How to integrate Hugging Face MCP with Vercel AI SDK v6

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Introduction

This guide walks you through connecting Hugging Face to Vercel AI SDK v6 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 Vercel AI SDK 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:
  • How to set up and configure a Vercel AI SDK agent with Hugging Face integration
  • Using Composio's Tool Router to dynamically load and access Hugging Face tools
  • Creating an MCP client connection using HTTP transport
  • Building an interactive CLI chat interface with conversation history management
  • Handling tool calls and results within the Vercel AI SDK framework

What is Vercel AI SDK?

The Vercel AI SDK is a TypeScript library for building AI-powered applications. It provides tools for creating agents that can use external services and maintain conversation state.

Key features include:

  • streamText: Core function for streaming responses with real-time tool support
  • MCP Client: Built-in support for Model Context Protocol via @ai-sdk/mcp
  • Step Counting: Control multi-step tool execution with stopWhen: stepCountIs()
  • OpenAI Provider: Native integration with OpenAI models

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:

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

Step-by-step Guide

Prerequisites

Before you begin, make sure you have:
  • Node.js and npm installed
  • A Composio account with API key
  • An OpenAI API key

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install required dependencies

bash
npm install @ai-sdk/openai @ai-sdk/mcp @composio/core ai dotenv

First, install the necessary packages for your project.

What you're installing:

  • @ai-sdk/openai: Vercel AI SDK's OpenAI provider
  • @ai-sdk/mcp: MCP client for Vercel AI SDK
  • @composio/core: Composio SDK for tool integration
  • ai: Core Vercel AI SDK
  • dotenv: Environment variable management

Set up environment variables

bash
OPENAI_API_KEY=your_openai_api_key_here
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here

Create a .env file in your project root.

What's needed:

  • OPENAI_API_KEY: Your OpenAI API key for GPT model access
  • COMPOSIO_API_KEY: Your Composio API key for tool access
  • COMPOSIO_USER_ID: A unique identifier for the user session

Import required modules and validate environment

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Composio } from "@composio/core";
import * as readline from "readline";
import { streamText, type ModelMessage, stepCountIs } from "ai";
import { createMCPClient } from "@ai-sdk/mcp";

const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey,
});
What's happening:
  • We're importing all necessary libraries including Vercel AI SDK's OpenAI provider and Composio
  • The dotenv/config import automatically loads environment variables
  • The MCP client import enables connection to Composio's tool server

Create Tool Router session and initialize MCP client

typescript
async function main() {
  // Create a tool router session for the user
  const session = await composio.create(composioUserID!, {
    toolkits: ["hugging_face"],
  });

  const mcpUrl = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Hugging Face tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned mcp object contains the URL and authentication headers needed to connect to the MCP server
  • This session provides access to all Hugging Face-related tools through the MCP protocol

Connect to MCP server and retrieve tools

typescript
const mcpClient = await createMCPClient({
  transport: {
    type: "http",
    url: mcpUrl,
    headers: session.mcp.headers, // Authentication headers for the Composio MCP server
  },
});

const tools = await mcpClient.tools();
What's happening:
  • We're creating an MCP client that connects to our Composio Tool Router session via HTTP
  • The mcp.url provides the endpoint, and mcp.headers contains authentication credentials
  • The type: "http" is important - Composio requires HTTP transport
  • tools() retrieves all available Hugging Face tools that the agent can use

Initialize conversation and CLI interface

typescript
let messages: ModelMessage[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log(
  "Ask any questions related to hugging_face, like summarize my last 5 emails, send an email, etc... :)))\n",
);

const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: "> ",
});

rl.prompt();
What's happening:
  • We initialize an empty messages array to maintain conversation history
  • A readline interface is created to accept user input from the command line
  • Instructions are displayed to guide the user on how to interact with the agent

Handle user input and stream responses with real-time tool feedback

typescript
rl.on("line", async (userInput: string) => {
  const trimmedInput = userInput.trim();

  if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
    console.log("\nGoodbye!");
    rl.close();
    process.exit(0);
  }

  if (!trimmedInput) {
    rl.prompt();
    return;
  }

  messages.push({ role: "user", content: trimmedInput });
  console.log("\nAgent is thinking...\n");

  try {
    const stream = streamText({
      model: openai("gpt-5"),
      messages,
      tools,
      toolChoice: "auto",
      stopWhen: stepCountIs(10),
      onStepFinish: (step) => {
        for (const toolCall of step.toolCalls) {
          console.log(`[Using tool: ${toolCall.toolName}]`);
          }
          if (step.toolCalls.length > 0) {
            console.log(""); // Add space after tool calls
          }
        },
      });

      for await (const chunk of stream.textStream) {
        process.stdout.write(chunk);
      }

      console.log("\n\n---\n");

      // Get final result for message history
      const response = await stream.response;
      if (response?.messages?.length) {
        messages.push(...response.messages);
      }
    } catch (error) {
      console.error("\nAn error occurred while talking to the agent:");
      console.error(error);
      console.log(
        "\nYou can try again or restart the app if it keeps happening.\n",
      );
    } finally {
      rl.prompt();
    }
  });

  rl.on("close", async () => {
    await mcpClient.close();
    console.log("\nđź‘‹ Session ended.");
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});
What's happening:
  • We use streamText instead of generateText to stream responses in real-time
  • toolChoice: "auto" allows the model to decide when to use Hugging Face tools
  • stopWhen: stepCountIs(10) allows up to 10 steps for complex multi-tool operations
  • onStepFinish callback displays which tools are being used in real-time
  • We iterate through the text stream to create a typewriter effect as the agent responds
  • The complete response is added to conversation history to maintain context
  • Errors are caught and displayed with helpful retry suggestions

Complete Code

Here's the complete code to get you started with Hugging Face and Vercel AI SDK:

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Composio } from "@composio/core";
import * as readline from "readline";
import { streamText, type ModelMessage, stepCountIs } from "ai";
import { createMCPClient } from "@ai-sdk/mcp";

const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey,
});

async function main() {
  // Create a tool router session for the user
  const session = await composio.create(composioUserID!, {
    toolkits: ["hugging_face"],
  });

  const mcpUrl = session.mcp.url;

  const mcpClient = await createMCPClient({
    transport: {
      type: "http",
      url: mcpUrl,
      headers: session.mcp.headers, // Authentication headers for the Composio MCP server
    },
  });

  const tools = await mcpClient.tools();

  let messages: ModelMessage[] = [];

  console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
  console.log(
    "Ask any questions related to hugging_face, like summarize my last 5 emails, send an email, etc... :)))\n",
  );

  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: "> ",
  });

  rl.prompt();

  rl.on("line", async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
      console.log("\nGoodbye!");
      rl.close();
      process.exit(0);
    }

    if (!trimmedInput) {
      rl.prompt();
      return;
    }

    messages.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    try {
      const stream = streamText({
        model: openai("gpt-5"),
        messages,
        tools,
        toolChoice: "auto",
        stopWhen: stepCountIs(10),
        onStepFinish: (step) => {
          for (const toolCall of step.toolCalls) {
            console.log(`[Using tool: ${toolCall.toolName}]`);
          }
          if (step.toolCalls.length > 0) {
            console.log(""); // Add space after tool calls
          }
        },
      });

      for await (const chunk of stream.textStream) {
        process.stdout.write(chunk);
      }

      console.log("\n\n---\n");

      // Get final result for message history
      const response = await stream.response;
      if (response?.messages?.length) {
        messages.push(...response.messages);
      }
    } catch (error) {
      console.error("\nAn error occurred while talking to the agent:");
      console.error(error);
      console.log(
        "\nYou can try again or restart the app if it keeps happening.\n",
      );
    } finally {
      rl.prompt();
    }
  });

  rl.on("close", async () => {
    await mcpClient.close();
    console.log("\nđź‘‹ Session ended.");
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});

Conclusion

You've successfully built a Hugging Face agent using the Vercel AI SDK with streaming capabilities! This implementation provides a powerful foundation for building AI applications with natural language interfaces and real-time feedback.

Key features of this implementation:

  • Real-time streaming responses for a better user experience with typewriter effect
  • Live tool execution feedback showing which tools are being used as the agent works
  • Dynamic tool loading through Composio's Tool Router with secure authentication
  • Multi-step tool execution with configurable step limits (up to 10 steps)
  • Comprehensive error handling for robust agent execution
  • Conversation history maintenance for context-aware responses

You can extend this further by adding custom error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

How to build Hugging Face MCP Agent with another framework

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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 Vercel AI SDK v6?

Yes, you can. Vercel AI SDK v6 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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