Connectto Doc2markdown, LinkedIn, Klavis ReportGen MCP Servers

Create powerful AI workflows by connecting multiple MCP servers including Doc2markdown, LinkedIn, Klavis ReportGen for enhanced automation capabilities in Klavis AI.

Doc2markdown icon

Doc2markdown

featured

Convert any file to markdown using markitdown

Available Tools:

  • convert_document_to_markdown
LinkedIn icon

LinkedIn

coming soon

LinkedIn is a business and employment-oriented online service

Klavis ReportGen icon

Klavis ReportGen

featured

Generate visually appealing JavaScript web reports from search queries with Klavis AI.

Available Tools:

  • generate_web_reports

Quick Setup Guide

Follow these steps to connect LangChain to these MCP servers

1

Create Your Account

Sign up for KlavisAI to access our MCP server management platform.

2

Configure Connections

Add your desired MCP servers to LangChain and configure authentication settings.

3

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"))

doc2markdown_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.DOC2MARKDOWN,
    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,
)

klavis_reportgen_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.KLAVIS_REPORTGEN,
    user_id="1234",
    platform_name="Klavis",
    connection_type=ConnectionType.STREAMABLE_HTTP,
)

mcp_client = MultiServerMCPClient({
    "doc2markdown": {
        "transport": "streamable_http",
        "url": doc2markdown_mcp_instance.server_url
    },
    "linkedin": {
        "transport": "streamable_http",
        "url": linkedin_mcp_instance.server_url
    },
    "klavis reportgen": {
        "transport": "streamable_http",
        "url": klavis_reportgen_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

Everything you need to know about connecting to these MCP servers

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