mcpField
H
Huggingface

Huggingface

Remote
pipeworx.ioAI & Agents0Updated 2d ago

About Huggingface

Huggingface is an MCP server for searching and inspecting public models, datasets, and Spaces on the Hugging Face Hub. It serves developers and ML researchers, with public reads available without authentication.

Key features

  1. 01Search models, datasets, and Spaces
  2. 02Inspect repository details
  3. 03List model and dataset files
  4. 04View trending models and datasets
  5. 05Filter results by metadata

Use cases

  1. 01Find models by task or library
  2. 02Locate datasets by language or category
  3. 03Inspect a repository before use
  4. 04Browse trending Hub resources

MCP support

Exposes Hugging Face Hub search, inspection, file-listing, and trending tools to MCP clients through Pipeworx or as a standalone local stdio server. Public reads need no authentication; tokens are optional for higher limits and gated or private repositories.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://gateway.pipeworx.io/huggingface/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

Tools

Details

From the project README

@pipeworx/huggingface

Hugging Face Hub MCP — search and inspect models, datasets, and Spaces on the largest open ML repository. No auth required for public reads.

Part of Pipeworx — an MCP gateway connecting AI agents to 1881+ live data sources. This is an independent, unofficial integration — not affiliated with, endorsed by, or published by the upstream provider.

Tools

  • search_models(search?, author?, library?, language?, pipeline_tag?, tags?, sort?, direction?, limit?, full?)
  • search_datasets(search?, author?, language?, task_categories?, sort?, direction?, limit?, full?)
  • search_spaces(search?, author?, sdk?, sort?, direction?, limit?, full?)
  • get_model(repo_id, revision?)
  • get_dataset(repo_id, revision?)
  • get_space(repo_id, revision?)
  • list_model_files(repo_id, revision?) / list_dataset_files(repo_id, revision?)
  • trending_models(limit?) / trending_datasets(limit?)

Auth

Public reads are keyless. Higher rate limits and gated/private repos require a token — optional:

  • Platform key (optional): gateway env PLATFORM_HF_KEY
  • BYO (optional): ?_apiKey=<hf_token>

Data source

https://huggingface.co/api/ — public REST.

Quick Start

Add to your MCP client (Claude Desktop, Cursor, Windsurf, etc.):

{
  "mcpServers": {
    "huggingface": {
      "url": "https://gateway.pipeworx.io/huggingface/mcp"
    }
  }
}
What this endpoint actually serves

tools/list at https://gateway.pipeworx.io/huggingface/mcp returns the tools in the table above plus the shared Pipeworx meta-tools — ask_pipeworx, discover_tools, search_within, remember/recall and the rest of the gateway-wide set. So the tool count you see is larger than this table: a single-pack endpoint currently lists roughly 30 shared tools alongside the pack's own. The connection's initialize response states its exact scope, and is the authoritative answer for a given day.

This is deliberate, not multiplexing by accident. The meta-tools are what let a scoped connection answer a question this pack does not cover — via ask_pipeworx, which routes across the whole catalog — without you adding a second MCP server. There is currently no way to mount a pack endpoint without them; if the extra schemas cost you more context than the routing is worth, connect to the full gateway once rather than to several pack endpoints.

Or connect to the full Pipeworx gateway to get every pack's tools listed directly, instead of just this one's:

{
  "mcpServers": {
    "pipeworx": {
      "url": "https://gateway.pipeworx.io/mcp"
    }
  }
}

Both URLs reach the same gateway and the same 1881+ data sources. The only difference is which pack's tools are listed directly; ask_pipeworx reaches all of them from either one.

No MCP client? Call it over HTTP

curl -X POST https://gateway.pipeworx.io/v1/tools/huggingface_search_models \
  -H 'Content-Type: application/json' \
  -d '{"search":"bert","library":"transformers","limit":20}'

No account needed for the first calls. Inspect any tool: GET https://gateway.pipeworx.io/v1/tools/huggingface_search_models. Find one: POST https://gateway.pipeworx.io/v1/tools/search_packs with {"query":"..."}.

Standalone (no gateway account)

This package also runs as a local stdio MCP server — no Pipeworx account, no gateway round-trip:

{
  "mcpServers": {
    "huggingface": {
      "command": "npx",
      "args": ["-y", "@pipeworx/mcp-huggingface"]
    }
  }
}

Or run it directly to confirm it starts:

npx -y @pipeworx/mcp-huggingface

It speaks MCP over stdin/stdout and answers initialize/tools/list/tools/call for only this pack's tools — none of the shared meta-tools the gateway connection above adds. Same source, same tools, no ask_pipeworx routing.

Using with ask_pipeworx

Instead of calling tools directly, you can ask questions in plain English — this works on the pack endpoint above as well as on the full gateway:

ask_pipeworx({ question: "your question about Huggingface data" })

The gateway picks the right tool and fills the arguments automatically.

More

License

MIT

Comments

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