Create powerful collaborative AI workflows by connecting multiple MCP servers including Attio, OneDrive, Plai for enhanced multi-agent automation capabilities in Klavis AI.
Attio is a next-generation CRM platform that helps teams build stronger relationships with their customers through powerful data management and automation
OneDrive is a file hosting service and synchronization service operated by Microsoft
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.
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"))
attio_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.ATTIO,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
onedrive_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.ONEDRIVE,
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
attio_tools = MCPServerAdapter(attio_mcp_instance.server_params)
onedrive_tools = MCPServerAdapter(onedrive_mcp_instance.server_params)
plai_tools = MCPServerAdapter(plai_mcp_instance.server_params)
# Create specialized agents for each service
attio_agent = Agent(
role="Attio Specialist",
goal="Handle all Attio related tasks and data processing",
backstory="You are an expert in Attio operations and data analysis",
tools=attio_tools,
reasoning=True,
verbose=False
)
onedrive_agent = Agent(
role="OneDrive Specialist",
goal="Handle all OneDrive related tasks and data processing",
backstory="You are an expert in OneDrive operations and data analysis",
tools=onedrive_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=attio_agent,
markdown=True
)
analysis_task = Task(
description="Analyze and synthesize the gathered data",
expected_output="Comprehensive analysis with insights and recommendations",
agent=onedrive_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[attio_agent, onedrive_agent, plai_agent],
tasks=[research_task, analysis_task],
verbose=False,
process=Process.sequential
)
result = multi_agent_crew.kickoff()
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