Create powerful AI workflows by connecting multiple MCP servers including Zendesk, LinkedIn, HubSpot for enhanced automation capabilities in Klavis AI.
Zendesk is a customer service software company
LinkedIn is a business and employment-oriented online 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"))
zendesk_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.ZENDESK,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
linkedin_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.LINKEDIN,
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({
"zendesk": {
"transport": "streamable_http",
"url": zendesk_mcp_instance.server_url
},
"linkedin": {
"transport": "streamable_http",
"url": linkedin_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"}]
}))
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