Connectto Mixpanel, Google Calendar, Google Jobs MCP Servers

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

Mixpanel icon

Mixpanel

featured

Mixpanel is a powerful product analytics platform that helps teams understand user behavior, track events, analyze conversion funnels, measure retention, and make data-driven decisions with real-time insights and advanced segmentation capabilities

Available Tools:

  • mixpanel_send_events
  • mixpanel_get_events
  • mixpanel_get_event_properties
  • +6 more tools
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
  • +3 more tools
Google Jobs icon

Google Jobs

featured

Google Jobs is a comprehensive job search platform that aggregates listings from across the web. Search for jobs by location, company, employment type, and more, with detailed information about requirements, benefits, and application processes

Available Tools:

  • google_jobs_search
  • google_jobs_get_details
  • google_jobs_search_by_company
  • +2 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"))

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

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,
)

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

# Initialize MCP tools for each server
mixpanel_tools = MCPServerAdapter(mixpanel_mcp_instance.server_params)
google_calendar_tools = MCPServerAdapter(google_calendar_mcp_instance.server_params)
google_jobs_tools = MCPServerAdapter(google_jobs_mcp_instance.server_params)

# Create specialized agents for each service
mixpanel_agent = Agent(
    role="Mixpanel Specialist",
    goal="Handle all Mixpanel related tasks and data processing",
    backstory="You are an expert in Mixpanel operations and data analysis",
    tools=mixpanel_tools,
    reasoning=True,
    verbose=False
)

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
)

google_jobs_agent = Agent(
    role="Google Jobs Specialist",
    goal="Handle all Google Jobs related tasks and data processing",
    backstory="You are an expert in Google Jobs operations and data analysis",
    tools=google_jobs_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=mixpanel_agent,
    markdown=True
)

analysis_task = Task(
    description="Analyze and synthesize the gathered data",
    expected_output="Comprehensive analysis with insights and recommendations",
    agent=google_calendar_agent,
    markdown=True
)

# Create multi-agent crew
multi_agent_crew = Crew(
    agents=[mixpanel_agent, google_calendar_agent, google_jobs_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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