# How to integrate Nasdaq MCP with Vercel AI SDK v6

```json
{
  "title": "How to integrate Nasdaq MCP with Vercel AI SDK v6",
  "toolkit": "Nasdaq",
  "toolkit_slug": "nasdaq",
  "framework": "Vercel AI SDK",
  "framework_slug": "ai-sdk",
  "url": "https://composio.dev/toolkits/nasdaq/framework/ai-sdk",
  "markdown_url": "https://composio.dev/toolkits/nasdaq/framework/ai-sdk.md",
  "updated_at": "2026-05-12T10:19:51.814Z"
}
```

## Introduction

This guide walks you through connecting Nasdaq to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Nasdaq agent that can get real-time quote for aapl stock, show analyst ratings for tsla this week, retrieve dividend history for msft through natural language commands.
This guide will help you understand how to give your Vercel AI SDK agent real control over a Nasdaq account through Composio's Nasdaq MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Nasdaq with

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

## TL;DR

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

The Nasdaq MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Nasdaq Data Link account. It provides structured and secure access to real-time financial data, analyst ratings, dividend histories, and more, so your agent can fetch live quotes, analyze trends, retrieve historical performance, and power your financial insights automatically.
- Real-time stock quote retrieval: Instantly get up-to-the-second price quotes and market data for any supported symbol, enabling timely investment decisions and market monitoring.
- Analyst ratings and target price lookup: Access the latest analyst recommendations and price targets for specific symbols to inform your trading strategies and research.
- Historical dividend tracking: Retrieve detailed dividend history for any stock, supporting portfolio income analysis and historical return calculations.
- Table metadata and structure exploration: Let your agent fetch and understand datatable schemas, making it easy to build data-driven applications with accurate column definitions and sample data.
- Targeted data row and table queries: Pull specific rows or tables updated on certain dates, supporting custom research, analysis, and reporting workflows.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `NASDAQ_GET_ANALYST_RATINGS` | Get Analyst Ratings and Target Prices | Retrieves comprehensive analyst ratings and target price data for a stock symbol from Zacks Investment Research. Returns current and historical (1-3 months ago) rating counts across all rating categories (Strong Buy, Buy, Hold, Sell, Strong Sell), mean rating scores, and consensus target price estimates including mean, median, high, low, standard deviation, and revision counts. Also provides historical target price data with observation dates showing how analyst estimates have changed over time. Data sources: ZACKS/AR (analyst ratings) and ZACKS/TP (target prices historical data). |
| `NASDAQ_GET_DATATABLE` | Get Datatable Bulk Export | Tool to request a bulk export of a NASDAQ Data Link datatable. Returns a download link for a zipped CSV file containing the entire table data, bypassing the standard 10,000 row limit. Use this when you need to download large datasets in full. Check the response status: if 'fresh', the file is ready for download; if 'creating' or 'regenerating', wait and retry the request. The download link is valid for 30 minutes only. |
| `NASDAQ_GET_DATATABLE_METADATA` | Get Datatable Metadata | Tool to retrieve metadata for a NASDAQ Data Link datatable. Returns complete schema information including column definitions, data types, filterable columns, primary keys, premium status, and refresh schedule. Use when you need to understand the structure and availability of a datatable before querying its data. |
| `NASDAQ_GET_DIVIDEND_HISTORY` | Get Dividend Fundamentals | Retrieves quarterly dividend-related fundamentals for a specific stock ticker from SHARADAR SF1 database. Returns dividend metrics including DPS (dividend per share), dividend yield, payout ratio, and cash flow from dividends. Data is quarterly (dimension=ARQ) and sourced from company financial statements. Note: This endpoint accesses the premium SHARADAR SF1 database which may require subscription access. |
| `NASDAQ_GET_REAL_TIME_QUOTE` | Get Stock Price Data | Retrieves end-of-day historical stock price data for a specific ticker symbol from the QUOTEMEDIA database. Returns OHLC (Open, High, Low, Close) prices, volume, and dividend/split information. Note: This provides historical end-of-day data, not live real-time quotes. |
| `NASDAQ_GET_TABLE_ROW` | Get Table Row By Filter | Retrieves rows from a NASDAQ Data Link datatable by filtering on a specified column and value. This action queries NASDAQ Data Link datatables (e.g., SHARADAR/SF1 for fundamental data, SHARADAR/TICKERS for ticker metadata) and returns rows matching the filter criteria. The API may return multiple rows if the filter isn't unique (e.g., filtering ticker='AAPL' in SF1 returns multiple fiscal periods). Use the 'columns' parameter to limit returned fields. Common use cases: - Get fundamental data for a stock: datacode='SHARADAR', datatable_code='SF1', filter_column_name='ticker' - Get ticker metadata: datacode='SHARADAR', datatable_code='TICKERS', filter_column_name='ticker' - Get price data: datacode='SHARADAR', datatable_code='SEP', filter_column_name='ticker' |
| `NASDAQ_GET_TABLES_BY_DATE` | Get Datatables Updated on Specific Date | Retrieves Nasdaq Data Link datatables (databases) that were last updated on a specific date. This action fetches all available datatables from Nasdaq Data Link's catalog and filters them by their last update timestamp to return only those updated on the target date. Useful for discovering recently updated data sources. |

## Supported Triggers

None listed.

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

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

  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 Nasdaq 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 nasdaq, 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 Nasdaq 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: ["nasdaq"],
  });

  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 nasdaq, 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 Nasdaq 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 Nasdaq MCP Agent with another framework

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

## Related Toolkits

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- [Benzinga](https://composio.dev/toolkits/benzinga) - Benzinga provides real-time financial news and data APIs for market coverage. It helps you track breaking news and actionable market insights instantly.
- [Brex](https://composio.dev/toolkits/brex) - Brex provides corporate credit cards and spend management tailored for startups and tech businesses. It helps optimize company cash flow, streamline accounting, and accelerate business growth.
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- [Coinranking](https://composio.dev/toolkits/coinranking) - Coinranking is a comprehensive cryptocurrency market data platform offering access to real-time coin prices, market caps, and historical data. Get accurate, up-to-date stats for thousands of digital assets in one place.
- [Coupa](https://composio.dev/toolkits/coupa) - Coupa is a business spend management platform for procurement, invoicing, and expenses. It helps organizations streamline purchasing, control costs, and gain complete visibility over financial operations.
- [CurrencyScoop](https://composio.dev/toolkits/currencyscoop) - CurrencyScoop is a developer-friendly API for real-time and historical currency exchange rates. Easily access fiat and crypto data for smart, up-to-date financial applications.
- [Daffy](https://composio.dev/toolkits/daffy) - Daffy is a modern charitable giving platform with a donor-advised fund. Easily set aside funds, grow them tax-free, and donate to over 1.7 million U.S. charities.
- [Eagle doc](https://composio.dev/toolkits/eagle_doc) - Eagle doc is an AI-powered OCR API for invoices and receipts. It delivers fast, reliable, and accurate document data extraction for seamless automation.
- [Elorus](https://composio.dev/toolkits/elorus) - Elorus is an online invoicing and time-tracking software for freelancers and small businesses. Easily manage finances, bill clients, and track work in one place.
- [Eodhd apis](https://composio.dev/toolkits/eodhd_apis) - Eodhd apis delivers comprehensive financial data, including live and historical stock prices, via robust APIs. Easily access reliable, up-to-date market insights to power your apps, dashboards, and analytics.
- [Fidel api](https://composio.dev/toolkits/fidel_api) - Fidel api is a secure platform for linking payment cards to web and mobile apps. It enables real-time card transaction monitoring and event-based automation for businesses.
- [Finage](https://composio.dev/toolkits/finage) - Finage is a secure API platform delivering real-time and historical financial data for stocks, forex, crypto, indices, and commodities. It empowers developers and businesses to access, analyze, and act on market data instantly.
- [Finmei](https://composio.dev/toolkits/finmei) - Finmei is an invoicing tool that simplifies billing, invoice management, and expense tracking. Ideal for automating and organizing your business finances in one place.
- [Fixer](https://composio.dev/toolkits/fixer) - Fixer is a currency data API offering real-time and historical exchange rates for 170 currencies. Instantly access accurate, up-to-date forex data for your applications and workflows.
- [Fixer io](https://composio.dev/toolkits/fixer_io) - Fixer.io is a lightweight API for real-time and historical foreign exchange rates. It makes global currency conversion fast, accurate, and hassle-free.

## Frequently Asked Questions

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

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

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

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

---
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