How to integrate Ip2location io MCP with LangChain

This guide walks you through connecting Ip2location io to LangChain using the Composio tool router. By the end, you'll have a working Ip2location io agent that can find location details for this ip address, get whois info for a specific domain, list all domains hosted on this ip through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Ip2location io account through Composio's Ip2location io MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Ip2location io is a fast and accurate IP geolocation API service that pinpoints user location and network info. It’s perfect for customizing user experiences, fraud detection, and security automation.

5 Tools

Introduction

This guide walks you through connecting Ip2location io to LangChain using the Composio tool router. By the end, you'll have a working Ip2location io agent that can find location details for this ip address, get whois info for a specific domain, list all domains hosted on this ip through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Ip2location io account through Composio's Ip2location io MCP server.

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

Also integrate Ip2location io with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Ip2location io project to Composio
  • Create a Tool Router MCP session for Ip2location io
  • Initialize an MCP client and retrieve Ip2location io tools
  • Build a LangChain agent that can interact with Ip2location io
  • 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 Ip2location io MCP server, and what's possible with it?

The Ip2location io MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Ip2location io account. It provides structured and secure access to robust IP geolocation and domain intelligence tools, so your agent can perform actions like locating users by IP, running bulk geolocation, performing WHOIS lookups, and discovering hosted domains on your behalf.

  • Bulk IP geolocation analysis: Instantly process and retrieve geolocation data for up to 1000 IP addresses at once, helping you analyze user locations at scale.
  • Domain WHOIS information retrieval: Ask your agent to fetch registration and contact details for any domain, making it easy to verify ownership or investigate web properties.
  • Hosted domain discovery by IP: Have your agent list all domains hosted on a specific IP address, supporting network analysis and infrastructure mapping tasks.
  • API key management assistance: Let your agent securely retrieve and use your configured API key to authenticate requests, streamlining connection setup and ongoing access.

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

Step by step10 STEPS
1

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
2

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.
3

Install dependencies

npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters 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/core is the core agent framework
  • dotenv/config loads environment variables
4

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
5

Import dependencies

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv/config import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Ip2location io functionality through MCP
6

Initialize Composio client

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
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 Ip2location io tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding
7

Create a Tool Router session

const session = await composio.create(
    userId as string,
    {
        toolkits: ['ip2location_io']
    }
);

const url = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Ip2location io 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 Ip2location io tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "ip2location_io-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Ip2location io MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Ip2location io tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model
9

Set up interactive chat interface

let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Ip2location io related question or task to the agent.\n");

const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: '
});

rl.prompt();

rl.on('line', async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
        console.log("\nGoodbye!");
        rl.close();
        process.exit(0);
    }

    if (!trimmedInput) {
        rl.prompt();
        return;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
What's happening:
  • We initialize an empty conversationHistory list to maintain context across interactions
  • A readline interface is used to continuously accept 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 invoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully
10

Run the application

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
What's happening:
  • We call the main() function to start the application

Complete Code

Here's the complete code to get you started with Ip2location io and LangChain:

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['ip2location_io']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "ip2location_io-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Ip2location io related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    rl.prompt();

    rl.on('line', async (userInput: string) => {
        const trimmedInput = userInput.trim();
        
        if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
            console.log("\nGoodbye!");
            rl.close();
            process.exit(0);
        }
        
        if (!trimmedInput) {
            rl.prompt();
            return;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});

Conclusion

You've successfully built a LangChain agent that can interact with Ip2location io 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.
TOOLS

Supported Tools

Every Ip2location io action and event your agent gets out of the box.

Bulk IP Geolocation

Tool to retrieve geolocation information for multiple IP addresses in bulk.

Domain WHOIS Lookup

Tool to retrieve WHOIS information for a given domain.

Get API Key

Tool to retrieve the configured API key.

Get IP Geolocation

Tool to retrieve comprehensive geolocation data for an IPv4 or IPv6 address.

IP2WHOIS Hosted Domain

Tool to retrieve hosted domain names by IP address.

FRAMEWORKS

How to build Ip2location io MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Ip2location io MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Ip2location io MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Ip2location io MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Ip2location io MCP with Claude Cowork

Codex logo

Codex

Use Ip2location io MCP with Codex

OpenClaw logo

OpenClaw

Use Ip2location io MCP with OpenClaw

Hermes logo

Hermes

Use Ip2location io MCP with Hermes

CLI logo

CLI

Use Ip2location io MCP with CLI

Google ADK logo

Google ADK

Use Ip2location io MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Ip2location io MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Ip2location io MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Ip2location io MCP with LlamaIndex

CrewAI logo

CrewAI

Use Ip2location io MCP with CrewAI

FAQ

Frequently asked questions

With a standalone Ip2location io MCP server, the agents and LLMs can only access a fixed set of Ip2location io tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Ip2location io and many other apps based on the task at hand, all through a single MCP endpoint.

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 Ip2location io tools.

Yes, absolutely. You can configure which Ip2location io 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.

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 Ip2location io data and credentials are handled as safely as possible.

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