ConnectCrewAIto Brave Search, Cal.com 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"))

brave_search_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.BRAVE_SEARCH,
    user_id="1234",
    connection_type=ConnectionType.STREAMABLE_HTTP,
)

cal.com_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.CAL.COM,
    user_id="1234",
    connection_type=ConnectionType.STREAMABLE_HTTP,
)

# Initialize MCP tools for each server
brave_search_tools = MCPServerAdapter(brave_search_mcp_instance.server_params)
cal.com_tools = MCPServerAdapter(cal.com_mcp_instance.server_params)

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

cal.com_agent = Agent(
    role="Cal.com Specialist",
    goal="Handle all Cal.com related tasks and data processing",
    backstory="You are an expert in Cal.com operations and data analysis",
    tools=cal.com_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=brave_search_agent,
    markdown=True
)

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

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

result = multi_agent_crew.kickoff()