How to integrate Token metrics MCP with LangChain

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Introduction

This guide walks you through connecting Token metrics to LangChain using the Composio tool router. By the end, you'll have a working Token metrics agent that can show real-time price for ethereum, list top 10 tokens by market cap, get technical indicators for bitcoin hourly, fetch latest trading signals for solana through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Token metrics account through Composio's Token metrics 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 Token metrics project to Composio
  • Create a Tool Router MCP session for Token metrics
  • Initialize an MCP client and retrieve Token metrics tools
  • Build a LangChain agent that can interact with Token metrics
  • 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 Token metrics MCP server, and what's possible with it?

The Token metrics MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Token Metrics account. It provides structured and secure access to real-time cryptocurrency data and analytics, so your agent can retrieve token prices, analyze market trends, surface technical indicators, and provide actionable trading signals on your behalf.

  • Real-time price and market data: Instantly get up-to-date prices, trading volumes, and market caps for any supported cryptocurrency, enabling smart portfolio management and trading decisions.
  • Comprehensive token listings: Fetch a paginated list of supported crypto tokens, including metadata like price, supply, and contract details, to stay on top of the ever-evolving market.
  • On-demand technical analysis: Retrieve technical indicators for any token and interval, so your agent can offer in-depth charting and analysis to guide investment strategies.
  • Automated trading signals: Access AI-powered crypto trading entry and exit signals, helping automate or optimize your trading strategies based on actionable insights.
  • Market cap leaderboards: Easily surface the top cryptocurrencies by market capitalization to monitor market trends, discover new opportunities, or rebalance your holdings.

Supported Tools & Triggers

Tools
Get PriceTool to retrieve real-time price and market metrics for a given cryptocurrency.
Get Technical IndicatorsTool to retrieve technical indicators for a token.
Get Top Market Cap TokensTool to retrieve a list of tokens ranked by market capitalization.
Get Trading SignalsTool to retrieve entry and exit crypto trading signals.
Get TokensTool to retrieve a paginated list of supported tokens with metadata.

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 Token metrics 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 Token metrics tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

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

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Token metrics 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 Token metrics tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "token_metrics-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 Token metrics MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Token metrics 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 Token metrics 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 Token metrics 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=['token_metrics']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "token_metrics-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 Token metrics 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 Token metrics 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 Token metrics MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Token metrics MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Token metrics MCP with Claude Agent SDK

Google ADK

Google ADK

Use Token metrics MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Token metrics MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Token metrics MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Token metrics MCP with LlamaIndex

CrewAI

CrewAI

Use Token metrics MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Token metrics MCP?

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

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

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

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