Content
# Hugging Face Official MCP Server
<img src='https://github.com/evalstate/hf-mcp-server/blob/main/hf-logo.svg' width='100'>
Welcome to the official Hugging Face MCP Server 🤗. Connect your LLM to the Hugging Face Hub and thousands of Gradio AI Applications.
## Installing the MCP Server
Follow the instructions below to get started:
<details>
<summary>Install in <b>Claude Desktop</b> or <b>claude.ai</b></summary>
<br />
Click [here](https://claude.ai/redirect/website.v1.67274164-23df-4883-8166-3c93ced276be/directory/37ed56d5-9d61-4fd4-ad00-b9134c694296) to add the Hugging Face connector to your account.
Alternatively, navigate to [https://claude.ai/settings/connectors](https://claude.ai/settings/connectors), and add "Hugging Face" from the gallery.
<img src='docs/claude-badge.png' width='50%' align='center' />
</details>
<details>
<summary>Install in <b>Claude Code</b></summary>
<br />
Enter the command below to install in <b>Claude Code</b>:
```bash
claude mcp add hf-mcp-server -t http https://huggingface.co/mcp?login
```
Then start `claude` and follow the instructions to complete authentication.
```bash
claude mcp add hf-mcp-server \
-t http https://huggingface.co/mcp \
-H "Authorization: Bearer <YOUR_HF_TOKEN>"
```
</details>
<details>
<summary>Install in <b>Gemini CLI</b></summary>
<br />
Enter the command below to install in <b>Gemini CLI</b>:
```bash
gemini mcp add -t http huggingface https://huggingface.co/mcp?login
```
Then start `gemini` and follow the instructions to complete authentication.
There is also a HuggingFace Gemini CLI extension that bundles the MCP server
with a context file and custom commands, teaching Gemini how to better use
all MCP tools.
```bash
gemini extensions install https://github.com/huggingface/hf-mcp-server
```
Start `gemini` and run `/mcp auth huggingface` to authenticate the extension.
</details>
<details>
<summary>Install in <b>VSCode</b></summary>
<br />
Click <a href="vscode:mcp/install?%7B%22name%22%3A%22huggingface%22%2C%22gallery%22%3Atrue%2C%22url%22%3A%22https%3A%2F%2Fhuggingface.co%2Fmcp%3Flogin%22%7D">here</a> to add the Hugging Face connector directly to VSCode. Alternatively, install from the gallery at [https://code.visualstudio.com/mcp](https://code.visualstudio.com/mcp):
<img src='docs/vscode-badge.png' width='50%' align='center' />
If you prefer to configure manually or use an auth token, add the snippet below to your `mcp.json` configuration:
```JSON
"huggingface": {
"url": "https://huggingface.co/mcp",
"headers": {
"Authorization": "Bearer <YOUR_HF_TOKEN>"
}
```
</details>
<details>
<summary>Install in <b>Cursor</b></summary>
<br />
Click <a href="https://cursor.com/en/install-mcp?name=Hugging%20Face&config=eyJ1cmwiOiJodHRwczovL2h1Z2dpbmdmYWNlLmNvL21jcD9sb2dpbiJ9">here</a> to install the Hugging Face MCP Server directly in <b>Cursor</b>.
If you prefer to use configure manually or specify an Authorization Token, use the snippet below:
```JSON
"huggingface": {
"url": "https://huggingface.co/mcp",
"headers": {
"Authorization": "Bearer <YOUR_HF_TOKEN>"
}
```
</details>
Once installed, navigate to https://huggingface.co/settings/mcp to configure your Tools and Spaces.
> [!TIP]
> Add ?no_image_content=true to the URL to remove ImageContent blocks from Gradio Servers.
## Quick Guide (Repository Packages)
This repo contains:
- (`/mcp`) MCP Implementations of Hub API and Search endpoints for integration with MCP Servers.
- (`/app`) An MCP Server and Web Application for deploying endpoints.
### MCP Server
The following transports are supported:
- STDIO
- StreamableHTTP
- StreamableHTTP in Stateless JSON Mode (**StreamableHTTPJson**)
The Web Application and HTTP Transports start by default on Port 3000.
The StreamableHTTP service is available at `/mcp`. Although though not strictly enforced by the specification this is common convention.
> [!TIP]
> The Web Application allows you to switch tools on and off. For STDIO and StreamableHTTP this will send a ToolListChangedNotification to the MCP Client. In StreamableHTTPJSON mode the tool will not be listed when the client next requests the tool lists.
### Running Locally
You can run the MCP Server locally with either `npx` or `docker`.
```bash
npx @llmindset/hf-mcp-server # Start in STDIO mode
npx @llmindset/hf-mcp-server-http # Start in Streamable HTTP mode
npx @llmindset/hf-mcp-server-json # Start in Streamable HTTP (JSON RPC) mode
```
To run with docker:
```bash
docker pull ghcr.io/evalstate/hf-mcp-server:latest
docker run --rm -p 3000:3000 ghcr.io/evalstate/hf-mcp-server:latest
```
All commands above start the Management Web interface on http://localhost:3000/. The Streamable HTTP server is accessible on http://localhost:3000/mcp. See [Environment Variables](#Environment Variables) for configuration options. Docker defaults to Streamable HTTP (JSON RPC) mode.
### Developing OpenAI Apps SDK Components
To build and test the Apps SDK component, run
```bash
cd packages/app
npm run dev:widget
```
Then open `http://localhost:5173/gradio-widget-dev.html`. This will bring up a browser with HMR where you can send Structured Content to the components for testing.
## Development
This project uses `pnpm` for build and development. Corepack is used to ensure everyone uses the same pnpm version (10.12.3).
```bash
# Install dependencies
pnpm install
# Build all packages
pnpm build
```
### Build Commands
`pnpm run clean` -> clean build artifacts
`pnpm run build` -> build packages
`pnpm run start` -> start the mcp server application
`pnpm run buildrun` -> clean, build and start
`pnpm run dev` -> concurrently watch `mcp` and start dev server with HMR
## Docker Build
Build the image:
```bash
docker build -t hf-mcp-server .
```
Run with default settings (Streaming HTTP JSON Mode), Dashboard on Port 3000:
```bash
docker run --rm -p 3000:3000 -e DEFAULT_HF_TOKEN=hf_xxx hf-mcp-server
```
Run STDIO MCP Server:
```bash
docker run -i --rm -e TRANSPORT=stdio -p 3000:3000 -e DEFAULT_HF_TOKEN=hf_xxx hf-mcp-server
```
`TRANSPORT` can be `stdio`, `streamableHttp` or `streamableHttpJson` (default).
### Transport Endpoints
The different transport types use the following endpoints:
- Streamable HTTP: `/mcp` (regular or JSON mode)
- STDIO: Uses stdin/stdout directly, no HTTP endpoint
### Stateful Connection Management
The `streamableHttp` transport is _stateful_ - it maintains a connection with the MCP Client through an SSE connection. When using this transport, the following configuration options take effect:
| Environment Variable | Default | Description |
|-----------------------------------|---------|-------------|
| `MCP_CLIENT_HEARTBEAT_INTERVAL` | 30000ms | How often to check connection health |
| `MCP_CLIENT_CONNECTION_CHECK` | 90000ms | How often to check for stale sessions |
| `MCP_CLIENT_CONNECTION_TIMEOUT` | 300000ms | Remove sessions inactive for this duration |
| `MCP_PING_ENABLED` | true | Enable ping keep-alive for sessions |
| `MCP_PING_INTERVAL` | 30000ms | Interval between ping cycles |
### Environment Variables
The server respects the following environment variables:
- `TRANSPORT`: The transport type to use (stdio, streamableHttp, or streamableHttpJson)
- `DEFAULT_HF_TOKEN`: ⚠️ Requests are serviced with the HF_TOKEN received in the Authorization: Bearer header. The DEFAULT_HF_TOKEN is used if no header was sent. Only set this in Development / Test environments or for local STDIO Deployments. ⚠️
- If running with `stdio` transport, `HF_TOKEN` is used if `DEFAULT_HF_TOKEN` is not set.
- `HF_API_TIMEOUT`: Timeout for Hugging Face API requests in milliseconds (default: 12500ms / 12.5 seconds)
- `USER_CONFIG_API`: URL to use for User settings (defaults to Local front-end)
- `ALLOW_INTERNAL_ADDRESS_HOSTS`: Optional comma-separated host allowlist to permit internal/reserved DNS resolutions for trusted domains during outbound checks (supports exact hosts and `*.` wildcards, for example: `huggingface.co,*.hf.space`).
- `MCP_STRICT_COMPLIANCE`: set to True for GET 405 rejects in JSON Mode (default serves a welcome page).
- `AUTHENTICATE_TOOL`: whether to include an `Authenticate` tool to issue an OAuth challenge when called
- `SEARCH_ENABLES_FETCH`: When set to `true`, automatically enables the `hf_doc_fetch` tool whenever `hf_doc_search` is enabled
- `PROXY_TOOLS_CSV`: Optional CSV that defines Streamable HTTP proxy tool sources (see below).
- `GRADIO_SKIP_INITIALIZE`: When set to `true`, Gradio MCP calls skip the `initialize` handshake and issue `tools/call` directly.
- `HF_SKILLS_DIR`: Local directory containing a prebuilt skills distribution in the [SEP-2640](https://github.com/modelcontextprotocol/modelcontextprotocol/pull/2640) index format (a `skill://index.json` whose entries carry verbatim `frontmatter`, an optional `url` + `digest`, and an `archives[]` array, alongside the expanded `SKILL.md`/supporting-file tree and `.tar.gz` archives). The server walks each skill directory and exposes every file as an individual `skill://` resource, supports `resources/directory/read` for scoped navigation, and advertises the `io.modelcontextprotocol/skills` extension with `directoryRead: true`. Defaults to `/mnt/hf-skills/distribution/latest`, intended for a Hugging Face Space volume mounted from `hf://buckets/huggingface/skills`.
To expose the shared Hugging Face skills catalog from a Space, mount the bucket and keep `HF_SKILLS_DIR` pointed at its latest distribution directory:
```bash
hf spaces volumes set <org>/<space> -v hf://buckets/huggingface/skills:/mnt/hf-skills:ro
hf spaces variables add <org>/<space> -e HF_SKILLS_DIR=/mnt/hf-skills/distribution/latest
```
### Proxy tools (Streamable HTTP via CSV)
You can load proxy tool definitions at startup by setting `PROXY_TOOLS_CSV` to a **HTTPS URL** or a **local file path**.
The server fetches each MCP endpoint once on startup, runs `initialize` + `tools/list` (10s timeout), and registers any tools returned.
If a source fails or returns no tools, it is skipped (no startup failure).
**CSV format**
```
tool_name,url,response_type
papers,https://evalstate-hf-papers.hf.space/mcp,SSE
news,https://example.com/mcp,JSON
```
- `tool_name`: local tool name for single-tool upstreams; identifier for the proxy source when the upstream exposes
multiple tools.
- `url`: Streamable HTTP MCP endpoint.
- `response_type`: `SSE` (streamed response) or `JSON` (direct JSON-RPC response).
**Tool naming**
Tool naming depends on how many tools the upstream MCP endpoint returns:
- Single upstream tool: the exposed tool name is the first CSV column.
- Multiple upstream tools: the exposed tool names are the upstream tool names.
If an exposed proxy tool name collides with an already-registered tool, the proxy tool is skipped and a warning is
logged.
You can include these tool names in bouquets or mixes as needed.
Use `bouquet=proxy` or `mix=proxy` to enable all proxy tools loaded from `PROXY_TOOLS_CSV` (in addition to the base built-in tools).
