Connectto Gmail, Figma, Plai MCP Servers
Create powerful collaborative AI workflows by connecting multiple MCP servers including Gmail, Figma, Plai for enhanced multi-agent automation capabilities in Klavis AI.
Gmail
Gmail is a free email service provided by Google
Available Tools:
- gmail_send_email
- gmail_draft_email
- gmail_read_email
- +5 more tools
Figma
Figma is a collaborative interface design tool for web and mobile applications.
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"))
gmail_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.GMAIL,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
figma_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.FIGMA,
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
gmail_tools = MCPServerAdapter(gmail_mcp_instance.server_params)
figma_tools = MCPServerAdapter(figma_mcp_instance.server_params)
plai_tools = MCPServerAdapter(plai_mcp_instance.server_params)
# Create specialized agents for each service
gmail_agent = Agent(
role="Gmail Specialist",
goal="Handle all Gmail related tasks and data processing",
backstory="You are an expert in Gmail operations and data analysis",
tools=gmail_tools,
reasoning=True,
verbose=False
)
figma_agent = Agent(
role="Figma Specialist",
goal="Handle all Figma related tasks and data processing",
backstory="You are an expert in Figma operations and data analysis",
tools=figma_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=gmail_agent,
markdown=True
)
analysis_task = Task(
description="Analyze and synthesize the gathered data",
expected_output="Comprehensive analysis with insights and recommendations",
agent=figma_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[gmail_agent, figma_agent, plai_agent],
tasks=[research_task, analysis_task],
verbose=False,
process=Process.sequential
)
result = multi_agent_crew.kickoff()Frequently Asked Questions
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