Create powerful collaborative AI workflows by connecting multiple MCP servers including Zendesk, Perplexity, WhatsApp for enhanced multi-agent automation capabilities in Klavis AI.
Zendesk is a customer service software company
Perplexity is an AI research assistant that provides accurate answers and cites sources
WhatsApp Business API integration that enables sending text messages, media, and managing conversations with customers. Perfect for customer support, marketing campaigns, and automated messaging workflows through the official WhatsApp Business platform.
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"))
zendesk_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.ZENDESK,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
perplexity_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.PERPLEXITY,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
whatsapp_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.WHATSAPP,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
# Initialize MCP tools for each server
zendesk_tools = MCPServerAdapter(zendesk_mcp_instance.server_params)
perplexity_tools = MCPServerAdapter(perplexity_mcp_instance.server_params)
whatsapp_tools = MCPServerAdapter(whatsapp_mcp_instance.server_params)
# Create specialized agents for each service
zendesk_agent = Agent(
role="Zendesk Specialist",
goal="Handle all Zendesk related tasks and data processing",
backstory="You are an expert in Zendesk operations and data analysis",
tools=zendesk_tools,
reasoning=True,
verbose=False
)
perplexity_agent = Agent(
role="Perplexity Specialist",
goal="Handle all Perplexity related tasks and data processing",
backstory="You are an expert in Perplexity operations and data analysis",
tools=perplexity_tools,
reasoning=True,
verbose=False
)
whatsapp_agent = Agent(
role="WhatsApp Specialist",
goal="Handle all WhatsApp related tasks and data processing",
backstory="You are an expert in WhatsApp operations and data analysis",
tools=whatsapp_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=zendesk_agent,
markdown=True
)
analysis_task = Task(
description="Analyze and synthesize the gathered data",
expected_output="Comprehensive analysis with insights and recommendations",
agent=perplexity_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[zendesk_agent, perplexity_agent, whatsapp_agent],
tasks=[research_task, analysis_task],
verbose=False,
process=Process.sequential
)
result = multi_agent_crew.kickoff()
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