Connectto Close, Calendly, Google Docs MCP Servers
Create powerful collaborative AI workflows by connecting multiple MCP servers including Close, Calendly, Google Docs for enhanced multi-agent automation capabilities in Klavis AI.
Close
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
Calendly
Manage scheduling and appointments with your agents.
Google Docs
Google Docs is a word processor included as part of the free, web-based Google Docs Editors suite
Available Tools:
- google_docs_get_document_by_id
- google_docs_get_all_documents
- google_docs_insert_text_at_end
- +2 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"))
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,
)
calendly_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.CALENDLY,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
google_docs_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.GOOGLE_DOCS,
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)
calendly_tools = MCPServerAdapter(calendly_mcp_instance.server_params)
google_docs_tools = MCPServerAdapter(google_docs_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
)
calendly_agent = Agent(
role="Calendly Specialist",
goal="Handle all Calendly related tasks and data processing",
backstory="You are an expert in Calendly operations and data analysis",
tools=calendly_tools,
reasoning=True,
verbose=False
)
google_docs_agent = Agent(
role="Google Docs Specialist",
goal="Handle all Google Docs related tasks and data processing",
backstory="You are an expert in Google Docs operations and data analysis",
tools=google_docs_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=calendly_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[close_agent, calendly_agent, google_docs_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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