How to integrate Nasdaq MCP with Pydantic AI

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Introduction

This guide walks you through connecting Nasdaq to Pydantic AI 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, find nasdaq tables updated today through natural language commands.

This guide will help you understand how to give your Pydantic AI 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.

TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Nasdaq
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Nasdaq workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

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 & Triggers

Tools
Get Datatable Metadata (All Symbols)Retrieves metadata for a specified datatable, which includes column definitions and potentially example data containing symbols.
Get Analyst Ratings and Target PricesRetrieves analyst ratings and target prices for a specific symbol using zacks datatables (zacks/ar for ratings, zacks/tp for target prices).
Get Dividend HistoryRetrieves dividend history for a specific symbol.
Get Real-Time QuoteRetrieves real-time quote data for a specific symbol.
Get Specific Table RowRetrieves a specific row from a table by filtering on a specified column and value.
Get Tables by Update DateRetrieves all tables updated on a specific date by fetching all tables and filtering them.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router 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 Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router 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

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active API key
  • Basic familiarity with Python and async programming

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.

Install dependencies

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Nasdaq
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Nasdaq
  • MCPServerStreamableHTTP connects to the Nasdaq MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Nasdaq
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["nasdaq"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
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 session.mcp.url is the MCP server URL that your agent will use

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
nasdaq_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[nasdaq_mcp],
    instructions=(
        "You are a Nasdaq assistant. Use Nasdaq tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Nasdaq endpoint
  • The agent uses GPT-5 to interpret user commands and perform Nasdaq operations
  • The instructions field defines the agent's role and behavior

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Nasdaq.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Nasdaq API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Nasdaq and Pydantic AI:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Nasdaq
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["nasdaq"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    nasdaq_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[nasdaq_mcp],
        instructions=(
            "You are a Nasdaq assistant. Use Nasdaq tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Nasdaq.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've built a Pydantic AI agent that can interact with Nasdaq through Composio's Tool Router. With this setup, your agent can perform real Nasdaq actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Nasdaq for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.

How to build Nasdaq MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Nasdaq MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Nasdaq MCP with Claude Agent SDK

Google ADK

Google ADK

Use Nasdaq MCP with Google ADK

LangChain

LangChain

Use Nasdaq MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Nasdaq MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Nasdaq MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Nasdaq MCP with LlamaIndex

CrewAI

CrewAI

Use Nasdaq MCP with CrewAI

FAQ

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 Pydantic AI?

Yes, you can. Pydantic AI 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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