Connectto QuickBooks, Mem0, Google Jobs MCP Servers
Create powerful collaborative AI workflows by connecting multiple MCP servers including QuickBooks, Mem0, Google Jobs for enhanced multi-agent automation capabilities in Klavis AI.
QuickBooks
QuickBooks is a comprehensive accounting software solution that helps small and medium businesses manage their finances, track expenses, create invoices, manage payroll, and generate financial reports with integrated banking and tax preparation features
Available Tools:
- create_account
- get_account
- list_accounts
- +32 more tools
Mem0
Mem0 is an intelligent memory layer for AI applications that provides long-term memory storage and retrieval. Store code snippets, implementation details, and programming knowledge for seamless context retention across conversations
Available Tools:
- mem0_add_memory
- mem0_get_all_memories
- mem0_search_memories
- +2 more tools
Google Jobs
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
Available Tools:
- google_jobs_search
- google_jobs_get_details
- google_jobs_search_by_company
- +2 more tools
Quick Setup Guide
Follow these steps to connect CrewAI to these MCP servers
Create Your Account
Sign up for KlavisAI to access our MCP server management platform.
Configure Agents & Tools
Set up your CrewAI agents with your desired MCP servers tools and configure authentication settings for collaborative workflows.
Deploy Your Crew
Test your multi-agent workflows and start using your enhanced collaborative AI capabilities.
CrewAI + KlavisAI Integration Snippets
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"))
quickbooks_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.QUICKBOOKS,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
mem0_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.MEM0,
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
quickbooks_tools = MCPServerAdapter(quickbooks_mcp_instance.server_params)
mem0_tools = MCPServerAdapter(mem0_mcp_instance.server_params)
google_jobs_tools = MCPServerAdapter(google_jobs_mcp_instance.server_params)
# Create specialized agents for each service
quickbooks_agent = Agent(
role="QuickBooks Specialist",
goal="Handle all QuickBooks related tasks and data processing",
backstory="You are an expert in QuickBooks operations and data analysis",
tools=quickbooks_tools,
reasoning=True,
verbose=False
)
mem0_agent = Agent(
role="Mem0 Specialist",
goal="Handle all Mem0 related tasks and data processing",
backstory="You are an expert in Mem0 operations and data analysis",
tools=mem0_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=quickbooks_agent,
markdown=True
)
analysis_task = Task(
description="Analyze and synthesize the gathered data",
expected_output="Comprehensive analysis with insights and recommendations",
agent=mem0_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[quickbooks_agent, mem0_agent, google_jobs_agent],
tasks=[research_task, analysis_task],
verbose=False,
process=Process.sequential
)
result = multi_agent_crew.kickoff()Frequently Asked Questions
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