# How to integrate Passcreator MCP with Vercel AI SDK v6

```json
{
  "title": "How to integrate Passcreator MCP with Vercel AI SDK v6",
  "toolkit": "Passcreator",
  "toolkit_slug": "passcreator",
  "framework": "Vercel AI SDK",
  "framework_slug": "ai-sdk",
  "url": "https://composio.dev/toolkits/passcreator/framework/ai-sdk",
  "markdown_url": "https://composio.dev/toolkits/passcreator/framework/ai-sdk.md",
  "updated_at": "2026-05-12T10:21:28.144Z"
}
```

## Introduction

This guide walks you through connecting Passcreator to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Passcreator agent that can find all event tickets created this week, check if a membership card exists for john doe, list available coupon pass templates for your account through natural language commands.
This guide will help you understand how to give your Vercel AI SDK agent real control over a Passcreator account through Composio's Passcreator MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Passcreator with

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

## TL;DR

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

The Passcreator MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Passcreator account. It provides structured and secure access to your digital wallet passes, so your agent can perform actions like searching passes, verifying pass existence, and retrieving pass templates on your behalf.
- Search and filter wallet passes: Quickly ask your agent to locate passes in your account using filters such as external ID, type, or status.
- Verify pass existence: Have your agent check if a specific digital pass already exists before sending updates or making changes.
- Retrieve pass templates: Let your agent list and browse available pass templates for creating or managing new digital passes.
- Support for bulk and paginated operations: Enable your agent to efficiently handle large numbers of passes or templates by using pagination and advanced search.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `PASSCREATOR_CHECK_PASS_EXISTENCE` | Check Pass Existence | Tool to check if a pass exists for a given ID. Use when verifying pass existence before subsequent operations like updates or deletions. The ID can be a generatedId (unique ID created for every pass, usually encoded in the barcode), userProvidedId (optional custom ID), or any other identifier associated with a pass. |
| `PASSCREATOR_CREATE_APP_SCAN` | Create App Scan | Tool to create a new App Scan in PassCreator. Use when recording pass validation or attendance scanning events. Supports tracking scan status, device information, and optional pass voiding. |
| `PASSCREATOR_GET_APP_CONFIGURATION` | Get App Configuration | Retrieves detailed information about an App Configuration by its identifier. Use when you need to get scan settings, UI customization, or validation rules for a specific App Configuration. |
| `PASSCREATOR_GET_PROCESS_STATUS` | Get Process Status | Get the current status and progress of a bulk operation including any errors. Use this to monitor long-running bulk operations like batch pass updates or creations. The identifier is returned when initiating a bulk operation. |
| `PASSCREATOR_GET_SIGNING_PUBLIC_KEY` | Get Signing Public Key | Tool to obtain the public key needed to verify signatures from the placeholder sign() function. Use when you need to verify cryptographic signatures generated by Passcreator's sign placeholder. |
| `PASSCREATOR_LIST_APP_CONFIGURATIONS` | List App Configurations | Retrieves all App Configurations for your Passcreator account. Use this action to get a list of validation configurations that control how passes are scanned and validated. Each configuration can be linked to specific pass templates or validate all passes. |
| `PASSCREATOR_LIST_APP_SCANS` | List App Scans | Retrieves a paginated list of scans for a given app configuration. Use this tool to view scan history, track attendance, and analyze scan data ordered by creation date. |
| `PASSCREATOR_LIST_PASSES` | List/Search Passes | List and search wallet passes from Passcreator using the v3 API. Use this tool to: - Retrieve all passes in your account - Filter passes by template ID or project ID - Search passes using a search phrase across all data fields - Paginate through large result sets Returns passes with metadata including identifiers, serial numbers, template info, and voided/redeemed status. |
| `PASSCREATOR_LIST_PASS_TEMPLATES` | List Pass Templates | Retrieves all pass templates for your Passcreator account. Use this action to get a list of available templates (each with its unique identifier and name) which are needed to create new passes. Templates must be created via the Passcreator web app. |
| `PASSCREATOR_SEND_BULK_PUSH_NOTIFICATIONS` | Send Bulk Push Notifications | Tool to send push notifications to multiple wallet passes simultaneously (up to 500 passes). Use when you need to notify pass holders about updates, events, or important information. The notification text can include personalization placeholders like {Firstname}. |
| `PASSCREATOR_UPDATE_PASSES_BULK` | Bulk Update Passes | Tool to bulk update multiple wallet passes using filter criteria. Returns immediately with a tracking URL to monitor the asynchronous bulk operation progress. Use when updating many passes at once with the same data changes. |

## Supported Triggers

None listed.

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

The Passcreator MCP server is an implementation of the Model Context Protocol that connects your AI agent to Passcreator. It provides structured and secure access so your agent can perform Passcreator 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 Passcreator 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 Passcreator-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: ["passcreator"],
  });

  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 Passcreator 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 passcreator, 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 Passcreator 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: ["passcreator"],
  });

  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 passcreator, 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 Passcreator 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 Passcreator MCP Agent with another framework

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

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## Frequently Asked Questions

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

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

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

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

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)
