hf-trending-mcp
Fetches trending Gradio spaces from Hugging Face, allowing users to discover popular interactive demos built with Gradio.
Tracks trending models, datasets, and spaces on Hugging Face, providing tools to fetch trending content, search for specific items, and analyze current trends on the platform.
Retrieves trending Streamlit spaces from Hugging Face, enabling users to find popular interactive applications built with Streamlit.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@hf-trending-mcpshow me the top 5 trending models"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
HF Trending MCP Server
An MCP server that tracks trending models, datasets, and spaces on Hugging Face.
It provides tools and prompts to fetch trending content, search for specific items, and analyze current trends, making it a valuable tool for developers, researchers, and AI enthusiasts.
Features
Trending Tools: Retrieve the top trending models, datasets, and spaces with customizable limits.
Search Functionality: Search trending items by query across models, datasets, or spaces.
Trend Analysis Prompt: A built-in prompt to analyze and summarize current trends on Hugging Face.
Related MCP server: Hugging Face MCP Server
Installation
Setup
Clone the Repository:
git clone https://github.com/kukapay/hf-trending-mcp.git cd hf-trending-mcpInstall Dependencies:
pip install mcp[cli] httpxClient Configuration:
"mcpServers": { "hf-trending-mcp": { "command": "python", "args": [ "path/to/hf-trending-mcp/main.py" ] } }
Usage
Once the server is running or installed, you can interact with it using an MCP client (e.g., Claude Desktop). Below are some example prompts:
Fetch Trending Content
Input:
"Show me the top 5 trending models on Hugging Face."
Output:
google/vit-base-patch16-224 (Downloads: 12345, Likes: 678)
Tags: vision, transformer
facebook/detr-resnet-50 (Downloads: 9876, Likes: 543)
Tags: object-detection, transformer
...Input:
"What are the top 10 trending datasets right now?"
Output:
imdb (Downloads: 45678, Likes: 2345)
Tags: nlp, sentiment-analysis
squad (Downloads: 34567, Likes: 1987)
Tags: nlp, question-answering
...Input:
"List the top 3 trending spaces."
Output:
gradio/image-classifier (Likes: 890, SDK: Gradio)
Tags: demo, vision
streamlit/text-generator (Likes: 765, SDK: Streamlit)
Tags: nlp, demo
...Search Trending Items
Input:
"Search for trending NLP models, limit to 5."
Output:
bert-base-uncased (Likes: 456)
Tags: nlp, transformer
distilbert-base-uncased (Likes: 321)
Tags: nlp, transformer
...Input:
"Find trending datasets about computer vision."
Output:
coco (Likes: 1234)
Tags: computer-vision, object-detection
imagenet-1k (Likes: 987)
Tags: computer-vision, classification
...Analyze Trends
Input:
"Analyze the current trends on Hugging Face."
Output:
A detailed response combining results from all trending tools, followed by an analysis (generated by the client’s AI):
Top Trending Models:
google/vit-base-patch16-224 (Downloads: 12345, Likes: 678)
Tags: vision, transformer
...
Top Trending Datasets:
imdb (Downloads: 45678, Likes: 2345)
Tags: nlp, sentiment-analysis
...
Top Trending Spaces:
gradio/image-classifier (Likes: 890, SDK: Gradio)
Tags: demo, vision
...
Summary: Vision models like ViT are trending, likely due to recent advancements in image processing. NLP datasets remain popular for text-based AI research, and Gradio spaces are gaining traction for interactive demos.API Details
Tools
get_trending_models(limit: int = 10): Fetches trending models with downloads, likes, and tags.get_trending_datasets(limit: int = 10): Fetches trending datasets with downloads, likes, and tags.get_trending_spaces(limit: int = 10): Fetches trending spaces with likes, SDK info, and tags.search_trending(query: str, type: str = "models", limit: int = 10): Searches trending items by query and type.
Prompt
analyze_trends(): Guides the analysis of trending items with a structured prompt.
License
This project is licensed under the MIT License. See the LICENSE file for details.
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