How to integrate Better proposals MCP with CrewAI

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Introduction

This guide walks you through connecting Better proposals to CrewAI using the Composio tool router. By the end, you'll have a working Better proposals agent that can list all proposals sent this month, create a new company profile, show available proposal templates for selection, retrieve all quotes for a specific client through natural language commands.

This guide will help you understand how to give your CrewAI 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.

TL;DR

Here's what you'll learn:
  • Get a Composio API key and configure your Better proposals connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Better proposals
  • Build a conversational loop where your agent can execute Better proposals operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

  • Agent Roles: Define specialized agents with specific goals and backstories
  • Task Management: Create tasks with clear descriptions and expected outputs
  • Crew Orchestration: Combine agents and tasks into collaborative workflows
  • MCP Integration: Connect to external tools through Model Context Protocol

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 workspace, so your agent can perform actions like creating companies, generating proposals, listing templates, and managing document types on your behalf.

  • Automated proposal creation: Instruct your agent to generate new proposal covers or assemble entire proposals using customizable templates and dynamic content.
  • Company and client management: Let your agent create new companies, retrieve lists of existing clients, and keep your contacts organized for faster proposal delivery.
  • Template and document type selection: Ask your agent to fetch available templates and document types, helping you choose the right style and format every time.
  • Quote and currency handling: Have your agent list all available quotes and supported currencies, streamlining the pricing and invoicing process for your proposals.
  • Bulk data retrieval and reporting: Direct the agent to gather lists of all proposals, document types, or companies for easy review, reporting, or dashboarding.

Supported Tools & Triggers

Tools
Get All Document TypesTool to retrieve a paginated list of all document types.
Create CompanyTool to create a new company.
Create Document TypeTool to create a new document type.
Create Proposal CoverTool to create a new proposal cover design.
Get All CompaniesTool to retrieve a paginated list of all companies.
Get All CurrenciesTool to retrieve a paginated list of all currencies.
Get All Document TypesTool to retrieve a paginated list of all document types.
Get All ProposalsTool to retrieve a paginated list of all proposals.
Get All QuotesTool to retrieve a paginated list of all quotes.
Get All TemplatesTool to retrieve a paginated list of all templates.
Get Brand SettingsTool to retrieve settings for the default brand.
Get CompanyTool to retrieve details of a specific company.
Get CurrencyTool to retrieve details of a specific currency.
Get New ProposalsTool to retrieve all new proposals.
Get Opened ProposalsTool to retrieve all opened proposals.
Get Paid ProposalsTool to retrieve all paid proposals.
Get Proposal CountTool to retrieve the total count of proposals.
Get QuoteTool to retrieve details of a specific quote.
Get Sent ProposalsTool to retrieve all sent proposals.
Get SettingsTool to retrieve current account settings.
Get Signed ProposalsTool to retrieve all signed proposals.
Get Template DetailsTool to retrieve details of a specific template.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router 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 Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router 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

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Better proposals connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python

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.

Install dependencies

bash
pip install composio crewai crewai-tools python-dotenv
What's happening:
  • composio connects your agent to Better proposals via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools includes MCP helpers
  • python-dotenv loads environment variables from .env

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_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 with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model

Import dependencies

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional import if you plan to adapt tools
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()
What's happening:
  • CrewAI classes define agents and tasks, and run the workflow
  • MCPServerHTTP connects the agent to an MCP endpoint
  • Composio will give you a short lived Better proposals MCP URL

Create a Composio Tool Router session for Better proposals

python
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
    user_id=os.getenv("USER_ID"),
    toolkits=["better_proposals"],
)
url = session.mcp.url
What's happening:
  • You create a Better proposals only session through Composio
  • Composio returns an MCP HTTP URL that exposes Better proposals tools

Configure the LLM

python
llm = LLM(
    model="gpt-5-mini",
    api_key=os.getenv("OPENAI_API_KEY"),
)
What's happening:
  • CrewAI will call this LLM for planning and responses
  • You can swap in a different model if needed

Attach the MCP server and create the agent

python
toolkit_agent = Agent(
    role="Better proposals Assistant",
    goal="Help users interact with Better proposals through natural language commands",
    backstory=(
        "You are an expert assistant with access to Better proposals tools. "
        "You can perform various Better proposals operations on behalf of the user."
    ),
    mcps=[
        MCPServerHTTP(
            url=url,
            streamable=True,
            cache_tools_list=True,
            headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
        ),
    ],
    llm=llm,
    verbose=True,
    max_iter=10,
)
What's happening:
  • MCPServerHTTP connects the agent to the Better proposals MCP endpoint
  • cache_tools_list saves a tools catalog for faster subsequent runs
  • verbose helps you see what the agent is doing

Add a REPL loop with Task and Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to perform Better proposals operations.\n")

conversation_context = ""

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Based on the conversation history:\n{conversation_context}\n\n"
            f"Current user request: {user_input}\n\n"
            f"Please help the user with their Better proposals related request."
        ),
        expected_output="A helpful response addressing the user's request",
        agent=toolkit_agent,
    )

    crew = Crew(
        agents=[toolkit_agent],
        tasks=[task],
        verbose=False,
    )

    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's happening:
  • You build a simple chat loop and keep a running context
  • Each user turn becomes a Task handled by the same agent
  • Crew executes the task and returns a response

Run the application

python
if __name__ == "__main__":
    main()
What's happening:
  • Standard Python entry point so you can run python crewai_better_proposals_agent.py

Complete Code

Here's the complete code to get you started with Better proposals and CrewAI:

python
# file: crewai_better_proposals_agent.py
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()

def main():
    # Initialize Composio and create a Better proposals session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["better_proposals"],
    )
    url = session.mcp.url

    # Configure LLM
    llm = LLM(
        model="gpt-5-mini",
        api_key=os.getenv("OPENAI_API_KEY"),
    )

    # Create Better proposals assistant agent
    toolkit_agent = Agent(
        role="Better proposals Assistant",
        goal="Help users interact with Better proposals through natural language commands",
        backstory=(
            "You are an expert assistant with access to Better proposals tools. "
            "You can perform various Better proposals operations on behalf of the user."
        ),
        mcps=[
            MCPServerHTTP(
                url=url,
                streamable=True,
                cache_tools_list=True,
                headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
            ),
        ],
        llm=llm,
        verbose=True,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Try asking the agent to perform Better proposals operations.\n")

    conversation_context = ""

    while True:
        user_input = input("You: ").strip()

        if user_input.lower() in ["exit", "quit", "bye"]:
            print("\nGoodbye!")
            break

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Based on the conversation history:\n{conversation_context}\n\n"
                f"Current user request: {user_input}\n\n"
                f"Please help the user with their Better proposals related request."
            ),
            expected_output="A helpful response addressing the user's request",
            agent=toolkit_agent,
        )

        crew = Crew(
            agents=[toolkit_agent],
            tasks=[task],
            verbose=False,
        )

        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

if __name__ == "__main__":
    main()

Conclusion

You now have a CrewAI agent connected to Better proposals through Composio's Tool Router. The agent can perform Better proposals operations through natural language commands. Next steps:
  • Add role-specific instructions to customize agent behavior
  • Plug in more toolkits for multi-app workflows
  • Chain tasks for complex multi-step operations

How to build Better proposals MCP Agent with another framework

FAQ

What are the differences in Tool Router MCP and Better proposals MCP?

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

Can I use Tool Router MCP with CrewAI?

Yes, you can. CrewAI 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 Better proposals tools.

Can I manage the permissions and scopes for Better proposals while using Tool Router?

Yes, absolutely. You can configure which Better proposals 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.

How safe is my data with Composio Tool Router?

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 Better proposals data and credentials are handled as safely as possible.

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