How to integrate Amcards MCP with LangChain

This guide walks you through connecting Amcards to LangChain using the Composio tool router. By the end, you'll have a working Amcards agent that can list all cards i've sent this month, find contacts with the last name smith, show newest contacts added last week through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Amcards account through Composio's Amcards MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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AMCards lets you create and mail personalized greeting cards online. Build stronger customer relationships with easy, automated card campaigns.

10 Tools

Introduction

This guide walks you through connecting Amcards to LangChain using the Composio tool router. By the end, you'll have a working Amcards agent that can list all cards i've sent this month, find contacts with the last name smith, show newest contacts added last week through natural language commands.

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

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

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

The Amcards MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Amcards account. It provides structured and secure access to your card library and contact list, so your agent can retrieve cards, manage contacts, monitor campaigns, and streamline customer engagement on your behalf.

  • Card library management: Instantly retrieve a list of all greeting cards in your Amcards account so you can review, organize, or select cards for campaigns.
  • Comprehensive contact retrieval: Ask your agent to fetch your contact list—including names, emails, and details—making it easy to personalize outreach or update records.
  • Filtered contact lookup: Find specific contacts by searching with filters like last name, first name, or email, allowing you to target the right recipients quickly.
  • Campaign preparation and monitoring: Let your agent pull card and contact data to support mailing campaigns, track card activity, and optimize your customer connection strategy.

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 Amcards 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 Amcards 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: ['amcards']
    }
);

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

Configure the agent with the MCP URL

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

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "amcards-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 Amcards 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 Amcards 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 Amcards action and event your agent gets out of the box.

Get API Schema

Tool to retrieve the entire schema for AMCards API V1.

Get Cards

This tool retrieves a list of all cards associated with the authenticated account.

Get Category

Tool to retrieve details of a specific category by ID.

Get Category Schema

Tool to retrieve the schema for Category resource.

Get Contacts

This tool retrieves a list of contacts from AMCards.

Get Gift

Tool to retrieve details of a specific gift by ID.

Get Public Template

Tool to retrieve details of a specific public template by ID.

List Categories

Tool to retrieve a list of card template categories from AMCards.

List Gifts

Tool to retrieve a list of Gift items offered by AMcards.

List Public Templates

Tool to retrieve a list of public templates available to all users.

FRAMEWORKS

How to build Amcards MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Amcards MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Amcards MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Amcards MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Amcards MCP with Claude Cowork

Codex logo

Codex

Use Amcards MCP with Codex

OpenClaw logo

OpenClaw

Use Amcards MCP with OpenClaw

Hermes logo

Hermes

Use Amcards MCP with Hermes

CLI logo

CLI

Use Amcards MCP with CLI

Google ADK logo

Google ADK

Use Amcards MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Amcards MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Amcards MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Amcards MCP with LlamaIndex

CrewAI logo

CrewAI

Use Amcards MCP with CrewAI

FAQ

Frequently asked questions

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

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

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