Connectto Markdown2doc, Google Docs, Plai MCP Servers
Create powerful AI workflows by connecting multiple MCP servers including Markdown2doc, Google Docs, Plai for enhanced automation capabilities in Klavis AI.
Markdown2doc
Convert markdown text to different file formats (pdf, docx, doc, html), based on Pandoc
Available Tools:
- convert_markdown_to_file
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
Plai
Plai is an AI-powered advertising platform that simplifies creating, managing, and optimizing Facebook, Instagram, and LinkedIn ad campaigns. It provides tools for lead generation, campaign insights, and automated ad management to help businesses scale their digital marketing efforts effectively.
Available Tools:
- plai_create_user_profile
- plai_get_user_profile
- plai_create_link
- +7 more tools
Quick Setup Guide
Follow these steps to connect LangChain to these MCP servers
Create Your Account
Sign up for KlavisAI to access our MCP server management platform.
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"))
markdown2doc_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.MARKDOWN2DOC,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
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,
)
plai_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.PLAI,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
mcp_client = MultiServerMCPClient({
"markdown2doc": {
"transport": "streamable_http",
"url": markdown2doc_mcp_instance.server_url
},
"google docs": {
"transport": "streamable_http",
"url": google_docs_mcp_instance.server_url
},
"plai": {
"transport": "streamable_http",
"url": plai_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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