Hugging Face with LlamaIndex

How to integrate Hugging Face MCP with LlamaIndex

Introduction TL;DR What is LlamaIndex What is the Hugging Face MCP server Supported Tools & Triggers Creating MCP Server - Stand-alone vs Tool Router Step-by-step Guide Complete Code Conclusion How to build Hugging Face MCP Agent with another framework Explore Other Toolkits FAQ

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Introduction

This guide walks you through connecting Hugging Face to LlamaIndex using the Composio tool router. By the end, you'll have a working Hugging Face agent that can run text summarization on uploaded document, list all my hugging face model repositories, deploy a new model to spaces through natural language commands.

This guide will help you understand how to give your LlamaIndex agent real control over a Hugging Face account through Composio's Hugging Face MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

TL;DR

Here's what you'll learn:

Set your OpenAI and Composio API keys
Install LlamaIndex and Composio packages
Create a Composio Tool Router session for Hugging Face
Connect LlamaIndex to the Hugging Face MCP server
Build a Hugging Face-powered agent using LlamaIndex
Interact with Hugging Face through natural language

What is LlamaIndex?

LlamaIndex is a data framework for building LLM applications. It provides tools for connecting LLMs to external data sources and services through agents and tools.

Key features include:

ReAct Agent: Reasoning and acting pattern for tool-using agents
MCP Tools: Native support for Model Context Protocol
Context Management: Maintain conversation context across interactions
Async Support: Built for async/await patterns

What is the Hugging Face MCP server, and what's possible with it?

The Hugging Face MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Hugging Face account. It provides structured and secure access so your agent can perform Hugging Face operations on your behalf.

Supported Tools & Triggers

Tools

Change Discussion StatusTool to change the status of a Hugging Face repository discussion.

Check Dataset ValidityTool to check whether a specific dataset is valid on Hugging Face Hub.

Check Models Upload MethodTool to check if files should be uploaded through the Large File mechanism or directly.

Check Spaces Upload MethodTool to check if files should be uploaded through the Large File mechanism or directly to Hugging Face Spaces.

Claim Paper AuthorshipTool to claim authorship of a paper on Hugging Face.

Request Repository AccessTool to request access to a gated repository on Hugging Face Hub.

Create CollectionTool to create a new collection on Hugging Face.

Create Datasets BranchTool to create a new branch in a Hugging Face dataset repository.

Create Datasets CommitTool to create a commit in a Hugging Face dataset repository.

Check Dataset File Upload MethodTool to check if files should be uploaded via Large File Storage (LFS) or directly to a Hugging Face dataset repository.

Create Datasets TagTool to create a tag on a Hugging Face dataset repository.

Create DiscussionTool to create a new discussion on a Hugging Face repository (model, dataset, or Space).

Create Discussion CommentTool to create a new comment on a Hugging Face repository discussion.

Pin discussionTool to pin or unpin a discussion on a Hugging Face repository (model, dataset, or Space).

Create models branchTool to create a new branch in a Hugging Face model repository.

Create Models CommitTool to create a commit to a Hugging Face model repository.

Create Models TagTool to create a tag on a Hugging Face model repository.

Create Paper CommentTool to create a new comment on a Hugging Face paper.

Create Papers Comment ReplyTool to create a reply to a comment on a Hugging Face paper.

Create Papers IndexTool to index a paper from arXiv by its ID on Hugging Face.

Create RepositoryTool to create a new repository (model, dataset, or Space) on Hugging Face Hub.

Create spaces branchTool to create a new branch in a Hugging Face space repository.

Create Spaces CommitTool to create a commit in a Hugging Face Space repository.

Create or update Space secretTool to create or update a secret in a Hugging Face Space.

Create Spaces TagTool to create a tag on a Hugging Face space repository.

Create or update Space variableTool to create or update a variable in a Hugging Face Space.

Create SQL Console EmbedTool to create a SQL Console embed for querying datasets on Hugging Face.

Create WebhookTool to create a webhook on Hugging Face that triggers on repository or discussion events.

Delete dataset branchTool to delete a branch from a Hugging Face dataset repository.

Delete Dataset TagTool to delete a tag from a Hugging Face dataset.

Delete discussionTool to delete a discussion from a Hugging Face repository.

Delete network CIDR listTool to delete a network CIDR list entry from Hugging Face Inference Endpoints.

Delete notificationsTool to delete notifications from Hugging Face.

Delete Settings WebhookTool to delete a webhook from Hugging Face settings.

Delete space branchTool to delete a branch from a Hugging Face space repository.

Delete space secretTool to delete a secret from a Hugging Face space.

Delete Spaces TagTool to delete a tag from a Hugging Face space.

Delete space variableTool to delete a variable from a Hugging Face space.

Filter dataset rowsTool to filter rows in a Hugging Face dataset split based on SQL-like query conditions.

Generate Chat CompletionTool to generate a response given a list of messages in a conversational context.

Generate Text EmbeddingsTool to convert text into vector embeddings for feature extraction, semantic search, and similarity tasks.

Get Daily PapersTool to retrieve daily papers from Hugging Face.

Get Dataset Croissant MetadataTool to get Croissant metadata about a Hugging Face dataset.

Get Dataset First RowsTool to get the first 100 rows of a dataset split along with column data types and features.

Get Dataset InfoTool to get general information about a dataset including description, citation, homepage, license, and features (column schemas).

Get Dataset Repository InfoTool to retrieve detailed information about a Hugging Face dataset repository.

Get Dataset RowsTool to retrieve a slice of rows from a Hugging Face dataset split at any given location (offset).

Get Datasets CompareTool to get a comparison (diff) between two revisions of a Hugging Face dataset.

Get Dataset SizeTool to get the size of a Hugging Face dataset including number of rows and size in bytes.

Get Datasets JWTTool to generate a JWT token for accessing a Hugging Face dataset repository.

Get Datasets LeaderboardTool to retrieve evaluation results ranked by score for a dataset's leaderboard.

Get Dataset Notebook URLTool to get a Jupyter notebook URL from a Hugging Face dataset repository.

Get Datasets ResolveTool to resolve and download a file from a Hugging Face dataset repository.

Get Dataset Security ScanTool to retrieve the security scan status of a Hugging Face dataset repository.

Get Dataset Tags by TypeTool to retrieve all possible tags used for datasets on Hugging Face, grouped by tag type.

Get Dataset StatisticsTool to get comprehensive statistics about a dataset split including column statistics and data distribution information.

Get dataset repository sizeTool to get the total size of a Hugging Face dataset repository at a specific revision and path.

Get Datasets XET Read TokenTool to get a read short-lived access token for XET from Hugging Face datasets.

Get Discussion DetailsTool to get detailed information about a specific discussion or pull request on Hugging Face Hub.

Get Discussion DetailsTool to retrieve discussion details from a Hugging Face repository.

Get Available Job HardwareTool to retrieve available hardware configurations for Hugging Face Jobs with their specifications and pricing.

Get Model InformationTool to retrieve detailed information about a Hugging Face model repository.

Get Models CompareTool to compare two revisions of a Hugging Face model repository.

Get Models JWTTool to generate a JWT token for accessing a Hugging Face model repository.

Get Models NotebookTool to retrieve a Jupyter notebook URL from a Hugging Face model repository.

Get Model Security Scan StatusTool to retrieve the security scan status of a Hugging Face model repository.

Get model repository sizeTool to get the total size of a Hugging Face model repository at a specific revision and path.

Get Model XET Read TokenTool to retrieve a short-lived XET read access token for a Hugging Face model repository.

Get Model Tags By TypeTool to retrieve all possible tags used for Hugging Face models, grouped by tag type.

Get Organization AvatarTool to retrieve the avatar URL for a Hugging Face organization.

Get Organization MembersTool to retrieve a list of members for a Hugging Face organization.

Get Organization Social HandlesTool to retrieve an organization's social media handles from Hugging Face.

Get ResolveTool to resolve a file in a Hugging Face repository.

Get Resolve Cache DatasetsTool to resolve a file from cache in a Hugging Face dataset repository.

Get Resolve Cache ModelsTool to resolve and retrieve files from the Hugging Face model cache.

Get Resolve Cache SpacesTool to resolve and retrieve a file from Hugging Face Spaces cache.

Get Billing UsageTool to retrieve user billing usage for a given period from Hugging Face.

Get Jobs UsageTool to retrieve Jobs usage and billing information for the current subscription period from Hugging Face.

Get Live Billing UsageTool to retrieve live billing usage stream from Hugging Face.

Get Billing Usage V2Tool to retrieve user billing usage for a custom date range from Hugging Face.

Get MCP SettingsTool to retrieve MCP (Model Context Protocol) tools configuration for the authenticated user.

Get Settings WebhookTool to retrieve a specific webhook configuration from Hugging Face settings.

Get Space InfoTool to retrieve detailed information about a Hugging Face Space repository.

Get Spaces CompareTool to compare two revisions of a Hugging Face Space repository.

Get Spaces EventsTool to stream status updates for a Hugging Face Space using SSE protocol.

Get Spaces JWTTool to generate a JWT token for accessing a Hugging Face space repository.

Get Space MetricsTool to get live metrics for a specific Space in a streaming fashion, with SSE protocol, such as current Zero-GPU usage.

Get Spaces NotebookTool to retrieve a Jupyter notebook URL from a Hugging Face space repository.

Get Spaces ResolveTool to resolve and retrieve a file from a Hugging Face Space repository.

Get Space Security Scan StatusTool to retrieve the security scan status of a Hugging Face space repository.

Get space repository sizeTool to get the total size of a Hugging Face space repository at a specific revision and path.

Get Space XET Read TokenTool to retrieve a short-lived XET read access token for a Hugging Face Space repository.

Get Spaces XET Write TokenTool to retrieve a short-lived XET write access token for a Hugging Face space repository.

Get Trending RepositoriesTool to retrieve trending repositories from Hugging Face.

Get User AvatarTool to retrieve the avatar URL for a Hugging Face user.

Get User OverviewTool to retrieve a comprehensive overview of a Hugging Face user's profile.

Get User Social HandlesTool to retrieve a user's social media handles from Hugging Face.

Get Authenticated User InfoTool to get information about the authenticated Hugging Face user including username, email, organizations, and token details.

Handle Dataset User Access RequestTool to handle a user's access request to a gated Hugging Face dataset.

List CollectionsTool to list collections on the Hugging Face Hub.

List Dataset Parquet FilesTool to get the list of Parquet files for a dataset.

List Dataset Paths InfoTool to list detailed information about specific paths in a Hugging Face dataset repository.

List DatasetsTool to list datasets on the Hugging Face Hub.

List Dataset CommitsTool to list commits from a Hugging Face dataset repository.

List Dataset SplitsTool to get the list of subsets and splits of a dataset.

List Dataset ReferencesTool to list all references (branches, tags, converts, pull requests) in a Hugging Face dataset repository.

List datasets treeTool to list the content of a Hugging Face dataset repository tree with pagination support.

List Dataset Access RequestsTool to list access requests for a gated Hugging Face dataset repository.

List DiscussionsTool to list discussions for a Hugging Face repository.

List Repository DiscussionsTool to list discussions and pull requests for a Hugging Face repository.

List Available DocumentationTool to retrieve the list of available documentation from Hugging Face.

List Inference EndpointsTool to list Hugging Face Inference Endpoints for a specific user or organization.

List models on Hugging Face HubTool to list models on the Hugging Face Hub with filtering options.

List Model CommitsTool to list commits from a Hugging Face model repository.

List Model Paths InfoTool to list detailed information about specific paths in a Hugging Face model repository.

List Model ReferencesTool to list all references (branches, tags, converts, and optionally pull requests) in a Hugging Face model repository.

List model repository treeTool to list the contents of a Hugging Face model repository tree at a specific revision and path, with pagination support.

List NotificationsTool to list notifications for the authenticated Hugging Face user.

List Repository CommitsTool to list commits for a Hugging Face repository.

List repository filesTool to get the file tree of a Hugging Face repository with pagination support.

List WebhooksTool to list all webhooks configured in Hugging Face settings.

List Spaces on Hugging Face HubTool to list Spaces on the Hugging Face Hub with filtering options.

List Space CommitsTool to list commits from a Hugging Face Space repository.

List Available Space HardwareTool to retrieve available hardware configurations for Hugging Face Spaces with their specifications and pricing.

List Space LFS FilesTool to list LFS (Large File Storage) files from a Hugging Face Space repository.

List Space Paths InfoTool to list detailed information about specific paths in a Hugging Face space repository.

List Space ReferencesTool to list all references (branches, tags, converts, pull requests) in a Hugging Face space repository.

List spaces treeTool to list the content of a Hugging Face space repository tree with pagination support.

List Cloud Provider VendorsTool to list available cloud provider vendors for Hugging Face Inference Endpoints.

Search datasetTool to search text in a dataset split on Hugging Face.

Search DocumentationTool to search Hugging Face documentation across all products and libraries.

Search PapersTool to perform hybrid semantic/full-text search on papers in Hugging Face.

Squash Dataset CommitsTool to squash all commits in a dataset ref into a single commit with the given message.

Squash Spaces CommitsTool to squash all commits in a space ref into a single commit with the given message.

Update Dataset SettingsTool to update settings for a Hugging Face dataset repository.

Update Discussion TitleTool to change the title of an existing discussion on a Hugging Face repository (model, dataset, or Space).

Update Model Repository SettingsTool to update settings for a Hugging Face model repository.

Update Notification SettingsTool to update notification settings for the authenticated Hugging Face user.

Update Watch SettingsTool to update watch settings for your Hugging Face account.

Update WebhookTool to update an existing webhook in Hugging Face settings.

Update Spaces Repository SettingsTool to update settings for a Hugging Face Spaces repository.

Update SQL Console EmbedTool to update an existing SQL console embed for a Hugging Face dataset.

Enable or Disable WebhookTool to enable or disable a webhook on Hugging Face.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router follows a three-phase workflow:

Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before you begin, make sure you have:

Python 3.8/Node 16 or higher installed
A Composio account with the API key
An OpenAI API key
A Hugging Face account and project
Basic familiarity with async Python/Typescript

Getting API Keys for OpenAI, Composio, and Hugging Face

OpenAI API key (OPENAI_API_KEY)

Go to the OpenAI dashboard
Create an API key if you don't have one
Assign it to OPENAI_API_KEY in .env

Composio API key and user ID

Log into the Composio dashboard
Copy your API key from Settings
- Use this as COMPOSIO_API_KEY
Pick a stable user identifier (email or ID)
- Use this as COMPOSIO_USER_ID

Installing dependencies

pip install composio-llamaindex llama-index llama-index-llms-openai llama-index-tools-mcp python-dotenv

Create a new Python project and install the necessary dependencies:

composio-llamaindex: Composio's LlamaIndex integration
llama-index: Core LlamaIndex framework
llama-index-llms-openai: OpenAI LLM integration
llama-index-tools-mcp: MCP client for LlamaIndex
python-dotenv: Environment variable management

Set environment variables

bash

OPENAI_API_KEY=your-openai-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_USER_ID=your-user-id

Create a .env file in your project root:

These credentials will be used to:

Authenticate with OpenAI's GPT-5 model
Connect to Composio's Tool Router
Identify your Composio user session for Hugging Face access

Import modules

import asyncio
import os
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

Create a new file called hugging face_llamaindex_agent.py and import the required modules:

Key imports:

asyncio: For async/await support
Composio: Main client for Composio services
LlamaIndexProvider: Adapts Composio tools for LlamaIndex
ReActAgent: LlamaIndex's reasoning and action agent
BasicMCPClient: Connects to MCP endpoints
McpToolSpec: Converts MCP tools to LlamaIndex format

Load environment variables and initialize Composio

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set in the environment")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment")

What's happening:

This ensures missing credentials cause early, clear errors before the agent attempts to initialise.

Create a Tool Router session and build the agent function

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["hugging_face"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")

    description = "An agent that uses Composio Tool Router MCP tools to perform Hugging Face actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Hugging Face actions.
    """
    return ReActAgent(tools=tools, llm=llm, description=description, system_prompt=system_prompt, verbose=True)

What's happening here:

We create a Composio client using your API key and configure it with the LlamaIndex provider
We then create a tool router MCP session for your user, specifying the toolkits we want to use (in this case, hugging face)
The session returns an MCP HTTP endpoint URL that acts as a gateway to all your configured tools
LlamaIndex will connect to this endpoint to dynamically discover and use the available Hugging Face tools.
The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.

Create an interactive chat loop

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

What's happening here:

We're creating a direct terminal interface to chat with your Hugging Face database
The LLM's responses are streamed to the CLI for faster interaction.
The agent uses context to maintain conversation history
You can type 'quit' or 'exit' to stop the chat loop gracefully
Agent responses and any errors are displayed in a clear, readable format

Define the main entry point

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")

What's happening here:

We're orchestrating the entire application flow
The agent gets built with proper error handling
Then we kick off the interactive chat loop so you can start talking to Hugging Face

Run the agent

npx ts-node llamaindex-agent.ts

When prompted, authenticate and authorise your agent with Hugging Face, then start asking questions.

Complete Code

Here's the complete code to get you started with Hugging Face and LlamaIndex:

import asyncio
import os
import signal
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["hugging_face"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")
    description = "An agent that uses Composio Tool Router MCP tools to perform Hugging Face actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Hugging Face actions.
    """
    return ReActAgent(
        tools=tools,
        llm=llm,
        description=description,
        system_prompt=system_prompt,
        verbose=True,
    );

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")

Conclusion

You've successfully connected Hugging Face to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways:

Tool Router dynamically exposes Hugging Face tools through an MCP endpoint
LlamaIndex's ReActAgent handles reasoning and orchestration; Composio handles integrations
The agent becomes more capable without increasing prompt size
Async Python provides clean, efficient execution of agent workflows

You can easily extend this to other toolkits like Gmail, Notion, Stripe, GitHub, and more by adding them to the toolkits parameter.

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FAQ

What are the differences in Tool Router MCP and Hugging Face MCP?

With a standalone Hugging Face MCP server, the agents and LLMs can only access a fixed set of Hugging Face tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Hugging Face and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with LlamaIndex?

Yes, you can. LlamaIndex fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Hugging Face tools.

Can I manage the permissions and scopes for Hugging Face while using Tool Router?

Yes, absolutely. You can configure which Hugging Face scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Hugging Face data and credentials are handled as safely as possible.

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Karan skipped his own birthday party to fix our critical issue. It was 10 pm and he diverted his Waymo to help us instead. This really sets the bar, shows you the commitment you need to have when users rely on your software.

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A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it has immensely helped in keeping new users on the platform.

Nirman DaveCEO, Zams

We chose Composio over Pipedream because it delivered depth where it mattered. It supported niche tools and tricky edge cases that other platforms simply ignored. Giving us confidence to scale without compromising.

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Before partnering with Composio, adding tool integrations was a slow, resource-intensive process. Each integration could take weeks or months of engineering time, and maintaining them meant constantly keeping up with API changes.

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With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Harsha GaddipatiCo-founder, Slashy

Abhi AryaCo-founder, Opennote

Nirman DaveCEO, Zams

Ryan YuFounder, Extra Thursday

Tomisin JenrolaFounder & CEO, SwarmZero

Jerome LeclancheCo-Founder, Ingram Technologies

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Harsha GaddipatiCo-founder, Slashy

Abhi AryaCo-founder, Opennote

Nirman DaveCEO, Zams

Ryan YuFounder, Extra Thursday

Tomisin JenrolaFounder & CEO, SwarmZero

Jerome LeclancheCo-Founder, Ingram Technologies

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

How to integrate Hugging Face MCP with LlamaIndex

Table of Contents

Connect Hugging Face without Auth hassles

Introduction

TL;DR

What is LlamaIndex?

What is the Hugging Face MCP server, and what's possible with it?

Supported Tools & Triggers

What is the Composio tool router, and how does it fit here?

What is Tool Router?

How the Tool Router works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI, Composio, and Hugging Face

Installing dependencies

Set environment variables

Import modules

Load environment variables and initialize Composio

Create a Tool Router session and build the agent function

Create an interactive chat loop

Define the main entry point

Run the agent

Complete Code

Conclusion

How to build Hugging Face MCP Agent with another framework

OpenAI Agents SDK

Claude Agent SDK

Google ADK

LangChain

Vercel AI SDK

Mastra AI

CrewAI

Claude Code

Codex

Explore Other Toolkits

Ai ml api

Aivoov

All images ai

FAQ

What are the differences in Tool Router MCP and Hugging Face MCP?

Can I use Tool Router MCP with LlamaIndex?

Can I manage the permissions and scopes for Hugging Face while using Tool Router?

How safe is my data with Composio Tool Router?

Used by agents from

Never worry about agent reliability