How to integrate Toggl Track MCP with Vercel AI SDK v6

This guide walks you through connecting Toggl Track to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Toggl Track agent that can track your time, create clients and projects, manage your tasks, and update time entries through natural language commands. This guide will help you understand how to give your Vercel AI SDK agent real control over a Toggl Track account through Composio's Toggl Track MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Toggl Track is a time tracking platform for recording work, organizing projects, and managing workspaces. It helps teams understand where time goes with clear reports, billable hours, and lightweight workflows.

21 Tools

Introduction

This guide walks you through connecting Toggl Track to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Toggl Track agent that can track your time, create clients and projects, manage your tasks, and update time entries through natural language commands.

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

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

Also integrate Toggl Track 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 Toggl Track integration
  • Using Composio's Tool Router to dynamically load and access Toggl Track 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 Toggl Track MCP server, and what's possible with it?

The Toggl Track MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Toggl Track account. It provides structured and secure access to your time entries, workspaces, projects, clients, tasks, and reports, so your agent can track work, manage timers, organize projects, update records, and produce time reports on your behalf.

  • Time entry management: Have your agent create, review, update, or delete time entries, including their descriptions, timing, billable status, tags, and work assignments.
  • Running timer control: Let the agent check your current timer, start a new running time entry, or stop work in progress.
  • Workspace and project organization: Direct your agent to list workspaces and manage projects, clients, and tasks used to organize tracked work.
  • Detailed time reporting: Instruct your agent to produce detailed time reports for a workspace and date range, grouped into useful report rows.
  • Bulk record updates: Have your agent apply the same description or billable status to multiple time entries at once.

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: ["toggl_track"],
  });

  const mcpUrl = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Toggl Track 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 Toggl Track-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 Toggl Track 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 toggl_track, 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 Toggl Track 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 Toggl Track 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: ["toggl_track"],
  });

  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 toggl_track, 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 Toggl Track 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 Toggl Track action and event your agent gets out of the box.

Bulk Update Time Entries

Apply the same description and/or billable state to 1 to 100 Toggl Track time entries in one provider-native request.

Create Client

Create a client in a workspace for grouping projects.

Create Project

Create a project in a workspace for organizing time entries and tasks.

Create Task

Create a task under an active project for more specific time-entry assignment.

Create Time Entry

Create a running or completed time entry in a workspace; omit duration and stop to start a running timer.

Delete Client

Permanently delete one client from a Toggl Track workspace.

Delete Project

Delete one project while preserving its time entries by unassigning them from the project.

Delete Task

Permanently delete one task from its parent project.

Delete Time Entry

Permanently delete one time entry from a workspace.

Get Current Time Entry

Return the currently running time entry for the connected user, or indicate that no timer is running.

Get Current User

Return the connected Toggl Track user's identity and defaults, including the default workspace ID, without exposing credentials.

Get Time Entry

Get one time entry by ID, with optional related-entity and sharing metadata.

List Time Entries

List the connected user's time entries in an explicit date range, optionally including related project, task, user, and sharing metadata.

List Workspace Resources

Find projects, clients, tags, or tasks in a workspace and return their IDs for time-entry and resource-management tools.

List Workspaces

List workspaces accessible to the connected user and return the IDs needed by workspace-scoped tools, without exposing private tokens or calendar URLs.

Run Detailed Time Report

Return one page of detailed, grouped time-entry report rows for a workspace and date range, with an opaque cursor for the next page.

Stop Time Entry

Stop a currently running time entry and return its finalized timing data.

Update Client

Update a client's name, notes, or external reference.

Update Project

Update a project's name, client, active state, privacy, color, or date range.

Update Task

Update a task's name, completion state, estimate, assignee, or external reference.

Update Time Entry

Update the timing, description, assignment, billable state, or tags of an existing time entry.

FRAMEWORKS

How to build Toggl Track MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Toggl Track MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Toggl Track MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Toggl Track MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Toggl Track MCP with Claude Cowork

Codex logo

Codex

Use Toggl Track MCP with Codex

OpenClaw logo

OpenClaw

Use Toggl Track MCP with OpenClaw

Hermes logo

Hermes

Use Toggl Track MCP with Hermes

CLI logo

CLI

Use Toggl Track MCP with CLI

Google ADK logo

Google ADK

Use Toggl Track MCP with Google ADK

LangChain logo

LangChain

Use Toggl Track MCP with LangChain

Mastra AI logo

Mastra AI

Use Toggl Track MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Toggl Track MCP with LlamaIndex

CrewAI logo

CrewAI

Use Toggl Track MCP with CrewAI

FAQ

Frequently asked questions

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

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

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