How do I get a Hugging Face Access Token?

Log in to [**Hugging Face**](https://huggingface.co), go to **Settings > Access Tokens**, click **New token**, give it a name and select scopes (read is sufficient for browsing, write if you need to create repos). Copy the token immediately — it starts with `hf_`.

Can I search models by task type (e.g. text-generation)?

Yes! Use `list_models` with a search query. While the search endpoint doesn't directly filter by pipeline_tag, you can search by task name (e.g. search='text-generation') and then use `get_model` or `get_model_tags` to verify the pipeline_tag of specific models.

Can I see what files are in a model repository?

Yes! Use `list_model_files` with the model ID (e.g. 'google-bert/bert-base-uncased') to see the complete file tree including model weights (.safetensors, .bin), config files, tokenizer files and README. Optionally set a path to browse a specific subdirectory like 'onnx' or 'pytorch'.

Can I create discussions on model pages?

Yes! Use `create_discussion` with the repo type ('model', 'dataset' or 'space'), the repo ID and a title. This creates a new discussion thread on the repository. You can use `list_model_discussions` first to check existing threads before creating a new one.

Hugging Face MCP Server for Vercel AI SDK 13 tools — connect in under 2 minutes

Name: Hugging Face
Availability: InStock
Author: Vinkius

ai-sdk.dev/docs/ai-sdk-core/mcp

Built by Vinkius GDPR 13 Tools SDK

The Vercel AI SDK is the TypeScript toolkit for building AI-powered applications. Connect Hugging Face through the Vinkius and every tool is available as a typed function — ready for React Server Components, API routes, or any Node.js backend.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

typescript

import { createMCPClient } from "@ai-sdk/mcp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

async function main() {
  const mcpClient = await createMCPClient({
    transport: {
      type: "http",
      // Your Vinkius token — get it at cloud.vinkius.com
      url: "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    },
  });

  try {
    const tools = await mcpClient.tools();
    const { text } = await generateText({
      model: openai("gpt-4o"),
      tools,
      prompt: "Using Hugging Face, list all available capabilities.",
    });
    console.log(text);
  } finally {
    await mcpClient.close();
  }
}

main();

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Hugging Face MCP Server

Connect your Hugging Face account to any AI agent and explore the world's largest AI model hub through natural conversation.

The Vercel AI SDK gives every Hugging Face tool full TypeScript type inference, IDE autocomplete, and compile-time error checking. Connect 13 tools through the Vinkius and stream results progressively to React, Svelte, or Vue components — works on Edge Functions, Cloudflare Workers, and any Node.js runtime.

What you can do

Model Discovery — Search and browse thousands of models by name, task type, framework and author
Model Inspection — View model metadata including pipeline task, tags, download counts, likes and file structure
Dataset Exploration — Find and inspect datasets with their descriptions, sizes and file trees
Spaces Gallery — Browse ML demo apps (Gradio, Streamlit, Docker) and check their runtime status
Collections — View curated collections of models, datasets and spaces organized by topic
Community Discussions — Read model discussion threads for bug reports, feature requests and usage tips
File Tree Browsing — List repository files (model weights, configs, tokenizers) without downloading

The Hugging Face MCP Server exposes 13 tools through the Vinkius. Connect it to Vercel AI SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Hugging Face to Vercel AI SDK via MCP

Follow these steps to integrate the Hugging Face MCP Server with Vercel AI SDK.

Install dependencies

Run npm install @ai-sdk/mcp ai @ai-sdk/openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the script

Save to agent.ts and run with npx tsx agent.ts

Explore tools

The SDK discovers 13 tools from Hugging Face and passes them to the LLM

Why Use Vercel AI SDK with the Hugging Face MCP Server

Vercel AI SDK provides unique advantages when paired with Hugging Face through the Model Context Protocol.

TypeScript-first: every MCP tool gets full type inference, IDE autocomplete, and compile-time error checking out of the box

Framework-agnostic core works with Next.js, Nuxt, SvelteKit, or any Node.js runtime — same Hugging Face integration everywhere

Built-in streaming UI primitives let you display Hugging Face tool results progressively in React, Svelte, or Vue components

Edge-compatible: the AI SDK runs on Vercel Edge Functions, Cloudflare Workers, and other edge runtimes for minimal latency

Hugging Face + Vercel AI SDK Use Cases

Practical scenarios where Vercel AI SDK combined with the Hugging Face MCP Server delivers measurable value.

AI-powered web apps: build dashboards that query Hugging Face in real-time and stream results to the UI with zero loading states

API backends: create serverless endpoints that orchestrate Hugging Face tools and return structured JSON responses to any frontend

Chatbots with tool use: embed Hugging Face capabilities into conversational interfaces with streaming responses and tool call visibility

Internal tools: build admin panels where team members interact with Hugging Face through natural language queries

Hugging Face MCP Tools for Vercel AI SDK (13)

These 13 tools become available when you connect Hugging Face to Vercel AI SDK via MCP:

create_discussion

Requires the repo type (model, dataset or space), the repo ID in "author/name" format and the discussion title. Returns the created discussion with its ID, title and URL. Create a new discussion on a Hugging Face repo

get_collection

Provide the collection slug. Get details for a specific Hugging Face collection

get_model

Provide the model ID in "author/name" format (e.g. "google-bert/bert-base-uncased"). Get details for a specific Hugging Face model

get_model_tags

Tags include framework (pytorch, tensorflow), license, dataset, language and task-specific labels. The pipeline_tag indicates the model's primary task (e.g. "text-generation", "image-classification", "translation"). Get tags and pipeline info for a Hugging Face model

get_space

Provide the space ID in "author/name" format. Get details for a specific Hugging Face Space

get_user

Returns user name, avatar, organizations, auth type, plan and access tokens metadata. Use this to verify your token is working correctly. Get the authenticated Hugging Face user

list_collections

Optionally filter by author and limit. Returns collection slug, title, description, author, item count and likes count. List collections on Hugging Face Hub

list_dataset_files

Returns filenames (e.g. "train.parquet", "test.parquet", "data/", "README.md"). Optionally set a subdirectory path. Useful for understanding dataset structure before downloading. List files in a Hugging Face dataset repository

list_datasets

Optionally filter by search term, author and limit. Returns dataset ID, author, description, download count, likes count and creation date. List datasets on Hugging Face Hub

list_model_discussions

Returns discussion title, author, creation date, number of comments and whether it is resolved. Use this to review community feedback, bug reports and feature requests for a model. List discussions for a Hugging Face model

list_model_files

Returns filenames, file sizes and paths (e.g. "model.safetensors", "tokenizer.json", "config.json", "README.md"). Optionally set a subdirectory path to list files within a specific folder. Useful for inspecting model artifacts and understanding the repository structure. List files in a Hugging Face model repository

list_models

Optionally filter by search term (free-text across model cards), author (organization or username) and limit the number of results. Returns model ID, author, pipeline task tag, download count, likes count and creation date. List models on Hugging Face Hub

list_spaces

Optionally filter by search term, author and limit. Returns space ID, title, author, SDK (Gradio, Streamlit, Docker), likes count and creation date. List Spaces on Hugging Face Hub

Example Prompts for Hugging Face in Vercel AI SDK

Ready-to-use prompts you can give your Vercel AI SDK agent to start working with Hugging Face immediately.

"Find popular text generation models with over 1000 likes."

"Show me what files are in the bert-base-uncased model."

"What discussions are happening on the Llama-3 model page?"

Troubleshooting Hugging Face MCP Server with Vercel AI SDK

Common issues when connecting Hugging Face to Vercel AI SDK through the Vinkius, and how to resolve them.

createMCPClient is not a function

Install: npm install @ai-sdk/mcp

Hugging Face + Vercel AI SDK FAQ

Common questions about integrating Hugging Face MCP Server with Vercel AI SDK.

How does the Vercel AI SDK connect to MCP servers?

Import createMCPClient from @ai-sdk/mcp and pass the server URL. The SDK discovers all tools and provides typed TypeScript interfaces for each one.