"Hugging Face" matching MCP servers:

Enables access to the Hugging Face Hub API to search and retrieve information about machine learning models, datasets, and their metadata. Provides comprehensive tools for exploring the Hugging Face ecosystem including model details, dataset information, and parquet file access.

Enables interaction with the Hugging Face Dataset Viewer API, allowing users to browse, search, filter, and analyze datasets hosted on the Hugging Face Hub.

An MCP server that provides Hugging Face Hub API and Search endpoints through multiple transport protocols (STDIO, SSE, StreamableHTTP, and StreamableHTTPJson), enabling integration with AI model capabilities.

An unofficial MCP server that provides semantic search capabilities for Hugging Face models and datasets, enabling Claude and other MCP-compatible clients to search, discover, and explore the Hugging Face ecosystem using natural language queries.

A production-ready local MCP server for Agent Studio, providing tools for calculation, search, chat, and Hugging Face text generation via the Inference API.

An MCP server that turns the Hugging Face Hub into a conversational model picker with a tool-use eval harness to prove the agent actually uses it correctly.

Enables MCP-compatible AI agents to run structured searches over India's court judgments, tribunal matters, and legislation directly from Hugging Face parquet files, with automatic license attribution.

Enables searching and gathering information from multiple online sources (web, Twitter/X, Telegram, GitHub, Hugging Face, arXiv) through a unified MCP interface.

Supercharges Claude Desktop with persistent semantic memory, sandboxed file I/O, live web search, and local emotional intelligence using a local ChromaDB and Hugging Face model.

An MCP server for the Hugging Face Dataset Viewer API that enables searching, fetching, and filtering datasets on the Hugging Face Hub. It allows users to explore schemas, perform full-text searches, and analyze dataset statistics through natural language.

This MCP server enables users to interact with and analyze the dair-ai/emotion dataset from Hugging Face containing labeled Twitter messages. It provides tools to sample data, search text, and perform statistical analysis on emotion distributions.

MCP Extension that gives LLMs access to arXiv and Hugging Face papers, enabling users to discuss papers, search for new research, and organize literature reviews through natural conversation.

Enables real-time tracking of AI/LLM research progress by searching and aggregating content from arXiv, GitHub, Hugging Face, and Papers with Code. Supports intelligent search, automated daily/weekly research summaries, and covers 15+ AI research areas with smart caching.

Search and retrieve models, datasets, and spaces from Hugging Face Hub. Enables browsing trending items and getting detailed info on repos.

Enables searching, fetching, and analyzing scientific papers from ArXiv, ACL Anthology, Semantic Scholar, and Hugging Face datasets, with optional LLM-powered document QA and research proposal workflows.

Provides direct access to the Hugging Face Hub for searching models and datasets, fetching metadata, and running inference on text, images, and audio.

Enables market research and analytics for digital, audio, POD, and ebook publishing by integrating DuckDuckGo search, Gemini API, Supabase, Airtable, Hugging Face, and GitHub.

Enables AI-powered Amazon product searches and recommendations by integrating the Amazon API with Hugging Face models. It allows users to filter products by price and specific features to receive tailored shopping suggestions.

Provides AI agents with 6 tools for searching Hugging Face models, GitHub trending repos, analyzing GitHub repositories, fetching dev.to articles, Show HN launches, and Product Hunt daily launches. Built for dev tooling research and AI ecosystem analysis.

A multi-MCP server hub that hosts several independent MCP servers (e.g., DuckDuckGo, Zhihu, Doubao, Academic) with per-path authentication, deployable on Hugging Face Spaces.