Connectto Asana, Google Docs, Dropbox MCP Servers
Create powerful collaborative AI workflows by connecting multiple MCP servers including Asana, Google Docs, Dropbox for enhanced multi-agent automation capabilities in Klavis AI.
Asana
Asana is a web and mobile application designed to help teams organize, track, and manage their work. It provides project management tools, task assignment, collaboration features, and progress tracking to boost team productivity
Available Tools:
- asana_create_task
- asana_get_task
- asana_search_tasks
- +16 more tools
Google Docs
Google Docs is a word processor included as part of the free, web-based Google Docs Editors suite
Available Tools:
- google_docs_get_document_by_id
- google_docs_get_all_documents
- google_docs_insert_text_at_end
- +2 more tools
Dropbox
Dropbox is a file hosting service that offers cloud storage and file synchronization
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"))
asana_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.ASANA,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
google_docs_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.GOOGLE_DOCS,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
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,
)
# Initialize MCP tools for each server
asana_tools = MCPServerAdapter(asana_mcp_instance.server_params)
google_docs_tools = MCPServerAdapter(google_docs_mcp_instance.server_params)
dropbox_tools = MCPServerAdapter(dropbox_mcp_instance.server_params)
# Create specialized agents for each service
asana_agent = Agent(
role="Asana Specialist",
goal="Handle all Asana related tasks and data processing",
backstory="You are an expert in Asana operations and data analysis",
tools=asana_tools,
reasoning=True,
verbose=False
)
google_docs_agent = Agent(
role="Google Docs Specialist",
goal="Handle all Google Docs related tasks and data processing",
backstory="You are an expert in Google Docs operations and data analysis",
tools=google_docs_tools,
reasoning=True,
verbose=False
)
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
)
# 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=asana_agent,
markdown=True
)
analysis_task = Task(
description="Analyze and synthesize the gathered data",
expected_output="Comprehensive analysis with insights and recommendations",
agent=google_docs_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[asana_agent, google_docs_agent, dropbox_agent],
tasks=[research_task, analysis_task],
verbose=False,
process=Process.sequential
)
result = multi_agent_crew.kickoff()Frequently Asked Questions
Everything you need to know about connecting CrewAI to these MCP servers
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