Connectto Mixpanel, LinkedIn MCP Servers
Create powerful AI workflows by connecting multiple MCP servers including Mixpanel, LinkedIn for enhanced automation capabilities in Klavis AI.
Mixpanel
Mixpanel is a powerful product analytics platform that helps teams understand user behavior, track events, analyze conversion funnels, measure retention, and make data-driven decisions with real-time insights and advanced segmentation capabilities
Available Tools:
- mixpanel_send_events
- mixpanel_get_events
- mixpanel_get_event_properties
- +6 more tools
LinkedIn is a business and employment-oriented online service
Available Tools:
- linkedin_get_profile_info
- linkedin_create_post
Quick Setup Guide
Follow these steps to connect LangChain to these MCP servers
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Configure Connections
Add your desired MCP servers to LangChain and configure authentication settings.
Test & Deploy
Verify your connections work correctly and start using your enhanced AI capabilities.
LangChain + KlavisAI Integration Snippets
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"))
mixpanel_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.MIXPANEL,
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,
)
mcp_client = MultiServerMCPClient({
"mixpanel": {
"transport": "streamable_http",
"url": mixpanel_mcp_instance.server_url
},
"linkedin": {
"transport": "streamable_http",
"url": linkedin_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"}]
}))Frequently Asked Questions
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