Create powerful collaborative AI workflows by connecting multiple MCP servers including Attio, Jira, LinkedIn for enhanced multi-agent automation capabilities in Klavis AI.
Attio is a next-generation CRM platform that helps teams build stronger relationships with their customers through powerful data management and automation
Jira is a project management and issue tracking tool developed by Atlassian
LinkedIn is a business and employment-oriented online service
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"))
attio_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.ATTIO,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
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,
)
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,
)
# Initialize MCP tools for each server
attio_tools = MCPServerAdapter(attio_mcp_instance.server_params)
jira_tools = MCPServerAdapter(jira_mcp_instance.server_params)
linkedin_tools = MCPServerAdapter(linkedin_mcp_instance.server_params)
# Create specialized agents for each service
attio_agent = Agent(
role="Attio Specialist",
goal="Handle all Attio related tasks and data processing",
backstory="You are an expert in Attio operations and data analysis",
tools=attio_tools,
reasoning=True,
verbose=False
)
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
)
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
)
# 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=attio_agent,
markdown=True
)
analysis_task = Task(
description="Analyze and synthesize the gathered data",
expected_output="Comprehensive analysis with insights and recommendations",
agent=jira_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[attio_agent, jira_agent, linkedin_agent],
tasks=[research_task, analysis_task],
verbose=False,
process=Process.sequential
)
result = multi_agent_crew.kickoff()
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