How to integrate Givebutter MCP with CrewAI

BOOK A CALLGET STARTED FOR FREE
Framework Integration Gradient
Givebutter Logo
CrewAI Logo
divider

Introduction

This guide walks you through connecting Givebutter to CrewAI using the Composio tool router. By the end, you'll have a working Givebutter agent that can create a new fundraising campaign for our school, list all recent payouts to our nonprofit account, get details for fund with id fund_abc123, retrieve all members of the summer gala campaign through natural language commands.

This guide will help you understand how to give your CrewAI agent real control over a Givebutter account through Composio's Givebutter 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 Givebutter connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Givebutter
  • Build a conversational loop where your agent can execute Givebutter 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 Givebutter MCP server, and what's possible with it?

The Givebutter MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Givebutter account. It provides structured and secure access to your fundraising platform, so your agent can perform actions like creating campaigns, tracking donations, managing contacts, and handling payouts on your behalf.

  • Campaign management and creation: Easily instruct your agent to start new fundraising campaigns, update campaign details, or remove old campaigns when needed.
  • Donation and payout tracking: Ask your agent to retrieve lists of payouts, monitor donation flows, and keep tabs on your fundraising progress in real time.
  • Contact and member administration: Let your agent add, archive, or delete contacts, and fetch lists of campaign members for smooth supporter management.
  • Fund and webhook operations: Direct your agent to get details about specific funds, create or remove webhooks for event notifications, and manage fundraising infrastructure automatically.
  • Automated data cleanup: Empower your agent to archive or delete obsolete contacts, funds, or webhooks, keeping your Givebutter account organized and up to date.

Supported Tools & Triggers

Tools
Archive ContactTool to archive a contact by their id.
Create CampaignTool to create a new campaign.
Create WebhookTool to create a new webhook subscription.
Delete CampaignTool to delete a campaign by its id.
Delete ContactTool to delete a contact by their id.
Delete FundTool to delete a fund by its id.
Delete WebhookTool to delete a webhook by its id.
Get FundTool to retrieve details of a specific fund by its id.
Get MembersTool to retrieve a paginated list of members for a given campaign.
Get PayoutsTool to retrieve a list of payouts associated with your account.
Get PlansTool to retrieve a list of plans associated with your account.
Get TeamsTool to retrieve a list of teams for a specific campaign.
Get TicketsTool to retrieve a list of tickets.
Get TransactionsTool to retrieve a list of transactions associated with your account.
Get WebhooksTool to retrieve all webhooks configured for your account.
Update CampaignTool to update an existing campaign's details by its id.
Update ContactTool to update an existing contact's details by contact id.
Update WebhookTool to update an existing webhook subscription's details.

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 Givebutter 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 Givebutter 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 Givebutter MCP URL

Create a Composio Tool Router session for Givebutter

python
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
    user_id=os.getenv("USER_ID"),
    toolkits=["givebutter"],
)
url = session.mcp.url
What's happening:
  • You create a Givebutter only session through Composio
  • Composio returns an MCP HTTP URL that exposes Givebutter 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="Givebutter Assistant",
    goal="Help users interact with Givebutter through natural language commands",
    backstory=(
        "You are an expert assistant with access to Givebutter tools. "
        "You can perform various Givebutter 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 Givebutter 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 Givebutter 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 Givebutter 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_givebutter_agent.py

Complete Code

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

python
# file: crewai_givebutter_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 Givebutter session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["givebutter"],
    )
    url = session.mcp.url

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

    # Create Givebutter assistant agent
    toolkit_agent = Agent(
        role="Givebutter Assistant",
        goal="Help users interact with Givebutter through natural language commands",
        backstory=(
            "You are an expert assistant with access to Givebutter tools. "
            "You can perform various Givebutter 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 Givebutter 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 Givebutter 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 Givebutter through Composio's Tool Router. The agent can perform Givebutter 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 Givebutter MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Givebutter MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Givebutter MCP with Claude Agent SDK

Google ADK

Google ADK

Use Givebutter MCP with Google ADK

LangChain

LangChain

Use Givebutter MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Givebutter MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Givebutter MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Givebutter MCP with LlamaIndex

FAQ

What are the differences in Tool Router MCP and Givebutter MCP?

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

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

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

Used by agents from

Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai

Never worry about agent reliability

We handle tool reliability, observability, and security so you never have to second-guess an agent action.

Book a call↗Get started for free