How to integrate Polygon MCP with LangChain

Framework Integration Gradient
Polygon Logo
LangChain Logo
divider

Introduction

This guide walks you through connecting Polygon to LangChain 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 LangChain 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:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Polygon project to Composio
  • Create a Tool Router MCP session for Polygon
  • Initialize an MCP client and retrieve Polygon tools
  • Build a LangChain agent that can interact with Polygon
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

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 this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI 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

pip install composio-langchain langchain-mcp-adapters langchain python-dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • composio-langchain provides Composio integration for LangChain
  • langchain-mcp-adapters enables MCP client connections
  • langchain is the core agent framework
  • python-dotenv loads environment variables

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models

Import dependencies

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Polygon functionality through MCP

Initialize Composio client

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Polygon tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Polygon
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['polygon']
)

url = session.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
  • This approach allows the agent to dynamically load and use Polygon tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "polygon-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Polygon MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Polygon tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Polygon related question or task to the agent.\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

Here's the complete code to get you started with Polygon and LangChain:

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['polygon']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "polygon-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Polygon related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

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

Conclusion

You've successfully built a LangChain agent that can interact with Polygon through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

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 LangChain?

Yes, you can. LangChain 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.

Used by agents from

Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai

Never worry about agent reliability

We handle tool reliability, observability, and security so you never have to second-guess an agent action.