huggingface

Official
GitHub Repo

Overview

The huggingface-mcp-server is a Model Context Protocol (MCP) server that enables AI assistants and agents to interact directly with the Hugging Face ecosystem, including models, datasets, and Spaces. It allows AI-driven workflows to discover models, inspect metadata, download artifacts, and reason about available ML resources without switching tools or manually navigating the Hugging Face Hub.

This server is especially useful for model exploration, experimentation, research, and workflows that integrate open-source ML assets into AI-assisted development.

Transport

streamable-http

Tools

  • hf_whoami
  • space_search
  • model_search
  • model_details
  • paper_search
  • dataset_search
  • dataset_details
  • hf_doc_search
  • hf_doc_fetch
  • gr1_flux1_schnell_infer

Key Capabilities

  • Model discovery — Explore thousands of open-source and hosted models across tasks, modalities, and frameworks.
  • Dataset access — Inspect datasets, schemas, and metadata for training, evaluation, or analysis.
  • Metadata-driven reasoning — Use tags, licenses, metrics, and descriptions to select appropriate models or datasets.
  • Reproducible workflows — Retrieve exact model versions and artifacts for consistent experimentation.
  • Research and prototyping support — Enable AI assistants to recommend or compare models based on task requirements.

How It Works

The huggingface MCP server runs as an MCP service and connects to the Hugging Face Hub using configured credentials or anonymous access, depending on the resource. AI clients communicate with the server over the MCP protocol to request information about models, datasets, or hosted resources as part of broader reasoning workflows.

The server handles authentication, API communication, and response normalization, returning structured results that AI assistants can reason over directly. This abstraction allows AI workflows to incorporate up-to-date knowledge of available ML assets without embedding Hugging Face–specific API logic in the client.

By exposing the Hugging Face ecosystem through MCP, the server enables workflows such as “find the best open-source model for this task,” “compare datasets for training,” or “inspect model licenses and requirements” — all driven through natural language and AI reasoning within a single environment.