MCP server providing access to the [Hugging Face Dataset Viewer API](https://huggingface.co/docs/dataset-viewer). Query datasets, explore data
Huggingface mcp mcp server connects Huggingface MCP to AI assistants that speak the Model Context Protocol. MCP server providing access to the Hugging Face Dataset Viewer API. Query datasets, explore data, search content, and analyze statistics from the Hugging Face Hub's extensive collection of machine learning datasets.
MCP server providing access to the Hugging Face Dataset Viewer API. Query datasets, explore data, search content, and analyze statistics from the Hugging Face Hub's extensive collection of machine learning datasets.
Once connected, the assistant can call these 3 tools directly:
Endpoint — ToolPrerequisites — The Prerequisites tool exposed by this serverSetup — The Setup tool exposed by this serverSetup follows the standard MCP pattern: clone or install the server, then register it in your client's configuration file and restart the client. The configuration snippets on this page cover Claude Desktop, Claude Code and Cursor.
AI-service servers chain other models into your assistant, turning a single chat into a small production pipeline. Huggingface MCP sits in that group, and the shape of its toolset — Endpoint, Prerequisites, Setup — tells you what it is really for. Worth comparing against the other ai services servers in this directory before you commit to one, since several overlap in scope but differ sharply in setup cost and permissions.
| Tool | What it does |
|---|---|
| Endpoint | Tool |
| Prerequisites | The Prerequisites tool exposed by this server. |
| Setup | The Setup tool exposed by this server. |
{
"mcpServers": {
"huggingface-mcp": {
"command": "docker",
"args": [
"run",
"--rm",
"-i",
"--name", "huggingface-mcp-claude",
"huggingface-mcp:latest"
]
}
}
}Configuration as documented by the project. Restart the client after saving.
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.