Create powerful collaborative AI workflows by connecting multiple MCP servers including Doc2markdown, Exa, Google Jobs for enhanced multi-agent automation capabilities in Klavis AI.
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Exa is an AI-powered search engine designed for AI applications. Use neural search to understand meaning and context, find similar content, get direct answers with citations, and conduct comprehensive research with structured analysis
Google Jobs is a comprehensive job search platform that aggregates listings from across the web. Search for jobs by location, company, employment type, and more, with detailed information about requirements, benefits, and application processes
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"))
doc2markdown_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.DOC2MARKDOWN,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
exa_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.EXA,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
google_jobs_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.GOOGLE_JOBS,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
# Initialize MCP tools for each server
doc2markdown_tools = MCPServerAdapter(doc2markdown_mcp_instance.server_params)
exa_tools = MCPServerAdapter(exa_mcp_instance.server_params)
google_jobs_tools = MCPServerAdapter(google_jobs_mcp_instance.server_params)
# Create specialized agents for each service
doc2markdown_agent = Agent(
role="Doc2markdown Specialist",
goal="Handle all Doc2markdown related tasks and data processing",
backstory="You are an expert in Doc2markdown operations and data analysis",
tools=doc2markdown_tools,
reasoning=True,
verbose=False
)
exa_agent = Agent(
role="Exa Specialist",
goal="Handle all Exa related tasks and data processing",
backstory="You are an expert in Exa operations and data analysis",
tools=exa_tools,
reasoning=True,
verbose=False
)
google_jobs_agent = Agent(
role="Google Jobs Specialist",
goal="Handle all Google Jobs related tasks and data processing",
backstory="You are an expert in Google Jobs operations and data analysis",
tools=google_jobs_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=doc2markdown_agent,
markdown=True
)
analysis_task = Task(
description="Analyze and synthesize the gathered data",
expected_output="Comprehensive analysis with insights and recommendations",
agent=exa_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[doc2markdown_agent, exa_agent, google_jobs_agent],
tasks=[research_task, analysis_task],
verbose=False,
process=Process.sequential
)
result = multi_agent_crew.kickoff()
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