Connectto Google Calendar, QuickBooks, Monday MCP Servers
Create powerful collaborative AI workflows by connecting multiple MCP servers including Google Calendar, QuickBooks, Monday for enhanced multi-agent automation capabilities in Klavis AI.
Google Calendar
Google Calendar is a time-management and scheduling calendar service
Available Tools:
- google_calendar_list_calendars
- google_calendar_create_event
- google_calendar_list_events
- +3 more tools
QuickBooks
QuickBooks is a comprehensive accounting software solution that helps small and medium businesses manage their finances, track expenses, create invoices, manage payroll, and generate financial reports with integrated banking and tax preparation features
Available Tools:
- create_account
- get_account
- list_accounts
- +32 more tools
Monday
Monday.com is a work operating system that powers teams to run projects and workflows with confidence. Create boards, manage items, customize columns, organize groups, and collaborate with team members in a visual workspace
Available Tools:
- monday_get_users_by_name
- monday_get_boards
- monday_create_board
- +9 more tools
Quick Setup Guide
Follow these steps to connect CrewAI to these MCP servers
Create Your Account
Sign up for KlavisAI to access our MCP server management platform.
Configure Agents & Tools
Set up your CrewAI agents with your desired MCP servers tools and configure authentication settings for collaborative workflows.
Deploy Your Crew
Test your multi-agent workflows and start using your enhanced collaborative AI capabilities.
CrewAI + KlavisAI Integration Snippets
import os
from crewai import Agent, Task, Crew, Process
from crewai_tools import MCPServerAdapter
from klavis import Klavis
from klavis.types import McpServerName, ConnectionType
# Initialize clients
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))
google_calendar_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.GOOGLE_CALENDAR,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
quickbooks_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.QUICKBOOKS,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
monday_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.MONDAY,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
# Initialize MCP tools for each server
google_calendar_tools = MCPServerAdapter(google_calendar_mcp_instance.server_params)
quickbooks_tools = MCPServerAdapter(quickbooks_mcp_instance.server_params)
monday_tools = MCPServerAdapter(monday_mcp_instance.server_params)
# Create specialized agents for each service
google_calendar_agent = Agent(
role="Google Calendar Specialist",
goal="Handle all Google Calendar related tasks and data processing",
backstory="You are an expert in Google Calendar operations and data analysis",
tools=google_calendar_tools,
reasoning=True,
verbose=False
)
quickbooks_agent = Agent(
role="QuickBooks Specialist",
goal="Handle all QuickBooks related tasks and data processing",
backstory="You are an expert in QuickBooks operations and data analysis",
tools=quickbooks_tools,
reasoning=True,
verbose=False
)
monday_agent = Agent(
role="Monday Specialist",
goal="Handle all Monday related tasks and data processing",
backstory="You are an expert in Monday operations and data analysis",
tools=monday_tools,
reasoning=True,
verbose=False
)
# Define collaborative tasks
research_task = Task(
description="Gather comprehensive data from all available sources",
expected_output="Raw data and initial findings from all services",
agent=google_calendar_agent,
markdown=True
)
analysis_task = Task(
description="Analyze and synthesize the gathered data",
expected_output="Comprehensive analysis with insights and recommendations",
agent=quickbooks_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[google_calendar_agent, quickbooks_agent, monday_agent],
tasks=[research_task, analysis_task],
verbose=False,
process=Process.sequential
)
result = multi_agent_crew.kickoff()Frequently Asked Questions
Everything you need to know about connecting CrewAI to these MCP servers
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