Connectto Google Sheets, Klavis ReportGen, Plai MCP Servers
Create powerful collaborative AI workflows by connecting multiple MCP servers including Google Sheets, Klavis ReportGen, Plai for enhanced multi-agent automation capabilities in Klavis AI.
Google Sheets
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
Klavis ReportGen
Generate visually appealing JavaScript web reports from search queries with Klavis AI.
Available Tools:
- generate_web_reports
Plai
Plai is an AI-powered advertising platform that simplifies creating, managing, and optimizing Facebook, Instagram, and LinkedIn ad campaigns. It provides tools for lead generation, campaign insights, and automated ad management to help businesses scale their digital marketing efforts effectively.
Available Tools:
- plai_create_user_profile
- plai_get_user_profile
- plai_create_link
- +7 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"))
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,
)
klavis_reportgen_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.KLAVIS_REPORTGEN,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
plai_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.PLAI,
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)
klavis_reportgen_tools = MCPServerAdapter(klavis_reportgen_mcp_instance.server_params)
plai_tools = MCPServerAdapter(plai_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
)
klavis_reportgen_agent = Agent(
role="Klavis ReportGen Specialist",
goal="Handle all Klavis ReportGen related tasks and data processing",
backstory="You are an expert in Klavis ReportGen operations and data analysis",
tools=klavis_reportgen_tools,
reasoning=True,
verbose=False
)
plai_agent = Agent(
role="Plai Specialist",
goal="Handle all Plai related tasks and data processing",
backstory="You are an expert in Plai operations and data analysis",
tools=plai_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=klavis_reportgen_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[google_sheets_agent, klavis_reportgen_agent, plai_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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