Create powerful collaborative AI workflows by connecting multiple MCP servers including Markdown2doc, Klavis ReportGen, WhatsApp for enhanced multi-agent automation capabilities in Klavis AI.
Convert markdown text to different file formats (pdf, docx, doc, html), based on Pandoc
Generate visually appealing JavaScript web reports from search queries with Klavis AI.
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"))
markdown2doc_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.MARKDOWN2DOC,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
klavis_reportgen_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.KLAVIS_REPORTGEN,
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
markdown2doc_tools = MCPServerAdapter(markdown2doc_mcp_instance.server_params)
klavis_reportgen_tools = MCPServerAdapter(klavis_reportgen_mcp_instance.server_params)
whatsapp_tools = MCPServerAdapter(whatsapp_mcp_instance.server_params)
# Create specialized agents for each service
markdown2doc_agent = Agent(
role="Markdown2doc Specialist",
goal="Handle all Markdown2doc related tasks and data processing",
backstory="You are an expert in Markdown2doc operations and data analysis",
tools=markdown2doc_tools,
reasoning=True,
verbose=False
)
klavis_reportgen_agent = Agent(
role="Klavis ReportGen Specialist",
goal="Handle all Klavis ReportGen related tasks and data processing",
backstory="You are an expert in Klavis ReportGen operations and data analysis",
tools=klavis_reportgen_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=markdown2doc_agent,
markdown=True
)
analysis_task = Task(
description="Analyze and synthesize the gathered data",
expected_output="Comprehensive analysis with insights and recommendations",
agent=klavis_reportgen_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[markdown2doc_agent, klavis_reportgen_agent, whatsapp_agent],
tasks=[research_task, analysis_task],
verbose=False,
process=Process.sequential
)
result = multi_agent_crew.kickoff()
Everything you need to know about connecting CrewAI to these MCP servers
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