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 CrewAI 13 tools — connect in under 2 minutes

Name: Hugging Face
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 13 Tools Framework

Connect your CrewAI agents to Hugging Face through Vinkius, pass the Edge URL in the `mcps` parameter and every Hugging Face tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Hugging Face Specialist",
    goal="Help users interact with Hugging Face effectively",
    backstory=(
        "You are an expert at leveraging Hugging Face tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Hugging Face "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 13 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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.

When paired with CrewAI, Hugging Face becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Hugging Face tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

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

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 13 tools from Hugging Face

Why Use CrewAI with the Hugging Face MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Hugging Face through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Hugging Face + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Hugging Face for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Hugging Face, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Hugging Face tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Hugging Face against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Hugging Face MCP Tools for CrewAI (13)

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

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

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Hugging Face + CrewAI FAQ

Common questions about integrating Hugging Face MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.