How to integrate Control d MCP with Vercel AI SDK

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Introduction

This guide walks you through connecting Control d to Vercel AI SDK using the Composio tool router. By the end, you'll have a working Control d agent that can list all devices connected to my account, remove a device by its id, show known access ips for my network, delete a custom dns rule from a profile through natural language commands.

This guide will help you understand how to give your Vercel AI SDK agent real control over a Control d account through Composio's Control d 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 Control d integration
  • Using Composio's Tool Router to dynamically load and access Control d 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
  • Step Counting: Control multi-step tool execution
  • OpenAI Provider: Native integration with OpenAI models

What is the Control d MCP server, and what's possible with it?

The Control d MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Control d account. It provides structured and secure access to your DNS filtering and device management environment, so your agent can perform actions like managing devices, enforcing policies, retrieving analytics, and monitoring network access on your behalf.

  • Device inventory management: Easily list all devices on your account or remove specific devices by their identifier for streamlined device control.
  • Profile and rule administration: Direct your agent to delete profiles, custom rules, or schedules—helping you maintain and enforce up-to-date network policies.
  • Network access monitoring: Retrieve a list of known access IPs to keep tabs on which endpoints are connecting to your network infrastructure.
  • Analytics endpoints discovery: Quickly fetch available analytics storage regions and endpoints so you can integrate and analyze DNS traffic data efficiently.
  • Organization details access: Have the agent fetch and present your organization's account details for easy reference and auditing.

Supported Tools & Triggers

Tools
Delete Device by IDTool to delete a Control-D device.
Delete Profile by IDTool to delete a profile.
Delete Profile Rule by HostnameTool to delete a specific custom rule by hostname from a profile.
Delete Profile Rule by Rule IDTool to delete a specific custom rule by its ID within a profile.
Delete Profile Rule in FolderTool to delete a specific custom rule within a folder.
Delete Profile ScheduleTool to delete a specific schedule within a profile.
List Known Access IPsTool to list known IPs associated with the account.
Get Analytics EndpointsTool to list analytics storage regions and their endpoints.
Get DevicesTool to list all devices associated with the account.
Get Organization DetailsTool to view the authenticated organization's details.
Get ProfilesTool to list all profiles associated with the authenticated account.
Get Profile OptionsTool to get all available profile options.
Get Profile by IDTool to retrieve details of a specific profile by its ID.
Get Profile AnalyticsTool to retrieve analytics data for a specific profile.
Get Profile Analytics LogsTool to list analytics log entries for a given profile.
Get Analytics Log EntryTool to retrieve a specific analytics log entry by its ID.
Get Profile Analytics SummaryTool to fetch a summary of analytics data for a given profile.
Get Profile Analytics Top DomainsTool to fetch top domains accessed within a specific profile.
Get Profile Top ServicesTool to fetch top services accessed within a profile.
Get Profile FiltersTool to list native filters associated with a specific profile.
List External Filters for ProfileTool to list third-party filters for a specific profile.
Get Profile FoldersTool to list rule folders (groups) within a profile.
List Custom Rules for ProfileTool to retrieve custom rules associated with a specific profile.
List Custom Rules in FolderTool to retrieve custom rules in a specific folder of a profile.
Get Custom Rule by Rule IDTool to retrieve details of a specific custom rule by its ID.
Get Specific Rule in FolderTool to retrieve a specific rule within a folder by its ID.
Get Profile SchedulesTool to list schedules associated with a specific profile.
Get Profile ScheduleTool to retrieve a specific schedule by its ID within a profile.
Get Profile ServicesTool to list services associated with a specific profile.
Get Service CategoriesTool to list all service categories.
List Services by CategoryTool to list all services within a specific category.
Get UsersTool to retrieve user account data.
Create DeviceTool to create a new device.
Create ProfileTool to create a new blank profile or clone an existing one.
Create Custom Rules for ProfileTool to create custom rules for a profile.
Create Custom Rules in Profile FolderTool to create custom rules within a specific folder for a profile.
Create Profile ScheduleTool to create a new schedule within a specific profile.
Modify DeviceTool to modify an existing device.
Modify OrganizationTool to modify organization settings and limits.
Update Profile by IDTool to modify an existing profile by ID.
Bulk Update Profile FiltersTool to bulk update filters on a specific profile.
Update External Filters for ProfileTool to update external filters for a specific profile.
Modify Profile FilterTool to modify the enabled state of a specific filter on a profile.
Modify Custom Rules for ProfileTool to modify existing custom rule(s) for a profile.
Update Custom Rule by Rule IDTool to update an existing custom rule by its ID.
Move Profile Rule to FolderTool to move a specific custom rule into a different folder.
Update Profile ScheduleTool to update a specific schedule within a profile.
Modify Service for ProfileTool to modify a specific service rule for a profile.

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 { experimental_createMCPClient as 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: ["control_d"],
  });

  const mcpUrl = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Control d 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 Control d-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 Control d 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 control_d, 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("\n👋 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 Control d 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 Control d 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 { experimental_createMCPClient as 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: ["control_d"],
  });

  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 control_d, 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("\n👋 Session ended.");
    process.exit(0);
  });
}

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

Conclusion

You've successfully built a Control d 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 Control d MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Control d MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Control d MCP with Claude Agent SDK

Google ADK

Google ADK

Use Control d MCP with Google ADK

LangChain

LangChain

Use Control d MCP with LangChain

Mastra AI

Mastra AI

Use Control d MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Control d MCP with LlamaIndex

CrewAI

CrewAI

Use Control d MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Control d MCP?

With a standalone Control d MCP server, the agents and LLMs can only access a fixed set of Control d tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Control d 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?

Yes, you can. Vercel AI SDK 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 Control d tools.

Can I manage the permissions and scopes for Control d while using Tool Router?

Yes, absolutely. You can configure which Control d 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 Control d data and credentials are handled as safely as possible.

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