# How to integrate LLMWhisperer MCP with Vercel AI SDK v6

```json
{
  "title": "How to integrate LLMWhisperer MCP with Vercel AI SDK v6",
  "toolkit": "LLMWhisperer",
  "toolkit_slug": "llmwhisperer",
  "framework": "Vercel AI SDK",
  "framework_slug": "ai-sdk",
  "url": "https://composio.dev/toolkits/llmwhisperer/framework/ai-sdk",
  "markdown_url": "https://composio.dev/toolkits/llmwhisperer/framework/ai-sdk.md",
  "updated_at": "2026-03-29T06:40:48.248Z"
}
```

## Introduction

This guide walks you through connecting LLMWhisperer to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working LLMWhisperer agent that can summarize main points from uploaded contract, extract key dates from legal document, classify sections of this technical report through natural language commands.
This guide will help you understand how to give your Vercel AI SDK agent real control over a LLMWhisperer account through Composio's LLMWhisperer MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate LLMWhisperer with

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

## TL;DR

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

The LLMWhisperer MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your LLMWhisperer account. It provides structured and secure access so your agent can perform LLMWhisperer operations on your behalf.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `LLMWHISPERER_USAGE_GET_INFO` | Get Usage Information | Tool to check usage metrics of your LLMWhisperer account. Use when you need to monitor API consumption, verify quotas, or check remaining page limits. |
| `LLMWHISPERER_USAGE_GET_STATS` | Get Usage Statistics | Tool to retrieve usage statistics for your LLMWhisperer account based on a specific tag. Use when you need to check consumption metrics for a given tag and optional date range. Returns usage data for the preceding 30 days when date parameters are omitted. |
| `LLMWHISPERER_WEBHOOK_DELETE` | Delete Webhook | Tool to delete a registered webhook from LLMWhisperer system. Use when you need to remove a webhook that is no longer needed. |
| `LLMWHISPERER_WEBHOOK_GET_DETAILS` | Get Webhook Details | Tool to retrieve registered webhook details for LLMWhisperer. Use when you need to get the configuration of a specific webhook including its URL and authentication token. |
| `LLMWHISPERER_REGISTER_WEBHOOK` | Register Webhook | Tool to register a new webhook endpoint for LLMWhisperer async notifications. Use when you need to set up a callback URL to receive processing results. During registration, a test payload is sent to verify the webhook endpoint is functioning and returns HTTP 200. |
| `LLMWHISPERER_UPDATE_WEBHOOK_CONFIGURATION` | Update Webhook Configuration | Tool to update an existing webhook configuration for document conversion callbacks. Use when you need to modify the callback URL, authentication token, or webhook identifier. The system validates the webhook by sending a test payload and requires a 200 status response. |
| `LLMWHISPERER_CHECK_WHISPER_STATUS` | Check Whisper Status | Tool to check the status of a text extraction process in LLMWhisperer. Use when the conversion is done in async mode to poll for completion status. |
| `LLMWHISPERER_CONVERT_DOCUMENT_TO_TEXT` | Convert Document to Text | Tool to convert PDFs and scanned documents into LLM-optimized text format asynchronously. Use when you need to extract text from documents for LLM processing. After submission, use the returned whisper_hash to poll status and retrieve converted text. Either document_url (with url_in_post=true) or document_content must be provided. |
| `LLMWHISPERER_GET_WHISPER_DETAIL` | Get Whisper Detail | Tool to retrieve comprehensive details about ongoing or completed text extraction process. Use when you need to monitor the status and progress metrics of a text extraction job. |
| `LLMWHISPERER_RETRIEVE_WHISPER_TEXT` | Retrieve Whisper Text | Tool to retrieve extracted text from asynchronous whisper processing. Use when the conversion process was initiated in async mode and you need to retrieve the results using the whisper_hash identifier. Note that retrieval is single-use for security - once retrieved, the same whisper_hash cannot be used again. |

## Supported Triggers

None listed.

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

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

  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 LLMWhisperer 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 llmwhisperer, 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 LLMWhisperer 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: ["llmwhisperer"],
  });

  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 llmwhisperer, 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 LLMWhisperer 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 LLMWhisperer MCP Agent with another framework

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

## Related Toolkits

- [Google Drive](https://composio.dev/toolkits/googledrive) - Google Drive is a cloud storage platform for uploading, sharing, and collaborating on files. It's perfect for keeping your documents accessible and organized across devices.
- [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.
- [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed.
- [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.
- [Google Docs](https://composio.dev/toolkits/googledocs) - Google Docs is a cloud-based word processor that enables document creation and real-time collaboration. Its seamless sharing and version history make team editing and content management a breeze.
- [Google Super](https://composio.dev/toolkits/googlesuper) - Google Super is an all-in-one suite combining Gmail, Drive, Calendar, Sheets, Analytics, and more. It gives you a unified platform to manage your digital life, boosting productivity and organization.
- [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.
- [Google Tasks](https://composio.dev/toolkits/googletasks) - Google Tasks is a to-do list and task management tool integrated into Gmail and Google Calendar. It helps you organize, track, and complete tasks across your Google ecosystem.
- [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.
- [Composio search](https://composio.dev/toolkits/composio_search) - Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service.
- [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.
- [Perplexityai](https://composio.dev/toolkits/perplexityai) - Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows.
- [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow.
- [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.
- [Affinda](https://composio.dev/toolkits/affinda) - Affinda is an AI-powered document processing platform that automates data extraction from resumes, invoices, and more. It streamlines document-heavy workflows by turning files into structured, actionable data.
- [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.
- [Agility cms](https://composio.dev/toolkits/agility_cms) - Agility CMS is a headless content management system for building and managing digital experiences across platforms. It lets teams update content quickly and deliver omnichannel experiences with ease.

## Frequently Asked Questions

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

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

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

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

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