How to integrate Simple analytics MCP with LangChain

This guide walks you through connecting Simple analytics to LangChain using the Composio tool router. By the end, you'll have a working Simple analytics agent that can show visitor stats for your main website, export last month's raw traffic data, list all websites linked to your account through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Simple analytics account through Composio's Simple analytics MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Simple Analytics is a privacy-friendly web analytics platform with no cookies or trackers. It delivers straightforward website insights that respect user privacy.

4 Tools

Introduction

This guide walks you through connecting Simple analytics to LangChain using the Composio tool router. By the end, you'll have a working Simple analytics agent that can show visitor stats for your main website, export last month's raw traffic data, list all websites linked to your account through natural language commands.

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

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

Also integrate Simple analytics with

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TL;DR

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

The Simple analytics MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Simple Analytics account. It provides structured and secure access to privacy-friendly analytics data, so your agent can perform actions like retrieving website stats, exporting raw data, and listing all your tracked websites with ease.

  • List all connected websites: Quickly get a comprehensive list of all websites associated with your Simple Analytics account, making it easy for your agent to select and manage different sites.
  • Export raw analytics data: Direct your agent to export detailed raw data points—like page views and events—for any website, ready for custom analysis or sharing in CSV or JSON formats.
  • Retrieve key website metrics: Ask your agent to fetch aggregated statistics for a specific site, including visitors, pageviews, bounce rate, and other essential metrics in one go.
  • Automated traffic reporting: Let your agent generate periodic or on-demand traffic reports, summarizing recent performance trends across your sites.

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 Simple analytics 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 Simple analytics 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: ['simple_analytics']
    }
);

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

Configure the agent with the MCP URL

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

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

List Websites (Admin API)

Tool to list all websites associated with the authenticated user.

Export Raw Data Points

Tool to export raw data points (page views and events) for a specific website.

Get aggregated website stats

Tool to retrieve aggregated statistics for a specified website.

Send Server-Side Event

Tool to submit server-side events or pageviews to Simple Analytics.

FRAMEWORKS

How to build Simple analytics MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Simple analytics MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Simple analytics MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Simple analytics MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Simple analytics MCP with Claude Cowork

Codex logo

Codex

Use Simple analytics MCP with Codex

OpenClaw logo

OpenClaw

Use Simple analytics MCP with OpenClaw

Hermes logo

Hermes

Use Simple analytics MCP with Hermes

CLI logo

CLI

Use Simple analytics MCP with CLI

Google ADK logo

Google ADK

Use Simple analytics MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Simple analytics MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Simple analytics MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Simple analytics MCP with LlamaIndex

CrewAI logo

CrewAI

Use Simple analytics MCP with CrewAI

FAQ

Frequently asked questions

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

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

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