# TrendRadar MCP Server: AI-Driven Public Opinion & Multi-Channel Alerts

> TrendRadar is an AI-driven MCP Server for public opinion and trend monitoring, aggregating multi-platform hot topics and RSS feeds. It solves information overload by providing smart alerts and AI-filtered news analysis, ideal for developers building AI natural language conversation analysis or trend prediction systems.

**Published:** 2026-08-16T12:00:31.623+00:00

**Keywords:** trendradar,mcp-server,ai-monitoring,public-opinion,multi-channel-alerts

# TrendRadar: Precision AI Alerts via MCP

TrendRadar is an AI-driven public opinion and trend monitor, aggregating hot topics from multiple platforms and RSS feeds to deliver smart, filtered alerts. With a significant 61,483 GitHub stars, this Python-based MCP Server is engineered to cut through information overload, providing developers with a structured way to integrate real-time trend data into their AI applications.

## MCP-Driven Multi-Channel AI Delivery

The core of TrendRadar's MCP integration lies in its ability to push AI-generated content directly to various communication channels. As of `mcp-v4.0.0` (released 2026/02/09), AI-written messages can be sent to 9 different platforms, including Feishu, DingTalk, Telegram, and email. The system automatically adapts Markdown formatting for each platform, eliminating manual adjustments.

A key feature for developers is the `get_channel_format_guide` tool. This exposes channel-specific formatting rules and limitations to an AI model, enabling it to generate better-laid-out content. For handling lengthy messages, TrendRadar intelligently splits content according to channel-specific byte limits (e.g., Feishu's 30KB, DingTalk's 20KB), with these configurations managed via `config.yaml`. The batch processing functions directly reuse TrendRadar's core modules, ensuring code efficiency.

## Extracting and Analyzing Content with MCP Tools

TrendRadar exposes a suite of tools for content extraction and analysis, crucial for any AI system aiming to understand public discourse.

Key tools include:
- `search_news(query="关键词", include_url=True)`: Initiates a search for news articles based on a query, with an option to include URLs.
- `read_article(url=...)`: Fetches the full text of a single article in Markdown format using Jina AI Reader.
- `read_articles_batch`: Reads up to 5 articles in a batch, with automatic rate limiting.
- `get_latest_rss`: Retrieves the latest RSS entries, supporting multi-day queries and cross-date URL deduplication.
- `search_rss`: Searches through RSS feeds.
- `get_rss_feeds_status`: Provides the status of configured RSS feeds.
- `aggregate_news`: Performs cross-platform news deduplication and aggregation.
- `compare_periods`: Conducts period-over-period analysis (e.g., week-on-week, month-on-month).
- `find_related_news`: Combines previous functionalities to find similar and related news.
- `get_trending_topics`: Enhanced with an `auto_extract` mode to automatically identify trending topics and support `/pattern/` regular expressions.

All 21 tool functions are wrapped with `asyncio.to_thread()` for asynchronous consistency, and their return values are standardized to a `{success, summary, data, error}` structure. TrendRadar also exposes MCP Resources for `platforms`, `rss-feeds`, `available-dates`, and `keywords`.

## AI-Powered Filtering and Translation

TrendRadar's core AI capabilities extend to filtering and translation. The `filter` method can be set to `ai` in `config.yaml`, with a `min_score` threshold (1-10) for pushing content. This AI filtering shares model configurations with AI analysis and translation.

For multi-language support, the `ai_translation` feature can be enabled in `config.yaml`, allowing content to be translated into any language, such as English, Korean, or Japanese. This feature supports custom translation styles via `ai_translation_prompt.txt` and uses intelligent batch processing to optimize API calls.

```yaml
# config.yaml quick start example for AI translation
ai_translation:
  enabled: true
  language: "English"  # Target translation language
```

This makes TrendRadar a strong candidate for developers building global AI-driven news analysis or sentiment platforms, allowing them to process and disseminate information across linguistic barriers and diverse communication channels efficiently.

## References

- [TrendRadar on GitHub](https://github.com/sansan0/TrendRadar)
- [Model Context Protocol Documentation](https://modelcontextprotocol.io/introduction)
- [TrendRadar on model-context-protocol.com](https://model-context-protocol.com/servers/)

## Related Repository

- [trendradar](https://model-context-protocol.com/servers/trendradar)

**Source:** https://model-context-protocol.com/blog/trendradar-mcp-server-ai-driven-public-opinion-multi-channel-mcp-server-guide