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 AutoGen 13 tools — connect in under 2 minutes

Name: Hugging Face
Availability: InStock
Author: Vinkius

microsoft.github.io/autogen/docs/tools/mcp-tools

Built by Vinkius GDPR 13 Tools Framework

Microsoft AutoGen enables multi-agent conversations where agents negotiate, delegate, and execute tasks collaboratively. Add Hugging Face as an MCP tool provider through Vinkius and every agent in the group can access live data and take action.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.tools.mcp import McpWorkbench

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with McpWorkbench(
        server_params={"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"},
        transport="streamable_http",
    ) as workbench:
        tools = await workbench.list_tools()
        agent = AssistantAgent(
            name="hugging_face_agent",
            tools=tools,
            system_message=(
                "You help users with Hugging Face. "
                "13 tools available."
            ),
        )
        print(f"Agent ready with {len(tools)} tools")

asyncio.run(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.

AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use Hugging Face tools. Connect 13 tools through Vinkius and assign role-based access. a data analyst queries while a reviewer validates, with optional human-in-the-loop approval for sensitive operations.

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 AutoGen 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 AutoGen via MCP

Follow these steps to integrate the Hugging Face MCP Server with AutoGen.

Install AutoGen

Run pip install "autogen-ext[mcp]"

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Integrate into workflow

Use the agent in your AutoGen multi-agent orchestration

Explore tools

The workbench discovers 13 tools from Hugging Face automatically

Why Use AutoGen with the Hugging Face MCP Server

AutoGen provides unique advantages when paired with Hugging Face through the Model Context Protocol.

Multi-agent conversations: multiple AutoGen agents discuss, delegate, and collaboratively use Hugging Face tools to solve complex tasks

Role-based architecture lets you assign Hugging Face tool access to specific agents. a data analyst queries while a reviewer validates

Human-in-the-loop support: agents can pause for human approval before executing sensitive Hugging Face tool calls

Code execution sandbox: AutoGen agents can write and run code that processes Hugging Face tool responses in an isolated environment

Hugging Face + AutoGen Use Cases

Practical scenarios where AutoGen combined with the Hugging Face MCP Server delivers measurable value.

Collaborative analysis: one agent queries Hugging Face while another validates results and a third generates the final report

Automated review pipelines: a researcher agent fetches data from Hugging Face, a critic agent evaluates quality, and a writer produces the output

Interactive planning: agents negotiate task allocation using Hugging Face data to make informed decisions about resource distribution

Code generation with live data: an AutoGen coder agent writes scripts that process Hugging Face responses in a sandboxed execution environment

Hugging Face MCP Tools for AutoGen (13)

These 13 tools become available when you connect Hugging Face to AutoGen 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 AutoGen

Ready-to-use prompts you can give your AutoGen 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 AutoGen

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

McpWorkbench not found

Install: pip install "autogen-ext[mcp]"

Hugging Face + AutoGen FAQ

Common questions about integrating Hugging Face MCP Server with AutoGen.

How does AutoGen connect to MCP servers?

Create an MCP tool adapter and assign it to one or more agents in the group chat. AutoGen agents can then call Hugging Face tools during their conversation turns.

Can different agents have different MCP tool access?

Yes. AutoGen's role-based architecture lets you assign specific MCP tools to specific agents, so a querying agent has different capabilities than a reviewing agent.