Aggregate trending data from 55+ platforms including Weibo, Zhihu, Bilibili, and GitHub for LLM and RAG applications. Developers use this to inject real-time trending content into AI systems without building individual platform scrapers.
io.github.FrankXMX/dailyhotmcp
Local install
STDIO
1 required env var
How models use it and what it is built for.
Aggregate trending data from 55+ platforms including Weibo, Zhihu, Bilibili, and GitHub for LLM and RAG applications. Developers use this to inject real-time trending content into AI systems without building individual platform scrapers.
Local install — runs as a subprocess.
Configuration this server reads at startup.
Your API key for the service
Where to find authoritative docs and source for Frankxmx Dailyhot.
Paste any of these into Agent Studio after connecting Frankxmx Dailyhot.
Common questions about connecting and running Frankxmx Dailyhot.
What platforms does this MCP server pull trending data from?
It aggregates hot lists from 55+ platforms including Weibo, Zhihu, Bilibili, GitHub, and others. The exact platform list is available in the server documentation.
How do I set up the API key for dailyhotmcp?
Set the YOUR_API_KEY environment variable before running the server. This is required for authentication with the trending data service.
Can I use this server with RAG and LLM applications?
Yes, it's specifically designed for LLM and RAG scenarios. You can inject real-time trending data into your AI pipelines to keep responses current.
What format does the trending data come back in?
The server returns structured data suitable for LLM processing. Refer to the server's schema documentation for exact field formats and response structure.
Is there rate limiting or pricing for the API key?
Rate limits and pricing depend on your API key tier. Check your account dashboard or the service documentation for current limits and billing details.
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