Create powerful collaborative AI workflows by connecting multiple MCP servers including Jira, Outlook Mail, Plai for enhanced multi-agent automation capabilities in Klavis AI.
Jira is a project management and issue tracking tool developed by Atlassian
Outlook Mail is a web-based suite of webmail, contacts, tasks, and calendaring services from 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"))
jira_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.JIRA,
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,
)
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
jira_tools = MCPServerAdapter(jira_mcp_instance.server_params)
outlook_mail_tools = MCPServerAdapter(outlook_mail_mcp_instance.server_params)
plai_tools = MCPServerAdapter(plai_mcp_instance.server_params)
# Create specialized agents for each service
jira_agent = Agent(
role="Jira Specialist",
goal="Handle all Jira related tasks and data processing",
backstory="You are an expert in Jira operations and data analysis",
tools=jira_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
)
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=jira_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=[jira_agent, outlook_mail_agent, plai_agent],
tasks=[research_task, analysis_task],
verbose=False,
process=Process.sequential
)
result = multi_agent_crew.kickoff()
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