Create powerful AI workflows by connecting multiple MCP servers including Google Sheets, Google Drive, HubSpot for enhanced automation capabilities in Klavis AI.
Google Sheets is a web-based spreadsheet application that allows users to create, edit, and collaborate on spreadsheets online
Google Drive is a cloud storage service
HubSpot is a developer and marketer of software products for inbound marketing, sales, and customer service
Follow these steps to connect LangChain to these MCP servers
Sign up for KlavisAI to access our MCP server management platform.
Add your desired MCP servers to LangChain and configure authentication settings.
Verify your connections work correctly and start using your enhanced AI capabilities.
import os
import asyncio
from klavis import Klavis
from klavis.types import McpServerName, ConnectionType
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI
# Initialize clients
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))
llm = ChatOpenAI(model="gpt-4o-mini", api_key=os.getenv("OPENAI_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,
)
google_drive_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.GOOGLE_DRIVE,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
hubspot_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.HUBSPOT,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
mcp_client = MultiServerMCPClient({
"google sheets": {
"transport": "streamable_http",
"url": google_sheets_mcp_instance.server_url
},
"google drive": {
"transport": "streamable_http",
"url": google_drive_mcp_instance.server_url
},
"hubspot": {
"transport": "streamable_http",
"url": hubspot_mcp_instance.server_url
}
})
tools = asyncio.run(mcp_client.get_tools())
agent = create_react_agent(
model=llm,
tools=tools,
)
response = asyncio.run(agent.ainvoke({
"messages": [{"role": "user", "content": "Your query here"}]
}))
Everything you need to know about connecting to these MCP servers
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