"Hugging Face" matching MCP servers:

Enables LLMs to search and retrieve read-only information from the Hugging Face Hub about models, datasets, Spaces, papers, and collections. It also supports model comparison and paper summarization prompts.

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.

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.

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 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.

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 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.

Semantic code search for any MCP client: indexes local folders of code and docs and answers plain-language or code queries with matching passages, file paths, line ranges and scores. Embedding runs on the local CPU with the Motherlode code-search encoder, so no file content leaves the machine; the encoder downloads once from Hugging Face. Install: npx -y blackwindow-weave-mcp.

Tell your AI agent what you want a model to do. It searches Hugging Face for each part of the job and tells you which parts already exist there: which are an exact fit, which to fine-tune, which to train, and which are just code. Every model it names is real, current, and sized for your hardware.

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.

Enables agents to trace any Hugging Face model's ancestry and descendants, find the most finetuned base models, and explore whole model families. It exposes tools for lineage, children, leaderboards, family views, and model search, answering from nightly-rebuilt index data.

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.

Enables safety-ranked discovery of open-source components across npm, PyPI, crates.io, RubyGems, GitHub, and Hugging Face, with license, security, and health checks.

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.