# How to integrate Google Tasks MCP with Vercel AI SDK v6 ```json { "title": "How to integrate Google Tasks MCP with Vercel AI SDK v6", "toolkit": "Google Tasks", "toolkit_slug": "googletasks", "framework": "Vercel AI SDK", "framework_slug": "ai-sdk", "url": "https://composio.dev/toolkits/googletasks/framework/ai-sdk", "markdown_url": "https://composio.dev/toolkits/googletasks/framework/ai-sdk.md", "updated_at": "2026-05-06T08:14:35.383Z" } ``` ## Introduction This guide walks you through connecting Google Tasks to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Google Tasks agent that can add a new task to your work list, list all tasks due this week, delete completed tasks from your shopping list through natural language commands. This guide will help you understand how to give your Vercel AI SDK agent real control over a Google Tasks account through Composio's Google Tasks MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Google Tasks with - [ChatGPT](https://composio.dev/toolkits/googletasks/framework/chatgpt) - [OpenAI Agents SDK](https://composio.dev/toolkits/googletasks/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/googletasks/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/googletasks/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/googletasks/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/googletasks/framework/codex) - [Cursor](https://composio.dev/toolkits/googletasks/framework/cursor) - [VS Code](https://composio.dev/toolkits/googletasks/framework/vscode) - [OpenCode](https://composio.dev/toolkits/googletasks/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/googletasks/framework/openclaw) - [Hermes](https://composio.dev/toolkits/googletasks/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/googletasks/framework/cli) - [Google ADK](https://composio.dev/toolkits/googletasks/framework/google-adk) - [LangChain](https://composio.dev/toolkits/googletasks/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/googletasks/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/googletasks/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/googletasks/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up and configure a Vercel AI SDK agent with Google Tasks integration - Using Composio's Tool Router to dynamically load and access Google Tasks 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 Google Tasks MCP server, and what's possible with it? The Google Tasks MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Google Tasks account. It provides structured and secure access to your to-do lists and tasks, so your agent can create task lists, add or update tasks, reorganize and clean up your lists, and fetch or manage your action items automatically. - Intelligent task list management: Ask your agent to create new to-do lists, fetch existing ones, or remove lists you no longer need—all without manual clicks. - Automated task creation and updates: Let your agent add new tasks, set due dates, or update existing to-dos to keep your lists current and organized. - Efficient task organization and movement: Move tasks between lists, reorder them, or set parent/child relationships so your priorities always stay clear. - Fast cleanup and deletion: Direct your agent to clear completed tasks or delete specific items and lists, helping you declutter swiftly and securely. - Detailed task retrieval and review: Have your agent pull details on any task or list so you can review upcoming deadlines, notes, and status at a glance. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `GOOGLETASKS_CLEAR_TASKS` | Clear tasks | Permanently clears all completed tasks from a specified google tasks list; this action is destructive and idempotent. | | `GOOGLETASKS_CREATE_TASK_LIST` | Create a task list | Creates a new task list with the specified title. | | `GOOGLETASKS_DELETE_TASK` | Delete task | Deletes a specified task from a given task list in google tasks. | | `GOOGLETASKS_DELETE_TASK_LIST` | Delete task list | Permanently deletes an existing google task list, identified by `tasklist id`, along with all its tasks; this operation is irreversible. | | `GOOGLETASKS_GET_TASK` | Get Task | Use to retrieve a specific google task if its `task id` and parent `tasklist id` are known. | | `GOOGLETASKS_GET_TASK_LIST` | Get task list | Retrieves a specific task list from the user's google tasks if the `tasklist id` exists for the authenticated user. | | `GOOGLETASKS_INSERT_TASK` | Insert Task | Creates a new task in a given `tasklist id`, optionally as a subtask of an existing `task parent` or positioned after an existing `task previous` sibling, where both `task parent` and `task previous` must belong to the same `tasklist id` if specified. | | `GOOGLETASKS_LIST_TASK_LISTS` | List task lists | Fetches the authenticated user's task lists from google tasks; results may be paginated. | | `GOOGLETASKS_LIST_TASKS` | List Tasks | Retrieves tasks from a google tasks list; all date/time strings must be rfc3339 utc, and `showcompleted` must be true if `completedmin` or `completedmax` are specified. | | `GOOGLETASKS_MOVE_TASK` | Move Task | Moves the specified task to another position in the destination task list. | | `GOOGLETASKS_PATCH_TASK` | Patch Task | Partially updates an existing task (identified by `task id`) within a specific google task list (identified by `tasklist id`), modifying only the provided attributes from `taskinput` (e.g., `title`, `notes`, `due` date, `status`) and requiring both the task and list to exist. | | `GOOGLETASKS_PATCH_TASK_LIST` | Patch task list | Updates the title of an existing google tasks task list. | | `GOOGLETASKS_UPDATE_TASK` | Update Task | Updates the specified task. | | `GOOGLETASKS_UPDATE_TASK_LIST` | Update Task List | Updates the authenticated user's specified task list. | ## Supported Triggers | Trigger slug | Name | Description | |---|---|---| | `GOOGLETASKS_NEW_TASK_CREATED_TRIGGER` | New Task Created | Triggers when a new task is created in a Google Tasks list. Uses timestamp filtering (updatedMin) to efficiently detect new tasks. | | `GOOGLETASKS_NEW_TASK_LIST_CREATED_TRIGGER` | New Task List Created | Triggers when a new Google Tasks task list is created. This trigger monitors Google Tasks and fires when new task lists are detected. | | `GOOGLETASKS_TASK_DETAILS_CHANGED_TRIGGER` | Task Details Changed | Triggers when a specific task's details change. This trigger monitors a single Google Task and fires when any of its details (title, notes, status, due date, completion, position) are modified. | | `GOOGLETASKS_TASK_LIST_CHANGED_TRIGGER` | Task List Changed | Triggers when a task list changes (title or content updates). This trigger monitors a specific Google Tasks list and fires when changes are detected. | | `GOOGLETASKS_TASK_UPDATED_TRIGGER` | Task Updated | Triggers when an existing task is updated in a Google Tasks list. This trigger monitors a specific task list and fires when tasks are modified. | ## Creating MCP Server - Stand-alone vs Composio SDK The Google Tasks MCP server is an implementation of the Model Context Protocol that connects your AI agent to Google Tasks. It provides structured and secure access so your agent can perform Google Tasks 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 Google Tasks 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 Google Tasks-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: ["googletasks"], }); 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 Google Tasks 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 googletasks, 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 Google Tasks 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: ["googletasks"], }); 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 googletasks, 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 Google Tasks 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 Google Tasks MCP Agent with another framework - [ChatGPT](https://composio.dev/toolkits/googletasks/framework/chatgpt) - [OpenAI Agents SDK](https://composio.dev/toolkits/googletasks/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/googletasks/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/googletasks/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/googletasks/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/googletasks/framework/codex) - [Cursor](https://composio.dev/toolkits/googletasks/framework/cursor) - [VS Code](https://composio.dev/toolkits/googletasks/framework/vscode) - [OpenCode](https://composio.dev/toolkits/googletasks/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/googletasks/framework/openclaw) - [Hermes](https://composio.dev/toolkits/googletasks/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/googletasks/framework/cli) - [Google ADK](https://composio.dev/toolkits/googletasks/framework/google-adk) - [LangChain](https://composio.dev/toolkits/googletasks/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/googletasks/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/googletasks/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/googletasks/framework/crew-ai) ## Related Toolkits - [Google Sheets](https://composio.dev/toolkits/googlesheets) - Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly. - [Notion](https://composio.dev/toolkits/notion) - Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place. - [Airtable](https://composio.dev/toolkits/airtable) - Airtable combines the flexibility of spreadsheets with the power of a database for easy project and data management. Teams use Airtable to organize, track, and collaborate with custom views and automations. - [Asana](https://composio.dev/toolkits/asana) - Asana is a collaborative work management platform for teams to organize and track projects. It streamlines teamwork, boosts productivity, and keeps everyone aligned on goals. - [Linear](https://composio.dev/toolkits/linear) - Linear is a modern issue tracking and project planning tool for fast-moving teams. It helps streamline workflows, organize projects, and boost productivity. - [Jira](https://composio.dev/toolkits/jira) - Jira is Atlassian’s platform for bug tracking, issue tracking, and agile project management. It helps teams organize work, prioritize tasks, and deliver projects efficiently. - [Clickup](https://composio.dev/toolkits/clickup) - ClickUp is an all-in-one productivity platform for managing tasks, docs, goals, and team collaboration. It streamlines project workflows so teams can work smarter and stay organized in one place. - [Monday](https://composio.dev/toolkits/monday) - Monday.com is a customizable work management platform for project planning and collaboration. It helps teams organize tasks, automate workflows, and track progress in real time. - [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time. - [Agiled](https://composio.dev/toolkits/agiled) - Agiled is an all-in-one business management platform for CRM, projects, and finance. It helps you streamline workflows, consolidate client data, and manage business processes in one place. - [Ascora](https://composio.dev/toolkits/ascora) - Ascora is a cloud-based field service management platform for service businesses. It streamlines scheduling, invoicing, and customer operations in one place. - [Basecamp](https://composio.dev/toolkits/basecamp) - Basecamp is a project management and team collaboration tool by 37signals. It helps teams organize tasks, share files, and communicate efficiently in one place. - [Beeminder](https://composio.dev/toolkits/beeminder) - Beeminder is an online goal-tracking platform that uses monetary pledges to keep you motivated. Stay accountable and hit your targets with real financial incentives. - [Boxhero](https://composio.dev/toolkits/boxhero) - Boxhero is a cloud-based inventory management platform for SMBs, offering real-time updates, barcode scanning, and team collaboration. It helps businesses streamline stock tracking and analytics for smarter inventory decisions. - [Breathe HR](https://composio.dev/toolkits/breathehr) - Breathe HR is cloud-based HR software for SMEs to manage employee data, absences, and performance. It simplifies HR admin, making it easy to keep employee records accurate and up to date. - [Breeze](https://composio.dev/toolkits/breeze) - Breeze is a project management platform designed to help teams plan, track, and collaborate on projects. It streamlines workflows and keeps everyone on the same page. - [Bugherd](https://composio.dev/toolkits/bugherd) - Bugherd is a visual feedback and bug tracking tool for websites. It helps teams and clients report website issues directly on live sites for faster fixes. - [Canny](https://composio.dev/toolkits/canny) - Canny is a platform for managing customer feedback and feature requests. It helps teams prioritize product decisions based on real user insights. - [Chmeetings](https://composio.dev/toolkits/chmeetings) - Chmeetings is a church management platform for events, members, donations, and volunteers. It streamlines church operations and improves community engagement. - [ClickSend](https://composio.dev/toolkits/clicksend) - ClickSend is a cloud-based SMS and email marketing platform for businesses. It streamlines communication by enabling quick message delivery and contact management. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Google Tasks MCP? With a standalone Google Tasks MCP server, the agents and LLMs can only access a fixed set of Google Tasks tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Google Tasks 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 Google Tasks tools. ### Can I manage the permissions and scopes for Google Tasks while using Tool Router? Yes, absolutely. You can configure which Google Tasks 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 Google Tasks data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)