Connectto Close, Resend, Plai MCP Servers

Create powerful collaborative AI workflows by connecting multiple MCP servers including Close, Resend, Plai for enhanced multi-agent automation capabilities in Klavis AI.

Close icon

Close

featured

Close is a modern CRM platform built for sales teams, providing powerful lead management, contact organization, and sales pipeline tracking to help businesses close more deals

Available Tools:

  • close_create_lead
  • close_get_lead
  • close_search_leads
  • +20 more tools
Resend icon

Resend

featured

Resend is a modern email API for sending and receiving emails programmatically

Available Tools:

  • resend_send_email
  • resend_create_audience
  • resend_get_audience
  • +12 more tools
Plai icon

Plai

featured

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

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"))

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

resend_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.RESEND,
    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
close_tools = MCPServerAdapter(close_mcp_instance.server_params)
resend_tools = MCPServerAdapter(resend_mcp_instance.server_params)
plai_tools = MCPServerAdapter(plai_mcp_instance.server_params)

# Create specialized agents for each service
close_agent = Agent(
    role="Close Specialist",
    goal="Handle all Close related tasks and data processing",
    backstory="You are an expert in Close operations and data analysis",
    tools=close_tools,
    reasoning=True,
    verbose=False
)

resend_agent = Agent(
    role="Resend Specialist",
    goal="Handle all Resend related tasks and data processing",
    backstory="You are an expert in Resend operations and data analysis",
    tools=resend_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=close_agent,
    markdown=True
)

analysis_task = Task(
    description="Analyze and synthesize the gathered data",
    expected_output="Comprehensive analysis with insights and recommendations",
    agent=resend_agent,
    markdown=True
)

# Create multi-agent crew
multi_agent_crew = Crew(
    agents=[close_agent, resend_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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