How to integrate Blooio MCP with Vercel AI SDK v6

This guide walks you through connecting Blooio to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Blooio agent that can review your chats, create contacts, tag contacts, manage groups, and check poll results through natural language commands. This guide will help you understand how to give your Vercel AI SDK agent real control over a Blooio account through Composio's Blooio MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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26 Tools

Introduction

This guide walks you through connecting Blooio to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Blooio agent that can review your chats, create contacts, tag contacts, manage groups, and check poll results through natural language commands.

This guide will help you understand how to give your Vercel AI SDK agent real control over a Blooio account through Composio's Blooio MCP server.

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

Also integrate Blooio with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • How to set up and configure a Vercel AI SDK agent with Blooio integration
  • Using Composio's Tool Router to dynamically load and access Blooio 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 Blooio MCP server, and what's possible with it?

The Blooio MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Blooio account. It provides structured and secure access to your messages, chats, contacts, groups, phone numbers, and account insights, so your agent can send messages, manage conversations, organize contacts, coordinate groups, and review account activity on your behalf.

  • Messaging and interactive polls: Have your agent send text, attachments, replies, or native iMessage polls, check delivery details, and review poll results.
  • Chat and reaction management: Let the agent find conversations, review message history, mark chats as read, and add or remove reactions from messages.
  • Contact organization: Direct your agent to create, find, update, or delete contacts, manage their tags, and check whether they can receive iMessage, SMS, or FaceTime.
  • Messaging group coordination: Instruct your agent to create, find, rename, or delete messaging groups and review their members.
  • Account and sender oversight: Have your agent review your organization, assigned devices, message usage, available sender numbers, and fleet risk summary.

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

What is Composio SDK?

Composio's Composio SDK 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 Composio SDK

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

How the Composio SDK works

The Composio SDK 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

Step by step09 STEPS
1

Prerequisites

Before you begin, make sure you have:
  • Node.js and npm installed
  • A Composio account with API key
  • An OpenAI API key
2

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.
3

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
4

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
5

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
6

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: ["blooio"],
  });

  const mcpUrl = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Blooio 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 Blooio-related tools through the MCP protocol
7

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 Blooio tools that the agent can use
8

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 blooio, 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
9

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 Blooio 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 Blooio 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: ["blooio"],
  });

  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 blooio, 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 Blooio 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.
TOOLS

Supported Tools

Every Blooio action and event your agent gets out of the box.

Add Contact Tags

Add or reactivate one or more free-form tags on a contact.

Create Contact

Create a contact from an E.

Create Group

Create a messaging group, optionally linking an existing iMessage chat and recording its members; new contacts may be created for unknown members.

Delete Contact

Soft-delete one contact identified by E.

Delete Group

Soft-delete a group, remove its members, and exit its linked iMessage chat when one exists.

Get Account

Return the authenticated organization, assigned devices, and message usage without exposing the API-key identifier.

Get Chat

Return detailed counts, participant context, and latest-message metadata for one conversation.

Get Contact

Return one contact by E.

Get Contact Capabilities

Check whether a contact can receive iMessage, SMS, or FaceTime before choosing a communication path.

Get Group

Return one messaging group by its grp_ identifier.

Get Message

Return one message with content, reactions, delivery status, protocol, errors, and inline-reply context.

Get Poll Results

Return a poll's definition, option vote counts, and total votes.

Get Risk Summary

Return the cached fleet risk rollup.

List Chats

Search and page through conversations, ordered by recent activity or oldest activity.

List Contacts

Search and page through organization contacts by identifier or name.

List Group Members

Page through the members recorded for a messaging group.

List Groups

Search and page through messaging groups by name.

List Messages

Page through messages in one chat with direction, time-window, and ordering filters.

List Numbers

List sender phone numbers assigned to the connected API key and their availability state.

Mark Chat Read

Mark every message in a chat as read and irreversibly send an externally visible read receipt to the sender; the receipt cannot be unsent.

Remove Contact Tags

Soft-delete one or more exact tags from a contact sequentially; if a later removal fails, earlier removals remain applied.

Send Message

Irreversibly send one or multiple text, attachment, multipart, or inline-reply messages; multi-recipient sends may create or reuse an unnamed group and may incur messaging charges.

Send Poll

Irreversibly send an interactive native iMessage poll to a chat.

Set Message Reaction

Add or remove a classic tapback or emoji reaction on a specific or relative message in a chat.

Update Contact

Set or clear the display name of an existing contact.

Update Group

Rename a group and, when linked, synchronize the new name to its iMessage chat.

FRAMEWORKS

How to build Blooio MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Blooio MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Blooio MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Blooio MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Blooio MCP with Claude Cowork

Codex logo

Codex

Use Blooio MCP with Codex

OpenClaw logo

OpenClaw

Use Blooio MCP with OpenClaw

Hermes logo

Hermes

Use Blooio MCP with Hermes

CLI logo

CLI

Use Blooio MCP with CLI

Google ADK logo

Google ADK

Use Blooio MCP with Google ADK

LangChain logo

LangChain

Use Blooio MCP with LangChain

Mastra AI logo

Mastra AI

Use Blooio MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Blooio MCP with LlamaIndex

CrewAI logo

CrewAI

Use Blooio MCP with CrewAI

FAQ

Frequently asked questions

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

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 Blooio tools.

Yes, absolutely. You can configure which Blooio 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.

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 Blooio data and credentials are handled as safely as possible.

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