Connectto QuickBooks, Freshdesk, Tavily MCP Servers

Create powerful collaborative AI workflows by connecting multiple MCP servers including QuickBooks, Freshdesk, Tavily for enhanced multi-agent automation capabilities in Klavis AI.

QuickBooks icon

QuickBooks

featured

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
Freshdesk icon

Freshdesk

featured

Freshdesk is a cloud-based customer support software that helps businesses manage customer inquiries across multiple channels. Create and manage tickets, contacts, companies, and agents with powerful automation and collaboration features

Available Tools:

  • freshdesk_create_ticket
  • freshdesk_get_ticket_by_id
  • freshdesk_update_ticket
  • +52 more tools
Tavily icon

Tavily

featured

Tavily is an AI-powered search API designed for LLMs and AI agents. Get real-time web search results, extract content from URLs, crawl websites, and generate site maps with advanced filtering and parsing capabilities

Available Tools:

  • tavily_search
  • tavily_extract
  • tavily_crawl
  • +1 more tools

Quick Setup Guide

Follow these steps to connect CrewAI to these MCP servers

1

Create Your Account

Sign up for KlavisAI to access our MCP server management platform.

2

Configure Agents & Tools

Set up your CrewAI agents with your desired MCP servers tools and configure authentication settings for collaborative workflows.

3

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,
)

freshdesk_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.FRESHDESK,
    user_id="1234",
    platform_name="Klavis",
    connection_type=ConnectionType.STREAMABLE_HTTP,
)

tavily_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.TAVILY,
    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)
freshdesk_tools = MCPServerAdapter(freshdesk_mcp_instance.server_params)
tavily_tools = MCPServerAdapter(tavily_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
)

freshdesk_agent = Agent(
    role="Freshdesk Specialist",
    goal="Handle all Freshdesk related tasks and data processing",
    backstory="You are an expert in Freshdesk operations and data analysis",
    tools=freshdesk_tools,
    reasoning=True,
    verbose=False
)

tavily_agent = Agent(
    role="Tavily Specialist",
    goal="Handle all Tavily related tasks and data processing",
    backstory="You are an expert in Tavily operations and data analysis",
    tools=tavily_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=freshdesk_agent,
    markdown=True
)

# Create multi-agent crew
multi_agent_crew = Crew(
    agents=[quickbooks_agent, freshdesk_agent, tavily_agent],
    tasks=[research_task, analysis_task],
    verbose=False,
    process=Process.sequential
)

result = multi_agent_crew.kickoff()

Frequently Asked Questions

Everything you need to know about connecting CrewAI to these MCP servers

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