ConnectCrewAIto PagerDuty, ServiceNow 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"))

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

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

# Initialize MCP tools for each server
pagerduty_tools = MCPServerAdapter(pagerduty_mcp_instance.server_params)
servicenow_tools = MCPServerAdapter(servicenow_mcp_instance.server_params)

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

servicenow_agent = Agent(
    role="ServiceNow Specialist",
    goal="Handle all ServiceNow related tasks and data processing",
    backstory="You are an expert in ServiceNow operations and data analysis",
    tools=servicenow_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=pagerduty_agent,
    markdown=True
)

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

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

result = multi_agent_crew.kickoff()