Create powerful AI workflows by connecting multiple MCP servers including Google Sheets, Jira, Postgres 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
Jira is a project management and issue tracking tool developed by Atlassian
PostgreSQL is a powerful, open source object-relational database system
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,
)
jira_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.JIRA,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
postgres_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.POSTGRES,
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
},
"jira": {
"transport": "streamable_http",
"url": jira_mcp_instance.server_url
},
"postgres": {
"transport": "streamable_http",
"url": postgres_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
Join developers who are already using KlavisAI to power their LangChain applications with these MCP servers.
Start For Free