Connectto Google Calendar, Calendly, Plai MCP Servers

Create powerful collaborative AI workflows by connecting multiple MCP servers including Google Calendar, Calendly, Plai for enhanced multi-agent automation capabilities in Klavis AI.

Google Calendar icon

Google Calendar

featured

Google Calendar is a time-management and scheduling calendar service

Available Tools:

  • google_calendar_list_calendars
  • google_calendar_create_event
  • google_calendar_list_events
  • +2 more tools
Calendly icon

Calendly

coming soon

Manage scheduling and appointments with your agents.

Plai icon

Plai

featured

Plai is an AI-powered advertising platform that simplifies creating, managing, and optimizing Facebook, Instagram, and LinkedIn ad campaigns. It provides tools for lead generation, campaign insights, and automated ad management to help businesses scale their digital marketing efforts effectively.

Available Tools:

  • plai_create_user_profile
  • plai_get_user_profile
  • plai_create_link
  • +7 more tools

Quick Setup Guide

Follow these steps to connect CrewAI to these MCP servers

1

Create Your Account

Sign up for KlavisAI to access our MCP server management platform.

2

Configure Agents & Tools

Set up your CrewAI agents with your desired MCP servers tools and configure authentication settings for collaborative workflows.

3

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,
)

calendly_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.CALENDLY,
    user_id="1234",
    platform_name="Klavis",
    connection_type=ConnectionType.STREAMABLE_HTTP,
)

plai_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.PLAI,
    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)
calendly_tools = MCPServerAdapter(calendly_mcp_instance.server_params)
plai_tools = MCPServerAdapter(plai_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
)

calendly_agent = Agent(
    role="Calendly Specialist",
    goal="Handle all Calendly related tasks and data processing",
    backstory="You are an expert in Calendly operations and data analysis",
    tools=calendly_tools,
    reasoning=True,
    verbose=False
)

plai_agent = Agent(
    role="Plai Specialist",
    goal="Handle all Plai related tasks and data processing",
    backstory="You are an expert in Plai operations and data analysis",
    tools=plai_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=calendly_agent,
    markdown=True
)

# Create multi-agent crew
multi_agent_crew = Crew(
    agents=[google_calendar_agent, calendly_agent, plai_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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