hf-mcp-server packages
@evalstate
关于 hf-mcp-server packages
Hugging Face MCP Server
基本信息
配置
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"hf-mcp-server": {
"command": "npx",
"args": [
"@llmindset/hf-mcp-server",
"#",
"Start",
"in",
"STDIO",
"mode"
]
}
}
}工具
未检测到工具
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
概览
What is hf-mcp-server?
hf-mcp-server is the official Hugging Face MCP server that connects LLMs to the Hugging Face Hub and thousands of Gradio AI applications. It provides MCP implementations of Hub API and search endpoints, enabling AI assistants to interact with Hugging Face resources.
How to use hf-mcp-server?
Install via one-click links for Claude Desktop/Web, VSCode, or Cursor, or use CLI commands for Claude Code (claude mcp add hf-mcp-server -t http https://huggingface.co/mcp?login) and Gemini CLI (gemini mcp add -t http huggingface https://huggingface.co/mcp?login). Run locally with npx @llmindset/hf-mcp-server (STDIO), npx @llmindset/hf-mcp-server-http (StreamableHTTP), or via Docker. Configure tokens via environment variables like DEFAULT_HF_TOKEN or HF_TOKEN.
Key features of hf-mcp-server
- Supports STDIO, StreamableHTTP, and StreamableHTTP JSON transports
- Web management interface on port 3000 for toggling tools
- Proxy tools loaded from CSV-defined MCP endpoints
- Skills directory support for shared Hugging Face skill catalogs
- Stateful connection management with configurable timeouts
- Environment variable configuration for timeouts, transport, and tool options
Use cases of hf-mcp-server
- Connect an LLM to search and retrieve models, datasets, and Spaces from the Hugging Face Hub
- Run Gradio AI applications directly through MCP tools
- Build custom AI assistants that can interact with Hugging Face APIs
- Deploy a local MCP server with proxy tools for internal workflows
FAQ from hf-mcp-server
What transports does hf-mcp-server support?
STDIO (stdin/stdout), StreamableHTTP (stateful HTTP with SSE), and StreamableHTTP JSON (stateless JSON-RPC mode).
How do I run hf-mcp-server locally?
Use npx @llmindset/hf-mcp-server for STDIO, npx @llmindset/hf-mcp-server-http for StreamableHTTP, or pull and run the Docker image from ghcr.io/evalstate/hf-mcp-server:latest.
How do I authenticate with the server?
Pass a Hugging Face token via the Authorization: Bearer header when configuring an MCP client, or set the DEFAULT_HF_TOKEN environment variable for development/STDIO use.
What environment variables control the server?
Key variables: TRANSPORT (transport mode), DEFAULT_HF_TOKEN, HF_TOKEN (STDIO fallback), HF_API_TIMEOUT, ALLOW_INTERNAL_ADDRESS_HOSTS, MCP_STRICT_COMPLIANCE, AUTHENTICATE_TOOL, SEARCH_ENABLES_FETCH, PROXY_TOOLS_CSV, GRADIO_SKIP_INITIALIZE, and HF_SKILLS_DIR.
Where can I manage tools after installation?
Navigate to https://huggingface.co/settings/mcp to configure your tools and Spaces. The local web dashboard on port 3000 also allows toggling tools.
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