Create powerful collaborative AI workflows by connecting multiple MCP servers including Google Drive, Firecrawl Deep Research, Motion for enhanced multi-agent automation capabilities in Klavis AI.
Google Drive is a cloud storage service
A personal research assistant that analyze sources across the web, based on Firecrawl
Motion is an intelligent project management and calendar application that automatically schedules your tasks, meetings, and projects to optimize your productivity and help you focus on what matters most
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"))
google_drive_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.GOOGLE_DRIVE,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
firecrawl_deep_research_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.FIRECRAWL_DEEP_RESEARCH,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
motion_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.MOTION,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
# Initialize MCP tools for each server
google_drive_tools = MCPServerAdapter(google_drive_mcp_instance.server_params)
firecrawl_deep_research_tools = MCPServerAdapter(firecrawl_deep_research_mcp_instance.server_params)
motion_tools = MCPServerAdapter(motion_mcp_instance.server_params)
# Create specialized agents for each service
google_drive_agent = Agent(
role="Google Drive Specialist",
goal="Handle all Google Drive related tasks and data processing",
backstory="You are an expert in Google Drive operations and data analysis",
tools=google_drive_tools,
reasoning=True,
verbose=False
)
firecrawl_deep_research_agent = Agent(
role="Firecrawl Deep Research Specialist",
goal="Handle all Firecrawl Deep Research related tasks and data processing",
backstory="You are an expert in Firecrawl Deep Research operations and data analysis",
tools=firecrawl_deep_research_tools,
reasoning=True,
verbose=False
)
motion_agent = Agent(
role="Motion Specialist",
goal="Handle all Motion related tasks and data processing",
backstory="You are an expert in Motion operations and data analysis",
tools=motion_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_drive_agent,
markdown=True
)
analysis_task = Task(
description="Analyze and synthesize the gathered data",
expected_output="Comprehensive analysis with insights and recommendations",
agent=firecrawl_deep_research_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[google_drive_agent, firecrawl_deep_research_agent, motion_agent],
tasks=[research_task, analysis_task],
verbose=False,
process=Process.sequential
)
result = multi_agent_crew.kickoff()
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