Create powerful collaborative AI workflows by connecting multiple MCP servers including Salesforce, Gmail, Firecrawl Deep Research 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
Gmail is a free email service provided by Google
A personal research assistant that analyze sources across the web, based on Firecrawl
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,
)
gmail_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.GMAIL,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
firecrawl_deep_research_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.FIRECRAWL_DEEP_RESEARCH,
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)
gmail_tools = MCPServerAdapter(gmail_mcp_instance.server_params)
firecrawl_deep_research_tools = MCPServerAdapter(firecrawl_deep_research_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
)
gmail_agent = Agent(
role="Gmail Specialist",
goal="Handle all Gmail related tasks and data processing",
backstory="You are an expert in Gmail operations and data analysis",
tools=gmail_tools,
reasoning=True,
verbose=False
)
firecrawl_deep_research_agent = Agent(
role="Firecrawl Deep Research Specialist",
goal="Handle all Firecrawl Deep Research related tasks and data processing",
backstory="You are an expert in Firecrawl Deep Research operations and data analysis",
tools=firecrawl_deep_research_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=gmail_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[salesforce_agent, gmail_agent, firecrawl_deep_research_agent],
tasks=[research_task, analysis_task],
verbose=False,
process=Process.sequential
)
result = multi_agent_crew.kickoff()
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