What is Hf Trending Mcp

hf-trending-mcp is an MCP server designed to track trending AI models, datasets, and spaces on Hugging Face, providing valuable tools for developers and researchers.

Use cases

Use cases include fetching the latest trending models, datasets, and spaces for research, development, and analysis of AI trends on Hugging Face.

How to use

To use hf-trending-mcp, clone the repository, install the required dependencies, and configure the client. Once set up, interact with the server using prompts to fetch trending content.

Key features

Key features include trending tools to retrieve top models and datasets, search functionality for querying items, and a trend analysis prompt for summarizing current trends.

Where to use

hf-trending-mcp can be used in fields such as artificial intelligence, data science, and machine learning, particularly for those interested in the latest trends in AI resources.

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.

Installation

Setup

1. Clone the Repository:

   git clone https://github.com/kukapay/hf-trending-mcp.git
   cd hf-trending-mcp
   

2. Install Dependencies:

   pip install mcp[cli] httpx
   

3. Client 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.