. The MCP Server is part of this project.
Graphic deployment tutorial for Cherry Studio
Reply 'mcp' in the official account 'Silicon - based Tea Break' to get it. It provides the most detailed and intuitive configuration steps for the Cherry Studio client.
README - Cherry - Studio.md
The dedicated configuration documentation for Cherry Studio in the project, including detailed instructions for both STDIO and HTTP modes.
README - MCP - FAQ.md
The MCP conversation skills and advanced usage guide in the project, teaching you how to more effectively collaborate with the AI to analyze data.
Model Context Protocol official website
Understand the official introduction, technical specifications, and latest developments of the MCP protocol.

Installation

Copy the following command to your Client for configuration
{
  "mcpServers": {
    "trendradar": {
      "url": "http://127.0.0.1:3333/mcp",
      "type": "streamableHttp"
    }
  }
}

{
  "mcpServers": {
    "trendradar": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/TrendRadar",
        "run",
        "python",
        "-m",
        "mcp_server.server"
      ],
      "env": {},
      "disabled": false,
      "alwaysAllow": []
    }
  }
}

{
     "mcpServers": {
       "trendradar": {
         "url": "http://localhost:3333/mcp",
         "description": "TrendRadar 新闻热点聚合分析"
       }
     }
   }

{
  "mcpServers": {
    "trendradar": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/TrendRadar",
        "run",
        "python",
        "-m",
        "mcp_server.server"
      ]
    }
  }
}
Note: Your key is sensitive information, do not share it with anyone.

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