How to integrate OneTapCheckIn MCP with Vercel AI SDK v6

This guide walks you through connecting OneTapCheckIn to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working OneTapCheckIn agent that can create attendee lists, add participants, find profiles, review alerts, and manage your profiles through natural language commands. This guide will help you understand how to give your Vercel AI SDK agent real control over a OneTapCheckIn account through Composio's OneTapCheckIn MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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OneTapCheckIn is an event attendance platform for managing attendees, profiles, check-ins, passports, integrations, and alerts. It helps teams run smoother events with faster check-ins and clearer attendance tracking.

19 Tools

Introduction

This guide walks you through connecting OneTapCheckIn to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working OneTapCheckIn agent that can create attendee lists, add participants, find profiles, review alerts, and manage your profiles through natural language commands.

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

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

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

The OneTapCheckIn MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your OneTapCheckIn account. It provides structured and secure access to your events, attendee profiles, attendance records, passports, alerts, and organization settings, so your agent can create event lists, manage attendee profiles, add participants, track attendance, and review event information on your behalf.

  • Event list management: Have your agent create, find, update, or delete event and check-in lists for your organization.
  • Attendee profile management: Let the agent create, search, update, or delete attendee profiles, including batches of up to 25 profiles and values based on your configured custom fields.
  • Participant organization: Direct your agent to add existing profiles to lists, find attendance records, update participant details, or remove participants from a list.
  • Attendance tracking: Instruct your agent to check in, check out, undo check-ins, or undo checkouts for one participant or batches of up to 25 participants.
  • Passports, alerts, and organization details: Have your agent review attendee passports, attendance alerts, automation rules, check-in confirmation settings, and connected organization information.

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: ["onetapcheckin"],
  });

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

  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 onetapcheckin, 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 OneTapCheckIn 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 OneTapCheckIn action and event your agent gets out of the box.

Add Participants

Add one or more existing profiles to one or more lists without checking them in.

Create List

Create an event/check-in list.

Create Profile

Create one person profile without adding it to a list.

Create Profiles

Create 1 to 25 person profiles in input order using one provider request per profile.

Delete List

Permanently delete one list by ID.

Delete Profile

Permanently delete one profile.

Find Lists

Get one list by exact ID or search the connected organization's lists.

Find Participants

Get one participant by exact ID or search attendance records by list, profile, status, text, or date range.

Find Profiles

Get one profile by ID or search profiles by identity, list, passport, or check-in code.

Get Organization

Return the organization associated with the connected API key.

Get Profile Custom Fields

Return the organization's configured profile custom-field definitions so an agent can use valid field names and value types when creating or updating profiles.

List Alerts

Return one page of organization alert and automation rules together with outbound check-in confirmation settings.

List Passports

List one page of standard passports for exactly one profile or participant.

Remove Participant

Remove one participant record from its list.

Update List

Partially update a list.

Update Participant

Update metadata on one participant record without performing check-in or checkout.

Update Participant Attendance

Check in, check out, undo check-in, or undo checkout for one known participant.

Update Participant Attendance Batch

Apply one shared check-in, checkout, undo check-in, or undo checkout operation to 1 to 25 participants in input order, using one provider request per item.

Update Profile

Partially update one profile.

FRAMEWORKS

How to build OneTapCheckIn MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use OneTapCheckIn MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use OneTapCheckIn MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use OneTapCheckIn MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use OneTapCheckIn MCP with Claude Cowork

Codex logo

Codex

Use OneTapCheckIn MCP with Codex

OpenClaw logo

OpenClaw

Use OneTapCheckIn MCP with OpenClaw

Hermes logo

Hermes

Use OneTapCheckIn MCP with Hermes

CLI logo

CLI

Use OneTapCheckIn MCP with CLI

Google ADK logo

Google ADK

Use OneTapCheckIn MCP with Google ADK

LangChain logo

LangChain

Use OneTapCheckIn MCP with LangChain

Mastra AI logo

Mastra AI

Use OneTapCheckIn MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use OneTapCheckIn MCP with LlamaIndex

CrewAI logo

CrewAI

Use OneTapCheckIn MCP with CrewAI

FAQ

Frequently asked questions

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

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

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