ConnectCrewAIto LinkedIn, Outlook Mail, Figma MCP Servers

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"))

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

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

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

# Initialize MCP tools for each server
linkedin_tools = MCPServerAdapter(linkedin_mcp_instance.server_params)
outlook_mail_tools = MCPServerAdapter(outlook_mail_mcp_instance.server_params)
figma_tools = MCPServerAdapter(figma_mcp_instance.server_params)

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

outlook_mail_agent = Agent(
    role="Outlook Mail Specialist",
    goal="Handle all Outlook Mail related tasks and data processing",
    backstory="You are an expert in Outlook Mail operations and data analysis",
    tools=outlook_mail_tools,
    reasoning=True,
    verbose=False
)

figma_agent = Agent(
    role="Figma Specialist",
    goal="Handle all Figma related tasks and data processing",
    backstory="You are an expert in Figma operations and data analysis",
    tools=figma_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=linkedin_agent,
    markdown=True
)

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

# Create multi-agent crew
multi_agent_crew = Crew(
    agents=[linkedin_agent, outlook_mail_agent, figma_agent],
    tasks=[research_task, analysis_task],
    verbose=False,
    process=Process.sequential
)

result = multi_agent_crew.kickoff()