How to integrate Polygon MCP with Pydantic AI

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Introduction

This guide walks you through connecting Polygon to Pydantic AI using the Composio tool router. By the end, you'll have a working Polygon agent that can show the latest closing price for btc-usd, list all stock tickers traded on nasdaq, get the crypto rsi for eth over 14 days, check if u.s. stock markets are open right now through natural language commands.

This guide will help you understand how to give your Pydantic AI agent real control over a Polygon account through Composio's Polygon 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 Polygon
  • 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 Polygon 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 Polygon MCP server, and what's possible with it?

The Polygon MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Polygon account. It provides structured and secure access to real-time and historical financial market data, so your agent can retrieve market status, analyze price indicators, track dividends, and monitor crypto and stock tickers on your behalf.

  • Market status and holiday tracking: Instantly check if U.S. exchanges are open or closed and retrieve upcoming market holidays or early closures to plan trading activities.
  • Comprehensive ticker retrieval: Ask your agent to fetch all available ticker symbols across asset classes, including stocks, crypto, forex, and options, filtered by market or exchange.
  • Crypto technical analysis: Have your agent calculate moving averages (EMA, SMA), MACD, and RSI indicators for specific cryptocurrencies to support informed trading decisions.
  • Daily and historical price fetching: Let your agent retrieve daily open/close prices, previous day’s close, or detailed price history for cryptocurrencies and stocks with just a prompt.
  • Dividend data extraction: Effortlessly access up-to-date corporate dividend information for stocks, enabling portfolio reviews and income analysis.

Supported Tools & Triggers

Tools
Get All TickersTool to retrieve all ticker symbols across asset classes.
Get Crypto Exponential Moving Average (EMA)Tool to retrieve exponential moving average (ema) for a cryptocurrency ticker.
Get Crypto MACD IndicatorTool to retrieve the macd (moving average convergence/divergence) for a crypto ticker.
Get Crypto RSI IndicatorTool to retrieve the relative strength index (rsi) for a crypto ticker.
Get Crypto Simple Moving Average (SMA)Tool to retrieve the simple moving average (sma) for a given crypto ticker.
Get Crypto Open/CloseTool to fetch daily open and close prices for a given crypto pair on a specified date.
Get Crypto Previous CloseTool to retrieve previous day’s close for a crypto ticker.
Get DividendsTool to retrieve dividend data for stocks.
Get Upcoming Market HolidaysTool to retrieve upcoming market holidays and half-day closures.
Get Market StatusTool to retrieve current market status.
Get NewsTool to retrieve recent news articles related to a ticker.
Get reference conditionsTool to retrieve market condition code mappings.
Get reference exchangesTool to retrieve supported exchanges and their details.
Get Stock SplitsTool to retrieve stock split events.
Get Exponential Moving Average (EMA)Tool to fetch exponential moving average (ema) for a given stock ticker.
Get Simple Moving Average (SMA)Tool to fetch simple moving average (sma) for a given stock ticker.
Get Ticker DetailsTool to retrieve detailed information for a ticker.
Get Ticker TypesTool to retrieve all ticker types supported by polygon.

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 Polygon
  • 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 Polygon
  • MCPServerStreamableHTTP connects to the Polygon 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 Polygon
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["polygon"],
    )
    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 Polygon 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
polygon_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[polygon_mcp],
    instructions=(
        "You are a Polygon assistant. Use Polygon tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Polygon endpoint
  • The agent uses GPT-5 to interpret user commands and perform Polygon 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 Polygon.\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
  • Polygon 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 Polygon 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 Polygon
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["polygon"],
    )
    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
    polygon_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[polygon_mcp],
        instructions=(
            "You are a Polygon assistant. Use Polygon 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 Polygon.\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 Polygon through Composio's Tool Router. With this setup, your agent can perform real Polygon 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 + Polygon 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 Polygon MCP Agent with another framework

FAQ

What are the differences in Tool Router MCP and Polygon MCP?

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

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

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

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Letta
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