Connectto Calendly MCP Server

Seamlessly integrate your LangChain and AI applications with Calendly using Klavis AI's comprehensive MCP server connection guide.

Calendly icon

Calendly

coming soon

Manage scheduling and appointments with your agents.

Quick Setup Guide

Follow these steps to connect LangChain to this MCP server

1

Create Your Account

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

2

Configure Connections

Add the MCP server 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"))

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

mcp_client = MultiServerMCPClient({
    "calendly": {
        "transport": "streamable_http",
        "url": calendly_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 this MCP server

Ready to Get Started?

Join developers who are already using KlavisAI to power their LangChain applications with this MCP server.

Start For Free