Create powerful collaborative AI workflows by connecting multiple MCP servers including Jira, Zendesk, Klavis ReportGen for enhanced multi-agent automation capabilities in Klavis AI.
Jira is a project management and issue tracking tool developed by Atlassian
Zendesk is a customer service software company
Generate visually appealing JavaScript web reports from search queries with Klavis AI.
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,
)
zendesk_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.ZENDESK,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
klavis_reportgen_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.KLAVIS_REPORTGEN,
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)
zendesk_tools = MCPServerAdapter(zendesk_mcp_instance.server_params)
klavis_reportgen_tools = MCPServerAdapter(klavis_reportgen_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
)
zendesk_agent = Agent(
role="Zendesk Specialist",
goal="Handle all Zendesk related tasks and data processing",
backstory="You are an expert in Zendesk operations and data analysis",
tools=zendesk_tools,
reasoning=True,
verbose=False
)
klavis_reportgen_agent = Agent(
role="Klavis ReportGen Specialist",
goal="Handle all Klavis ReportGen related tasks and data processing",
backstory="You are an expert in Klavis ReportGen operations and data analysis",
tools=klavis_reportgen_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=zendesk_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[jira_agent, zendesk_agent, klavis_reportgen_agent],
tasks=[research_task, analysis_task],
verbose=False,
process=Process.sequential
)
result = multi_agent_crew.kickoff()
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