Connectto Asana, Outlook Mail, Slack MCP Servers
Create powerful collaborative AI workflows by connecting multiple MCP servers including Asana, Outlook Mail, Slack 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
Outlook Mail
Outlook Mail is a web-based suite of webmail, contacts, tasks, and calendaring services from Microsoft
Slack
Slack is a messaging app for business that connects people to the information they need
Available Tools:
- slack_list_channels
- slack_post_message
- slack_reply_to_thread
- +6 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"))
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,
)
outlook_mail_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.OUTLOOK_MAIL,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
slack_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.SLACK,
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)
outlook_mail_tools = MCPServerAdapter(outlook_mail_mcp_instance.server_params)
slack_tools = MCPServerAdapter(slack_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
)
outlook_mail_agent = Agent(
role="Outlook Mail Specialist",
goal="Handle all Outlook Mail related tasks and data processing",
backstory="You are an expert in Outlook Mail operations and data analysis",
tools=outlook_mail_tools,
reasoning=True,
verbose=False
)
slack_agent = Agent(
role="Slack Specialist",
goal="Handle all Slack related tasks and data processing",
backstory="You are an expert in Slack operations and data analysis",
tools=slack_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=outlook_mail_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[asana_agent, outlook_mail_agent, slack_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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