Create powerful collaborative AI workflows by connecting multiple MCP servers including Perplexity, Calendly, QuickBooks for enhanced multi-agent automation capabilities in Klavis AI.
Perplexity is an AI research assistant that provides accurate answers and cites sources
Manage scheduling and appointments with your agents.
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
Follow these steps to connect CrewAI to these MCP servers
Sign up for KlavisAI to access our MCP server management platform.
Set up your CrewAI agents with your desired MCP servers tools and configure authentication settings for collaborative workflows.
Test your multi-agent workflows and start using your enhanced collaborative AI capabilities.
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"))
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,
)
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,
)
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,
)
# Initialize MCP tools for each server
perplexity_tools = MCPServerAdapter(perplexity_mcp_instance.server_params)
calendly_tools = MCPServerAdapter(calendly_mcp_instance.server_params)
quickbooks_tools = MCPServerAdapter(quickbooks_mcp_instance.server_params)
# Create specialized agents for each service
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
)
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
)
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
)
# 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=perplexity_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=[perplexity_agent, calendly_agent, quickbooks_agent],
tasks=[research_task, analysis_task],
verbose=False,
process=Process.sequential
)
result = multi_agent_crew.kickoff()
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