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How to integrate Better proposals MCP with LangChain

Connect LangChain to Better proposals MCP. List all proposals sent this month, create a new company profile, and more using natural language, with authentication handled for you.

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Api Key

Better Proposals is a web-based tool for crafting and sending professional proposals. It helps teams impress clients and close deals faster with slick, easy-to-use templates.

23 Tools

Introduction

This guide walks you through connecting Better proposals to LangChain using the Composio tool router. By the end, you'll have a working Better proposals agent that can track sent and signed proposals, fetch quotes and templates, create companies and proposal covers, and check your brand settings through natural language commands.

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

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

For exact tool slugs and parameter tables, see the tool catalog in the Composio docs.

Also integrate Better proposals with

Claude CodeClaude CoworkOpenAI Agents SDKClaude Agent SDKCodexOpenClawHermesAtomic AgentGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAIPydantic AIAutoGen

TL;DR

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

The Better Proposals MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Better Proposals account. It provides structured and secure access to your proposals, quotes, companies, templates, and settings, so your agent can track proposal progress, review quotes, inspect templates, create companies, and check account settings on your behalf.

  • Proposal tracking: Have your agent list proposals, count them, retrieve specific proposal details, and find proposals that are new, opened, sent, signed, or paid.
  • Quote review: Instruct your agent to list available quotes and retrieve the details of a specific quote.
  • Company management: Let the agent create new companies, list existing companies, and retrieve details about a specific company.
  • Template and document setup: Direct your agent to inspect templates and document types, create new document types, or add proposal cover designs.
  • Workspace configuration lookup: Have your agent retrieve account and brand settings, review supported currencies, and list custom merge tags.

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 Better proposals 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 Better proposals 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: ['better_proposals']
    }
);

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

Configure the agent with the MCP URL

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

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

Create Company

Tool to create a new company.

Create Document Type

Tool to create a new document type.

Create Proposal Cover

Tool to create a new proposal cover design.

Get All Companies

Tool to retrieve a paginated list of all companies.

Get All Currencies

Tool to retrieve a paginated list of all currencies.

Get All Document Types

Tool to retrieve a paginated list of all document types.

Get All Proposals

Tool to retrieve a paginated list of all proposals.

Get All Quotes

Tool to retrieve a paginated list of all quotes.

Get All Templates

Tool to retrieve a paginated list of all templates.

Get Brand Settings

Retrieve settings for the default brand, including its name, currency and tax configuration, for use when creating or customizing documents.

Get Company

Tool to retrieve details of a specific company.

Get Currency

Retrieve details of a specific Better Proposals currency by its ID.

Get New Proposals

Retrieve a paginated list of new proposals, optionally filtered by document type.

Get Opened Proposals

Retrieve a paginated list of proposals that recipients have opened, optionally filtered by document type.

Get Paid Proposals

Retrieve a paginated list of paid proposals, optionally filtered by document type.

Get Proposal

Retrieve details of a specific proposal by ID, including available metadata, contacts and price tables.

Get Proposal Count

Tool to retrieve the total count of proposals.

Get Quote

Tool to retrieve details of a specific quote.

Get Sent Proposals

Retrieve a page of sent proposals, optionally filtered by document type.

Get Settings

Tool to retrieve current account settings.

Get Signed Proposals

Retrieve a page of signed proposals, optionally filtered by document type.

Get Template Details

Tool to retrieve details of a specific template.

List Merge Tags

Tool to retrieve custom merge tags with pagination support.

FRAMEWORKS

How to build Better proposals MCP Agent with another framework

Claude Code logo

Claude Code

Use Better proposals MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Better proposals MCP with Claude Cowork

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Better proposals MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Better proposals MCP with Claude Agent SDK

Codex logo

Codex

Use Better proposals MCP with Codex

OpenClaw logo

OpenClaw

Use Better proposals MCP with OpenClaw

Hermes logo

Hermes

Use Better proposals MCP with Hermes

Atomic Agent logo

Atomic Agent

Use Better proposals MCP with Atomic Agent

Google ADK logo

Google ADK

Use Better proposals MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Better proposals MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Better proposals MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Better proposals MCP with LlamaIndex

CrewAI logo

CrewAI

Use Better proposals MCP with CrewAI

Pydantic AI logo

Pydantic AI

Use Better proposals MCP with Pydantic AI

AutoGen logo

AutoGen

Use Better proposals MCP with AutoGen

FAQ

Frequently asked questions

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