Create powerful collaborative AI workflows by connecting multiple MCP servers including Mixpanel, Markdown2doc, Jira for enhanced multi-agent automation capabilities in Klavis AI.
Mixpanel is a powerful product analytics platform that helps teams understand user behavior, track events, analyze conversion funnels, measure retention, and make data-driven decisions with real-time insights and advanced segmentation capabilities
Convert markdown text to different file formats (pdf, docx, doc, html), based on Pandoc
Jira is a project management and issue tracking tool developed by Atlassian
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"))
mixpanel_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.MIXPANEL,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
markdown2doc_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.MARKDOWN2DOC,
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,
)
# Initialize MCP tools for each server
mixpanel_tools = MCPServerAdapter(mixpanel_mcp_instance.server_params)
markdown2doc_tools = MCPServerAdapter(markdown2doc_mcp_instance.server_params)
jira_tools = MCPServerAdapter(jira_mcp_instance.server_params)
# Create specialized agents for each service
mixpanel_agent = Agent(
role="Mixpanel Specialist",
goal="Handle all Mixpanel related tasks and data processing",
backstory="You are an expert in Mixpanel operations and data analysis",
tools=mixpanel_tools,
reasoning=True,
verbose=False
)
markdown2doc_agent = Agent(
role="Markdown2doc Specialist",
goal="Handle all Markdown2doc related tasks and data processing",
backstory="You are an expert in Markdown2doc operations and data analysis",
tools=markdown2doc_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
)
# 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=mixpanel_agent,
markdown=True
)
analysis_task = Task(
description="Analyze and synthesize the gathered data",
expected_output="Comprehensive analysis with insights and recommendations",
agent=markdown2doc_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[mixpanel_agent, markdown2doc_agent, jira_agent],
tasks=[research_task, analysis_task],
verbose=False,
process=Process.sequential
)
result = multi_agent_crew.kickoff()
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