Connectto Google Sheets, Exa, Monday MCP Servers

Create powerful collaborative AI workflows by connecting multiple MCP servers including Google Sheets, Exa, Monday for enhanced multi-agent automation capabilities in Klavis AI.

Google Sheets icon

Google Sheets

featured

Google Sheets is a web-based spreadsheet application that allows users to create, edit, and collaborate on spreadsheets online

Available Tools:

  • google_sheets_create_spreadsheet
  • google_sheets_get_spreadsheet
  • google_sheets_write_to_cell
  • +1 more tools
Exa icon

Exa

featured

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

Available Tools:

  • exa_search
  • exa_get_contents
  • exa_find_similar
  • +2 more tools
Monday icon

Monday

featured

Monday.com is a work operating system that powers teams to run projects and workflows with confidence. Create boards, manage items, customize columns, organize groups, and collaborate with team members in a visual workspace

Available Tools:

  • monday_get_users_by_name
  • monday_get_boards
  • monday_create_board
  • +9 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"))

google_sheets_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.GOOGLE_SHEETS,
    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,
)

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

# Initialize MCP tools for each server
google_sheets_tools = MCPServerAdapter(google_sheets_mcp_instance.server_params)
exa_tools = MCPServerAdapter(exa_mcp_instance.server_params)
monday_tools = MCPServerAdapter(monday_mcp_instance.server_params)

# Create specialized agents for each service
google_sheets_agent = Agent(
    role="Google Sheets Specialist",
    goal="Handle all Google Sheets related tasks and data processing",
    backstory="You are an expert in Google Sheets operations and data analysis",
    tools=google_sheets_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
)

monday_agent = Agent(
    role="Monday Specialist",
    goal="Handle all Monday related tasks and data processing",
    backstory="You are an expert in Monday operations and data analysis",
    tools=monday_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=google_sheets_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=[google_sheets_agent, exa_agent, monday_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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