Overview

The HotNews MCP Server delivers up-to‑date trending topics from nine major Chinese social and news platforms. By exposing a single tool, it allows AI assistants to fetch and embed real‑time popular content directly into conversations. The server solves the problem of data latency and fragmentation that developers face when gathering hot topics from multiple sources—each platform typically offers its own API or web scraping route. With a unified MCP interface, the server consolidates these disparate feeds into one consistent, Markdown‑formatted output that includes clickable links and optional heat indices where available.

For developers building AI applications, this server is a valuable bridge between the model and the dynamic pulse of Chinese media. It eliminates the need to write separate scrapers, manage API keys, or handle rate limits for each platform. Instead, a single function call can retrieve trending items from Zhihu, 36Kr, Baidu Hot Discussion, Bilibili, Weibo, Douyin, Hupu, Douban, or IT News. The output is already formatted for readability, making it ready to be inserted into chat responses or dashboards without additional processing.

Key capabilities include:

• Real‑time aggregation of hot topics from nine high‑traffic Chinese sites.
• Heat index support to indicate the relative popularity of each item, giving context at a glance.
• Markdown output with hyperlinks, enabling instant navigation for end users.
• MCP‑compatible tool definition, ensuring seamless integration with any AI assistant that follows the Model Context Protocol.
• Flexible source selection via a list of platform IDs, allowing fine‑grained control over which feeds to include.

Typical use cases span a wide range of scenarios. A news aggregator chatbot can surface the latest viral stories without exposing users to multiple sites. An internal corporate assistant might monitor industry chatter on 36Kr and IT News to keep teams informed of market trends. Social media managers can pull the latest Weibo or Douyin hot topics to inform content strategy in real time. Finally, developers building trend‑aware recommendation engines can tap into the server’s heat indices to weight content relevance.

Integration is straightforward: once the MCP server is running, an AI model can invoke with a list of desired platform IDs. The server returns a Markdown string that the model can inject directly into its response, preserving formatting and interactivity. This tight coupling between data retrieval and model output reduces latency and simplifies the overall architecture, allowing developers to focus on higher‑level application logic rather than data plumbing.