Create powerful collaborative AI workflows by connecting multiple MCP servers including Dropbox, Plai, OpenRouter for enhanced multi-agent automation capabilities in Klavis AI.
Complete file management solution for Dropbox cloud storage. Upload, download, organize files and folders, manage sharing and collaboration, handle file versions, create file requests, and perform batch operations on your Dropbox files and folders
Plai is an AI-powered advertising platform that simplifies creating, managing, and optimizing Facebook, Instagram, and LinkedIn ad campaigns. It provides tools for lead generation, campaign insights, and automated ad management to help businesses scale their digital marketing efforts effectively.
Access to multiple AI models through a unified API. Generate chat completions, compare model performance, manage usage and costs, get model recommendations, and analyze model capabilities across various providers like OpenAI, Anthropic, Meta, Google, and more
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"))
dropbox_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.DROPBOX,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
plai_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.PLAI,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
openrouter_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.OPENROUTER,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
# Initialize MCP tools for each server
dropbox_tools = MCPServerAdapter(dropbox_mcp_instance.server_params)
plai_tools = MCPServerAdapter(plai_mcp_instance.server_params)
openrouter_tools = MCPServerAdapter(openrouter_mcp_instance.server_params)
# Create specialized agents for each service
dropbox_agent = Agent(
role="Dropbox Specialist",
goal="Handle all Dropbox related tasks and data processing",
backstory="You are an expert in Dropbox operations and data analysis",
tools=dropbox_tools,
reasoning=True,
verbose=False
)
plai_agent = Agent(
role="Plai Specialist",
goal="Handle all Plai related tasks and data processing",
backstory="You are an expert in Plai operations and data analysis",
tools=plai_tools,
reasoning=True,
verbose=False
)
openrouter_agent = Agent(
role="OpenRouter Specialist",
goal="Handle all OpenRouter related tasks and data processing",
backstory="You are an expert in OpenRouter operations and data analysis",
tools=openrouter_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=dropbox_agent,
markdown=True
)
analysis_task = Task(
description="Analyze and synthesize the gathered data",
expected_output="Comprehensive analysis with insights and recommendations",
agent=plai_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[dropbox_agent, plai_agent, openrouter_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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