Connectto Confluence, Calendly, Motion MCP Servers
Create powerful collaborative AI workflows by connecting multiple MCP servers including Confluence, Calendly, Motion for enhanced multi-agent automation capabilities in Klavis AI.
Confluence
Confluence is a team workspace where knowledge and collaboration meet
Available Tools:
- confluence_create_page
- confluence_get_page
- confluence_get_pages_by_id
- +11 more tools
Calendly
Manage scheduling and appointments with your agents.
Motion
Motion is an intelligent project management and calendar application that automatically schedules your tasks, meetings, and projects to optimize your productivity and help you focus on what matters most
Available Tools:
- motion_get_workspaces
- motion_get_users
- motion_get_my_user
- +11 more tools
Quick Setup Guide
Follow these steps to connect CrewAI to these MCP servers
Create Your Account
Sign up for KlavisAI to access our MCP server management platform.
Configure Agents & Tools
Set up your CrewAI agents with your desired MCP servers tools and configure authentication settings for collaborative workflows.
Deploy Your Crew
Test your multi-agent workflows and start using your enhanced collaborative AI capabilities.
CrewAI + KlavisAI Integration Snippets
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,
)
calendly_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.CALENDLY,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
motion_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.MOTION,
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)
calendly_tools = MCPServerAdapter(calendly_mcp_instance.server_params)
motion_tools = MCPServerAdapter(motion_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
)
calendly_agent = Agent(
role="Calendly Specialist",
goal="Handle all Calendly related tasks and data processing",
backstory="You are an expert in Calendly operations and data analysis",
tools=calendly_tools,
reasoning=True,
verbose=False
)
motion_agent = Agent(
role="Motion Specialist",
goal="Handle all Motion related tasks and data processing",
backstory="You are an expert in Motion operations and data analysis",
tools=motion_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=calendly_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[confluence_agent, calendly_agent, motion_agent],
tasks=[research_task, analysis_task],
verbose=False,
process=Process.sequential
)
result = multi_agent_crew.kickoff()Frequently Asked Questions
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