Create powerful collaborative AI workflows by connecting multiple MCP servers including Confluence, Gong, HubSpot for enhanced multi-agent automation capabilities in Klavis AI.
Confluence is a team workspace where knowledge and collaboration meet
Gong is a revenue intelligence platform that captures and analyzes all revenue-related interactions to help sales teams close more deals. It provides conversation analytics, deal insights, and sales performance tracking through call recordings and transcripts
HubSpot is a developer and marketer of software products for inbound marketing, sales, and customer 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"))
confluence_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.CONFLUENCE,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
gong_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.GONG,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
hubspot_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.HUBSPOT,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
# Initialize MCP tools for each server
confluence_tools = MCPServerAdapter(confluence_mcp_instance.server_params)
gong_tools = MCPServerAdapter(gong_mcp_instance.server_params)
hubspot_tools = MCPServerAdapter(hubspot_mcp_instance.server_params)
# Create specialized agents for each service
confluence_agent = Agent(
role="Confluence Specialist",
goal="Handle all Confluence related tasks and data processing",
backstory="You are an expert in Confluence operations and data analysis",
tools=confluence_tools,
reasoning=True,
verbose=False
)
gong_agent = Agent(
role="Gong Specialist",
goal="Handle all Gong related tasks and data processing",
backstory="You are an expert in Gong operations and data analysis",
tools=gong_tools,
reasoning=True,
verbose=False
)
hubspot_agent = Agent(
role="HubSpot Specialist",
goal="Handle all HubSpot related tasks and data processing",
backstory="You are an expert in HubSpot operations and data analysis",
tools=hubspot_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=confluence_agent,
markdown=True
)
analysis_task = Task(
description="Analyze and synthesize the gathered data",
expected_output="Comprehensive analysis with insights and recommendations",
agent=gong_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[confluence_agent, gong_agent, hubspot_agent],
tasks=[research_task, analysis_task],
verbose=False,
process=Process.sequential
)
result = multi_agent_crew.kickoff()
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