Create powerful collaborative AI workflows by connecting multiple MCP servers including Markdown2doc, Figma, Firecrawl Web Search for enhanced multi-agent automation capabilities in Klavis AI.
Convert markdown text to different file formats (pdf, docx, doc, html), based on Pandoc
Figma is a collaborative interface design tool for web and mobile applications.
Advanced web crawling, scraping, and search capabilities powered by 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"))
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,
)
figma_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.FIGMA,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
firecrawl_web_search_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.FIRECRAWL_WEB_SEARCH,
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)
figma_tools = MCPServerAdapter(figma_mcp_instance.server_params)
firecrawl_web_search_tools = MCPServerAdapter(firecrawl_web_search_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
)
figma_agent = Agent(
role="Figma Specialist",
goal="Handle all Figma related tasks and data processing",
backstory="You are an expert in Figma operations and data analysis",
tools=figma_tools,
reasoning=True,
verbose=False
)
firecrawl_web_search_agent = Agent(
role="Firecrawl Web Search Specialist",
goal="Handle all Firecrawl Web Search related tasks and data processing",
backstory="You are an expert in Firecrawl Web Search operations and data analysis",
tools=firecrawl_web_search_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=figma_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[markdown2doc_agent, figma_agent, firecrawl_web_search_agent],
tasks=[research_task, analysis_task],
verbose=False,
process=Process.sequential
)
result = multi_agent_crew.kickoff()
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