Connectto Google Docs, OpenRouter, Monday MCP Servers
Create powerful collaborative AI workflows by connecting multiple MCP servers including Google Docs, OpenRouter, Monday for enhanced multi-agent automation capabilities in Klavis AI.
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
OpenRouter
Access to multiple AI models through a unified API. Generate chat completions, compare model performance, manage usage and costs, get model recommendations, and analyze model capabilities across various providers like OpenAI, Anthropic, Meta, Google, and more
Available Tools:
- openrouter_list_models
- openrouter_search_models
- openrouter_get_model_pricing
- +11 more tools
Monday
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
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_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,
)
openrouter_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.OPENROUTER,
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_docs_tools = MCPServerAdapter(google_docs_mcp_instance.server_params)
openrouter_tools = MCPServerAdapter(openrouter_mcp_instance.server_params)
monday_tools = MCPServerAdapter(monday_mcp_instance.server_params)
# Create specialized agents for each service
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
)
openrouter_agent = Agent(
role="OpenRouter Specialist",
goal="Handle all OpenRouter related tasks and data processing",
backstory="You are an expert in OpenRouter operations and data analysis",
tools=openrouter_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_docs_agent,
markdown=True
)
analysis_task = Task(
description="Analyze and synthesize the gathered data",
expected_output="Comprehensive analysis with insights and recommendations",
agent=openrouter_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[google_docs_agent, openrouter_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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