Create powerful collaborative AI workflows by connecting multiple MCP servers including Affinity, Figma, Motion for enhanced multi-agent automation capabilities in Klavis AI.
Affinity is a relationship intelligence platform that helps teams manage relationships, track deals, and leverage network insights to drive business growth with powerful CRM and relationship mapping capabilities
Figma is a collaborative interface design tool for web and mobile applications.
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
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"))
affinity_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.AFFINITY,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
figma_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.FIGMA,
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
affinity_tools = MCPServerAdapter(affinity_mcp_instance.server_params)
figma_tools = MCPServerAdapter(figma_mcp_instance.server_params)
motion_tools = MCPServerAdapter(motion_mcp_instance.server_params)
# Create specialized agents for each service
affinity_agent = Agent(
role="Affinity Specialist",
goal="Handle all Affinity related tasks and data processing",
backstory="You are an expert in Affinity operations and data analysis",
tools=affinity_tools,
reasoning=True,
verbose=False
)
figma_agent = Agent(
role="Figma Specialist",
goal="Handle all Figma related tasks and data processing",
backstory="You are an expert in Figma operations and data analysis",
tools=figma_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=affinity_agent,
markdown=True
)
analysis_task = Task(
description="Analyze and synthesize the gathered data",
expected_output="Comprehensive analysis with insights and recommendations",
agent=figma_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[affinity_agent, figma_agent, motion_agent],
tasks=[research_task, analysis_task],
verbose=False,
process=Process.sequential
)
result = multi_agent_crew.kickoff()
Everything you need to know about connecting CrewAI to these MCP servers
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