How to integrate Genderapi io MCP with LangChain

This guide walks you through connecting Genderapi io to LangChain using the Composio tool router. By the end, you'll have a working Genderapi io agent that can determine gender from a customer email address, infer gender based on a given username, get gender prediction for full names in a csv through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Genderapi io account through Composio's Genderapi io MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Genderapi io is an API that determines gender from names, emails, or usernames. Instantly enrich your data and personalize user experiences with accurate gender prediction.

5 Tools

Introduction

This guide walks you through connecting Genderapi io to LangChain using the Composio tool router. By the end, you'll have a working Genderapi io agent that can determine gender from a customer email address, infer gender based on a given username, get gender prediction for full names in a csv through natural language commands.

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

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

Also integrate Genderapi 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 Genderapi io project to Composio
  • Create a Tool Router MCP session for Genderapi io
  • Initialize an MCP client and retrieve Genderapi io tools
  • Build a LangChain agent that can interact with Genderapi 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 Genderapi io MCP server, and what's possible with it?

The Genderapi 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 Genderapi io account. It provides structured and secure access to gender identification services, so your agent can perform actions like inferring gender from names, emails, or usernames, checking usage statistics, and validating API errors on your behalf.

  • Gender inference from first names: Your agent can determine the likely gender associated with a given first name, supporting localization for more accurate results.
  • Gender prediction from email addresses: Easily infer gender from a provided email address, enabling smart personalization workflows after obtaining proper consent.
  • Analyze usernames and full names for gender: Let your agent deduce gender from usernames, nicknames, or full names—even when only partial information is available.
  • Account usage and API credit monitoring: Check remaining GenderAPI.io credits and expiry dates so you never run out of quota unexpectedly.
  • Comprehensive error code listing: Retrieve and understand all possible API error codes to streamline debugging and ensure robust integrations.

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 Genderapi 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 Genderapi 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: ['genderapi_io']
    }
);

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

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "genderapi_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 Genderapi io MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Genderapi 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 Genderapi 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 Genderapi 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: ['genderapi_io']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "genderapi_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 Genderapi 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 Genderapi 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 Genderapi io action and event your agent gets out of the box.

Gender API Get Statistics

Tool to retrieve account usage statistics from GenderAPI.

Query Gender by First Name

Tool to determine gender by querying first name.

Get Gender from Username

Tool to determine gender from a username or nickname.

List Gender API Error Codes

Tool to list all possible error codes returned by Gender API.

Query gender by email address

Tool to determine gender from an email address.

FRAMEWORKS

How to build Genderapi io MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Genderapi io MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Genderapi io MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Genderapi io MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Genderapi io MCP with Claude Cowork

Codex logo

Codex

Use Genderapi io MCP with Codex

OpenClaw logo

OpenClaw

Use Genderapi io MCP with OpenClaw

Hermes logo

Hermes

Use Genderapi io MCP with Hermes

CLI logo

CLI

Use Genderapi io MCP with CLI

Google ADK logo

Google ADK

Use Genderapi io MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Genderapi io MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Genderapi io MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Genderapi io MCP with LlamaIndex

CrewAI logo

CrewAI

Use Genderapi io MCP with CrewAI

FAQ

Frequently asked questions

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

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

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