Connectto Linear, Google Calendar, Perplexity MCP Servers

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

Linear icon

Linear

featured

Linear is a modern issue tracking and project management tool designed for high-performance teams to build better software faster

Available Tools:

  • linear_get_teams
  • linear_get_issues
  • linear_get_issue_by_id
  • +9 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
  • +2 more tools
Perplexity icon

Perplexity

coming soon

Perplexity is an AI research assistant that provides accurate answers and cites sources

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"))

linear_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.LINEAR,
    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,
)

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

# Initialize MCP tools for each server
linear_tools = MCPServerAdapter(linear_mcp_instance.server_params)
google_calendar_tools = MCPServerAdapter(google_calendar_mcp_instance.server_params)
perplexity_tools = MCPServerAdapter(perplexity_mcp_instance.server_params)

# Create specialized agents for each service
linear_agent = Agent(
    role="Linear Specialist",
    goal="Handle all Linear related tasks and data processing",
    backstory="You are an expert in Linear operations and data analysis",
    tools=linear_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
)

perplexity_agent = Agent(
    role="Perplexity Specialist",
    goal="Handle all Perplexity related tasks and data processing",
    backstory="You are an expert in Perplexity operations and data analysis",
    tools=perplexity_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=linear_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=[linear_agent, google_calendar_agent, perplexity_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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