Create powerful collaborative AI workflows by connecting multiple MCP servers including Salesforce, Outlook Mail, Google Docs for enhanced multi-agent automation capabilities in Klavis AI.
Salesforce is the world's leading customer relationship management (CRM) platform that helps businesses connect with customers, partners, and potential customers
Outlook Mail is a web-based suite of webmail, contacts, tasks, and calendaring services from Microsoft
Google Docs is a word processor included as part of the free, web-based Google Docs Editors suite
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"))
salesforce_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.SALESFORCE,
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,
)
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,
)
# Initialize MCP tools for each server
salesforce_tools = MCPServerAdapter(salesforce_mcp_instance.server_params)
outlook_mail_tools = MCPServerAdapter(outlook_mail_mcp_instance.server_params)
google_docs_tools = MCPServerAdapter(google_docs_mcp_instance.server_params)
# Create specialized agents for each service
salesforce_agent = Agent(
role="Salesforce Specialist",
goal="Handle all Salesforce related tasks and data processing",
backstory="You are an expert in Salesforce operations and data analysis",
tools=salesforce_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
)
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
)
# 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=salesforce_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=[salesforce_agent, outlook_mail_agent, google_docs_agent],
tasks=[research_task, analysis_task],
verbose=False,
process=Process.sequential
)
result = multi_agent_crew.kickoff()
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