# How to integrate Segment MCP with Vercel AI SDK v6

```json
{
  "title": "How to integrate Segment MCP with Vercel AI SDK v6",
  "toolkit": "Segment",
  "toolkit_slug": "segment",
  "framework": "Vercel AI SDK",
  "framework_slug": "ai-sdk",
  "url": "https://composio.dev/toolkits/segment/framework/ai-sdk",
  "markdown_url": "https://composio.dev/toolkits/segment/framework/ai-sdk.md",
  "updated_at": "2026-05-12T10:25:03.502Z"
}
```

## Introduction

This guide walks you through connecting Segment to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Segment agent that can fetch daily api usage for each source, add metadata labels to a specific source, identify a user and update their traits through natural language commands.
This guide will help you understand how to give your Vercel AI SDK agent real control over a Segment account through Composio's Segment MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Segment with

- [OpenAI Agents SDK](https://composio.dev/toolkits/segment/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/segment/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/segment/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/segment/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/segment/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/segment/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/segment/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/segment/framework/cli)
- [Google ADK](https://composio.dev/toolkits/segment/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/segment/framework/langchain)
- [Mastra AI](https://composio.dev/toolkits/segment/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/segment/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/segment/framework/crew-ai)

## TL;DR

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

The Segment MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Segment account. It provides structured and secure access to your customer data platform, so your agent can perform actions like identifying users, tracking analytics events, managing sources and destinations, and monitoring API usage on your behalf.
- User identification and trait management: Your agent can identify users, set or update their traits, and manage user profiles using Segment's Identify and Group tools.
- Analytics event tracking and batching: Effortlessly track individual or batched analytics events, enabling automated insights and seamless event monitoring across platforms.
- Source and destination administration: Let your agent add labels to sources, delete sources, retrieve detailed destination configurations, and list warehouses connected to a source.
- Alias and merge user identities: Merge anonymous and known identities by aliasing user IDs for more accurate customer journeys and unified profiles.
- Usage monitoring and delivery metrics: Fetch daily API call usage per source and view delivery metrics summaries for destinations to keep tabs on system health and integration performance.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `SEGMENT_ADD_LABELS_TO_SOURCE` | Add Labels to Source | Tool to add existing labels to a Source. Use when you have the source ID and want to tag it with metadata labels. |
| `SEGMENT_ALIAS` | Segment Alias | Tool to alias a previous user ID to a new user ID. Use when merging anonymous and known identities. |
| `SEGMENT_BATCH` | Batch Segment Analytics Events | Tool to send multiple analytics calls in a single batch request. Use when you want to reduce HTTP overhead by batching Identify/Track/Page/Screen/Group calls into one request. |
| `SEGMENT_DELETE_SOURCE` | Delete Source | Tool to delete a Segment Source. Use when you need to permanently remove a Source by its ID after confirmation. |
| `SEGMENT_GET_DAILY_PER_SOURCE_API_CALLS_USAGE` | Get Daily Per Source API Calls Usage | Tool to fetch daily API call counts per source for a given period. Use when you need daily breakdown of API usage by source after determining the reporting period. |
| `SEGMENT_GET_DESTINATION` | Get Destination | Tool to retrieve a Destination by ID. Use when you need to fetch the full configuration of a Segment Destination instance by its unique identifier. Falls back US→EU public API and legacy app endpoint; returns minimal envelope on legacy HTML or parse errors. |
| `SEGMENT_GROUP` | Segment Group | Tool to associate an identified user with a group via Segment HTTP Tracking API. Use when grouping users with traits. |
| `SEGMENT_IDENTIFY` | Segment Identify | Tool to identify a user and set/update traits via Segment HTTP Tracking API. |
| `SEGMENT_IMPORT_HISTORICAL_DATA` | Import Historical Data | Tool to import historical data in bulk with support for historical timestamps. Use when you need to backfill or import past events with their original timestamps into Segment. |
| `SEGMENT_LIST_CONNECTED_WAREHOUSES_FROM_SOURCE` | List Connected Warehouses From Source | Tool to list warehouses connected to a Source. Use when you need to retrieve warehouses for a given source ID. |
| `SEGMENT_LIST_DELIVERY_METRICS_SUMMARY_FROM_DESTINATION` | List Delivery Metrics Summary from Destination | Get an event delivery metrics summary from a Destination. Primary attempt uses Segment Public API; fallback to legacy app host if needed. On HTML fallback responses, returns a minimal valid envelope to maintain contract. |
| `SEGMENT_LIST_SCHEMA_SETTINGS_IN_SOURCE` | List Schema Settings in Source | Retrieve schema configuration settings for a Source. |
| `SEGMENT_PAGE` | Segment Page View | Tool to record a page view via Segment HTTP Tracking API. Use when sending page views with optional page name and properties. |
| `SEGMENT_REMOVE_SOURCE_WRITE_KEY` | Remove Source Write Key | Tool to remove a write key from a Source. Use when you need to revoke an existing write key for security or rotation. |
| `SEGMENT_SCREEN` | Segment Screen Event | Tool to record a mobile app screen view. Use when tracking screen views in a mobile app via Segment HTTP Tracking API. |
| `SEGMENT_TRACK` | Segment Track Event | Tool to record a custom user event via Segment HTTP Tracking API. Use when sending events to Segment with valid identity. |
| `SEGMENT_UPDATE_SOURCE` | Update Source | Tool to update a Source's metadata and settings. Use when you need to modify an existing Source after confirming its ID. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Segment MCP server is an implementation of the Model Context Protocol that connects your AI agent to Segment. It provides structured and secure access so your agent can perform Segment operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

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

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install required dependencies

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

### 3. Set up environment variables

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
```bash
OPENAI_API_KEY=your_openai_api_key_here
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here
```

### 4. Import required modules and validate environment

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
```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,
});
```

### 5. Create Tool Router session and initialize MCP client

What's happening:
- We're creating a Tool Router session that gives your agent access to Segment 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 Segment-related tools through the MCP protocol
```typescript
async function main() {
  // Create a tool router session for the user
  const session = await composio.create(composioUserID!, {
    toolkits: ["segment"],
  });

  const mcpUrl = session.mcp.url;
```

### 6. Connect to MCP server and retrieve 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 Segment tools that the agent can use
```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();
```

### 7. Initialize conversation and CLI interface

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
```typescript
let messages: ModelMessage[] = [];

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

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

rl.prompt();
```

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

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 Segment 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
```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);
});
```

## Complete Code

```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: ["segment"],
  });

  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 segment, 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 Segment 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 Segment MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/segment/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/segment/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/segment/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/segment/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/segment/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/segment/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/segment/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/segment/framework/cli)
- [Google ADK](https://composio.dev/toolkits/segment/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/segment/framework/langchain)
- [Mastra AI](https://composio.dev/toolkits/segment/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/segment/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/segment/framework/crew-ai)

## Related Toolkits

- [Firecrawl](https://composio.dev/toolkits/firecrawl) - Firecrawl automates large-scale web crawling and data extraction. It helps organizations efficiently gather, index, and analyze content from online sources.
- [Tavily](https://composio.dev/toolkits/tavily) - Tavily offers powerful search and data retrieval from documents, databases, and the web. It helps teams locate and filter information instantly, saving hours on research.
- [Exa](https://composio.dev/toolkits/exa) - Exa is a data extraction and search platform for gathering and analyzing information from websites, APIs, or databases. It helps teams quickly surface insights and automate data-driven workflows.
- [Serpapi](https://composio.dev/toolkits/serpapi) - SerpApi is a real-time API for structured search engine results. It lets you automate SERP data collection, parsing, and analysis for SEO and research.
- [Peopledatalabs](https://composio.dev/toolkits/peopledatalabs) - Peopledatalabs delivers B2B data enrichment and identity resolution APIs. Supercharge your apps with accurate, up-to-date business and contact data.
- [Snowflake](https://composio.dev/toolkits/snowflake) - Snowflake is a cloud data warehouse built for elastic scaling, secure data sharing, and fast SQL analytics across major clouds.
- [Posthog](https://composio.dev/toolkits/posthog) - PostHog is an open-source analytics platform for tracking user interactions and product metrics. It helps teams refine features, analyze funnels, and reduce churn with actionable insights.
- [Amplitude](https://composio.dev/toolkits/amplitude) - Amplitude is a digital analytics platform for product and behavioral data insights. It helps teams analyze user journeys and make data-driven decisions quickly.
- [Bright Data MCP](https://composio.dev/toolkits/brightdata_mcp) - Bright Data MCP is an AI-powered web scraping and data collection platform. Instantly access public web data in real time with advanced scraping tools.
- [Browseai](https://composio.dev/toolkits/browseai) - Browseai is a web automation and data extraction platform that turns any website into an API. It's perfect for monitoring websites and retrieving structured data without manual scraping.
- [ClickHouse](https://composio.dev/toolkits/clickhouse) - ClickHouse is an open-source, column-oriented database for real-time analytics and big data processing using SQL. Its lightning-fast query performance makes it ideal for handling large datasets and delivering instant insights.
- [Coinmarketcal](https://composio.dev/toolkits/coinmarketcal) - CoinMarketCal is a community-powered crypto calendar for upcoming events, announcements, and releases. It helps traders track market-moving developments and stay ahead in the crypto space.
- [Control d](https://composio.dev/toolkits/control_d) - Control d is a customizable DNS filtering and traffic redirection platform. It helps you manage internet access, enforce policies, and monitor usage across devices and networks.
- [Databox](https://composio.dev/toolkits/databox) - Databox is a business analytics platform that connects your data from any tool and device. It helps you track KPIs, build dashboards, and discover actionable insights.
- [Databricks](https://composio.dev/toolkits/databricks) - Databricks is a unified analytics platform for big data and AI on the lakehouse architecture. It empowers data teams to collaborate, analyze, and build scalable solutions efficiently.
- [Datagma](https://composio.dev/toolkits/datagma) - Datagma delivers data intelligence and analytics for business growth and market discovery. Get actionable market insights and track competitors to inform your strategy.
- [Delighted](https://composio.dev/toolkits/delighted) - Delighted is a customer feedback platform based on the Net Promoter System®. It helps you quickly gather, track, and act on customer sentiment.
- [Dovetail](https://composio.dev/toolkits/dovetail) - Dovetail is a research analysis platform for transcript review and insight generation. It helps teams code interviews, analyze feedback, and create actionable research summaries.
- [Dub](https://composio.dev/toolkits/dub) - Dub is a short link management platform with analytics and API access. Use it to easily create, manage, and track branded short links for your business.
- [Elasticsearch](https://composio.dev/toolkits/elasticsearch) - Elasticsearch is a distributed, RESTful search and analytics engine for all types of data. It delivers fast, scalable search and powerful analytics across massive datasets.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Segment MCP?

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

### Can I manage the permissions and scopes for Segment while using Tool Router?

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

---
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