How to integrate Interzoid MCP with LangChain

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Introduction

This guide walks you through connecting Interzoid to LangChain using the Composio tool router. By the end, you'll have a working Interzoid agent that can match duplicate customer records by name, verify email addresses in a contact list, enrich company data with industry details through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Interzoid account through Composio's Interzoid MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Interzoid with

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Interzoid project to Composio
  • Create a Tool Router MCP session for Interzoid
  • Initialize an MCP client and retrieve Interzoid tools
  • Build a LangChain agent that can interact with Interzoid
  • 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 Interzoid MCP server, and what's possible with it?

The Interzoid MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Interzoid account. It provides structured and secure access to Interzoid's powerful data quality APIs, so your agent can perform actions like matching records, verifying data, enriching information, and analyzing datasets on your behalf.

  • Data matching and deduplication: Let your agent detect and merge duplicate records across datasets using fuzzy and advanced matching algorithms.
  • Real-time data verification: Have the agent verify email addresses, phone numbers, and other key data points to ensure accuracy and reliability.
  • Data enrichment and augmentation: Automatically enhance your records with additional company, contact, or geographic information pulled from Interzoid's enrichment APIs.
  • Similarity scoring and analysis: Enable your agent to compare names, addresses, or other fields for similarity, helping with record linkage or fraud detection.
  • Automated quality checks: Easily set up workflows where your agent scans new or existing data for quality issues and suggests corrections or improvements.

Supported Tools & Triggers

Tools
Parse AddressTool to parse a free-form address into structured components.
Interzoid Email Trust ScoreTool to return a trust score for an email address.
Get Address Match AdvancedTool to generate a similarity key for a US street address.
Get Area Code InformationTool to retrieve telephone area code information including primary city and geographic locale.
Get Area Code From NumberTool to get area code information from a telephone number.
Get Business InfoTool to retrieve comprehensive company profiles and business intelligence.
Get Company Match AdvancedTool to generate a fuzzy-matching key for an organization name.
Get Country InfoTool to standardize a country name and return metadata like ISO codes, currency, TLD, and calling code.
Get Currency RateTool to retrieve live USD exchange rate for a currency symbol.
Get Custom DataTool to retrieve custom enriched data based on a topic and lookup value.
Get Email InfoTool to validate an email and return enrichment/demographics.
Get Entity TypeTool to classify a text string into an entity type.
Get Executive ProfileTool to retrieve executive profile details based on company and title keywords.
Get Full Name MatchTool to generate a similarity key for a full name.
Get Full Name Match ScoreTool to return a similarity score between two full names.
Get Global Address MatchTool to generate a similarity key for a global address.
Get Global Page Load PerformanceTool to measure page/API load time from a specified global origin.
Get Global WeatherTool to return current weather conditions for a global location.
Get IP ProfileTool to retrieve IP intelligence including ASN, organization, geolocation, and reputation.
Get API License KeyTool to retrieve the configured Interzoid API license key.
Get Name OriginTool to infer the likely country or region of origin from a personal name.
Get Org Match ScoreTool to return a 1–99 match score between two organization names.
Get Org StandardTool to standardize an organization name to a canonical English form.
Get Parent Company InfoTool to retrieve ultimate parent company information.
Get Phone Number ProfileTool to retrieve phone number intelligence including validation, normalization, carrier, and risk assessment.
Get Product MatchTool to generate a similarity key for a product name.
Get Remaining API CreditsTool to retrieve remaining Interzoid API credits.
Get Weather by ZIP CodeTool to get current weather conditions for a US ZIP code.
Identify LanguageTool to detect the language of a text string.
Translate any text (auto-detect language)Tool to auto-detect the input language and translate given text to the specified target language.

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

What is Composio SDK?

Composio's Composio SDK 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 Composio SDK

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

How the Composio SDK works

The Composio SDK 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 Interzoid 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 Interzoid tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

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

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

Configure the agent with the MCP URL

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

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "interzoid-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 Interzoid 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 Interzoid 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.

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FAQ

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

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

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

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

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