How do I connect my AI agent to multiple MCP servers (Calendly, Zendesk)?

Connecting to multiple MCP servers like Calendly, Zendesk allows your AI agent to leverage capabilities from each service simultaneously. You can configure each server connection independently in your MCP client, and your AI agent will be able to use tools from all connected servers in a single conversation. Klavis AI simplifies this process with centralized connection management.

What are the benefits of using Calendly and Zendesk together?

Using Calendly and Zendesk together creates powerful synergies for your AI workflows. Each service brings unique capabilities that complement each other, enabling more comprehensive automation and data processing across multiple platforms.

Do I need authentication to use these MCP servers?

Authentication requirements depend on the specific MCP server and the operations you want to perform. Some of these servers require OAuth or API key authentication for full functionality. Klavis AI handles the authentication flow automatically once you provide the necessary credentials, making the setup process seamless.

What is the Model Context Protocol (MCP)?

The Model Context Protocol (MCP) is an open standard that enables AI assistants to securely connect to external tools and data sources. It provides a consistent way for AI agents to interact with various services, databases, and APIs while maintaining security and reliability.

Is it safe to connect my AI agent to these services?

Yes, MCP connections are designed with security in mind. All communications are secured, and you maintain control over what data and operations your AI agent can access. Klavis AI implements additional security measures and follows best practices for data protection, ensuring your connections are safe and reliable.

Can I use these MCP servers with any AI model?

MCP servers are designed to work with various AI models and clients that support the Model Context Protocol. Popular AI assistants like Claude, ChatGPT, and others can connect to MCP servers when properly configured.

How does Klavis AI make MCP server connections easier?

Klavis AI provides a comprehensive platform for managing MCP server connections with features like automated setup, centralized connection management, detailed configuration guides, and secure credential storage. Our platform simplifies the entire process from discovery to deployment, making it easy for both beginners and experts to leverage the power of MCP servers.

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Connect Using Klavis UI

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View Step by Step Guide

Navigate to Klavis Home

Visit the Klavis home page and you will see a list of MCP servers available in Klavis.

Authorize Your Servers

Click the "Authorize" button next to your chosen servers. Once servers are authorized, you will see a Green Checkmark status.

Add to Your AI Client

Click "Add to Cursor", "Add to VS Code", "Add to Claude" or "Add to Other Clients" button to connect the MCP server to your preferred AI client.

Connect Using API

Programmatically connect your AI agents to these MCP servers

Get Your API Key

Configure Connections

Use the code examples below to add your desired MCP servers to your AI client and configure authentication settings.

Test & Deploy

Verify your connections work correctly and start using your enhanced AI capabilities.

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View Documentation

from klavis import Klavis
from klavis.types import McpServerName

klavis_client = Klavis(api_key="KLAVIS_API_KEY")

# Create strata server with all MCP servers
response = klavis_client.mcp_server.create_strata_server(
    servers=[McpServerName.CALENDLY, McpServerName.ZENDESK],
    user_id="<USER_ID>"
)

mcp_server_url = response.strata_server_url

Code Examples for claude

import os
from anthropic import Anthropic
from klavis import Klavis
from klavis.types import McpServerName, ToolFormat

# Initialize clients
anthropic_client = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))

# Constants
CLAUDE_MODEL = "claude-3-5-sonnet-20241022"
user_message = "Your message here"

# Create strata server with all MCP servers
response = klavis_client.mcp_server.create_strata_server(
    servers=[McpServerName.CALENDLY, McpServerName.ZENDESK],
    user_id="1234"
)

mcp_server_url = response.strata_server_url

# Get tools from the strata server
mcp_tools = klavis_client.mcp_server.list_tools(
    server_url=mcp_server_url,
    format=ToolFormat.ANTHROPIC,
)

messages = [
    {"role": "user", "content": user_message}
]

response = anthropic_client.messages.create(
    model=CLAUDE_MODEL,
    max_tokens=4000,
    messages=messages,
    tools=mcp_tools.tools
)

import Anthropic from '@anthropic-ai/sdk';
import { KlavisClient, Klavis } from 'klavis';

// Initialize clients
const anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });
const klavisClient = new KlavisClient({ apiKey: process.env.KLAVIS_API_KEY });

const CLAUDE_MODEL = "claude-3-5-sonnet-20241022";
const userMessage = "Your message here";

// Create strata server with all MCP servers
const strataResponse = await klavisClient.mcpServer.createStrataServer({
    servers: [Klavis.McpServerName.Calendly, Klavis.McpServerName.Zendesk],
    userId: "1234"
});

const mcpServerUrl = strataResponse.strataServerUrl;

// Get tools from the strata server
const mcpTools = await klavisClient.mcpServer.listTools({
    serverUrl: mcpServerUrl,
    format: Klavis.ToolFormat.Anthropic,
});

const response = await anthropic.messages.create({
    model: CLAUDE_MODEL,
    max_tokens: 4000,
    messages: [{ role: 'user', content: userMessage }],
    tools: mcpTools.tools,
});

Code Examples for openai

import os
from openai import OpenAI
from klavis import Klavis
from klavis.types import McpServerName, ToolFormat

# Initialize clients
openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))

# Constants
OPENAI_MODEL = "gpt-4o-mini"
user_message = "Your query here"

# Create strata server with all MCP servers
response = klavis_client.mcp_server.create_strata_server(
    servers=[McpServerName.CALENDLY, McpServerName.ZENDESK],
    user_id="1234"
)

mcp_server_url = response.strata_server_url

# Get tools from the strata server
mcp_tools = klavis_client.mcp_server.list_tools(
    server_url=mcp_server_url,
    format=ToolFormat.OPENAI,
)

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": user_message}
]

response = openai_client.chat.completions.create(
    model=OPENAI_MODEL,
    messages=messages,
    tools=mcp_tools
)

import OpenAI from 'openai';
import { KlavisClient, Klavis } from 'klavis';

// Initialize clients
const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
const klavisClient = new KlavisClient({ apiKey: process.env.KLAVIS_API_KEY });

const OPENAI_MODEL = "gpt-4o-mini";
const userMessage = "Your query here";

// Create strata server with all MCP servers
const strataResponse = await klavisClient.mcpServer.createStrataServer({
    servers: [Klavis.McpServerName.Calendly, Klavis.McpServerName.Zendesk],
    userId: "1234"
});

const mcpServerUrl = strataResponse.strataServerUrl;

// Get tools from the strata server
const mcpTools = await klavisClient.mcpServer.listTools({
    serverUrl: mcpServerUrl,
    format: Klavis.ToolFormat.Openai,
});

const response = await openai.chat.completions.create({
    model: OPENAI_MODEL,
    messages: [
        { role: 'system', content: 'You are a helpful assistant.' },
        { role: 'user', content: userMessage }
    ],
    tools: mcpTools,
});

Code Examples for gemini

import os
from google import genai
from klavis import Klavis
from klavis.types import McpServerName, ToolFormat

# Initialize clients
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))
client = genai.Client(api_key=os.getenv("GOOGLE_API_KEY"))

user_message = "Your query here"

# Create strata server with all MCP servers
response = klavis_client.mcp_server.create_strata_server(
    servers=[McpServerName.CALENDLY, McpServerName.ZENDESK],
    user_id="1234"
)

mcp_server_url = response.strata_server_url

# Get tools from the strata server
mcp_tools = klavis_client.mcp_server.list_tools(
    server_url=mcp_server_url,
    format=ToolFormat.GEMINI,
)

response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents=user_message,
    config=genai.types.GenerateContentConfig(
        tools=mcp_tools.tools,
    ),
)

import { GoogleGenAI } from '@google/genai';
import { KlavisClient, Klavis } from 'klavis';

// Initialize clients
const ai = new GoogleGenAI({ apiKey: process.env.GOOGLE_API_KEY });
const klavisClient = new KlavisClient({ apiKey: process.env.KLAVIS_API_KEY });

const userMessage = "Your query here";

// Create strata server with all MCP servers
const strataResponse = await klavisClient.mcpServer.createStrataServer({
    servers: [Klavis.McpServerName.Calendly, Klavis.McpServerName.Zendesk],
    userId: "1234"
});

const mcpServerUrl = strataResponse.strataServerUrl;

// Get tools from the strata server
const mcpTools = await klavisClient.mcpServer.listTools({
    serverUrl: mcpServerUrl,
    format: Klavis.ToolFormat.Gemini,
});

const response = await ai.models.generateContent({
    model: "gemini-2.5-flash",
    contents: userMessage,
    tools: mcpTools.tools,
});

Code Examples for langchain

import os
import asyncio
from klavis import Klavis
from klavis.types import McpServerName
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"))

# Create strata server with all MCP servers
response = klavis_client.mcp_server.create_strata_server(
    servers=[McpServerName.CALENDLY, McpServerName.ZENDESK],
    user_id="1234"
)

mcp_server_url = response.strata_server_url

mcp_client = MultiServerMCPClient({
    "strata": {
        "transport": "streamable_http",
        "url": mcp_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"}]
}))

import { KlavisClient, Klavis } from 'klavis';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { ChatOpenAI } from "@langchain/openai";
import { createReactAgent } from "@langchain/langgraph/prebuilt";

// Initialize clients
const klavisClient = new KlavisClient({ apiKey: process.env.KLAVIS_API_KEY });
const llm = new ChatOpenAI({ model: "gpt-4o-mini", apiKey: process.env.OPENAI_API_KEY });

// Create strata server with all MCP servers
const strataResponse = await klavisClient.mcpServer.createStrataServer({
    servers: [Klavis.McpServerName.Calendly, Klavis.McpServerName.Zendesk],
    userId: "1234"
});

const mcpServerUrl = strataResponse.strataServerUrl;

const mcpClient = new MultiServerMCPClient({
    "strata": {
        transport: "streamable_http",
        url: mcpServerUrl
    }
});

const tools = await mcpClient.getTools();

const agent = createReactAgent({
    llm: llm,
    tools: tools,
});

const response = await agent.invoke({
    messages: [{ role: "user", content: "Your query here" }]
});

Code Examples for llamaindex

import os
from klavis import Klavis
from klavis.types import McpServerName
from llama_index.tools.mcp import (
    BasicMCPClient,
    aget_tools_from_mcp_url,
)
from llama_index.core.agent.workflow import FunctionAgent

# Initialize clients
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))

# Create strata server with all MCP servers
response = klavis_client.mcp_server.create_strata_server(
    servers=[McpServerName.CALENDLY, McpServerName.ZENDESK],
    user_id="1234"
)

mcp_server_url = response.strata_server_url

# Get tools from the strata server
strata_tools = await aget_tools_from_mcp_url(
    mcp_server_url, 
    client=BasicMCPClient(mcp_server_url)
)

# Create agent with all tools
agent = FunctionAgent(
    name="strata_agent",
    tools=strata_tools,
    llm=llm,
)

import { KlavisClient, Klavis } from 'klavis';
import { mcp } from "@llamaindex/tools";
import { agent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/llm";

// Initialize clients
const klavisClient = new KlavisClient({ apiKey: process.env.KLAVIS_API_KEY });

// Create strata server with all MCP servers
const strataResponse = await klavisClient.mcpServer.createStrataServer({
    servers: [Klavis.McpServerName.Calendly, Klavis.McpServerName.Zendesk],
    userId: "1234"
});

const mcpServerUrl = strataResponse.strataServerUrl;

// Create MCP server connection
const strataServer = mcp({
    url: mcpServerUrl,
    verbose: true,
});

// Get tools from strata server
const strataTools = await strataServer.tools();

// Create agent with all tools
const strataAgent = agent({
    name: "strata_agent",
    llm: openai({ model: "gpt-4o" }),
    tools: strataTools,
});

Code Examples for crewai

import os
from crewai import Agent, Task, Crew, Process
from crewai_tools import MCPServerAdapter
from klavis import Klavis
from klavis.types import McpServerName

# Initialize clients
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))

# Create strata server with all MCP servers
response = klavis_client.mcp_server.create_strata_server(
    servers=[McpServerName.CALENDLY, McpServerName.ZENDESK],
    user_id="1234"
)

mcp_server_url = response.strata_server_url

# Initialize MCP tools from strata server
with MCPServerAdapter(mcp_server_url) as mcp_tools:
    # Create agent with access to all MCP server tools
    strata_agent = Agent(
        role="Multi-Service Specialist",
        goal="Handle tasks across multiple services and data sources",
        backstory="You are an expert at coordinating and analyzing data from multiple services",
        tools=mcp_tools,
        reasoning=True,
        verbose=False
    )
    
    # Define Task
    research_task = Task(
        description="Gather and analyze comprehensive data from all available sources",
        expected_output="Complete analysis with structured summary and key insights",
        agent=strata_agent,
        markdown=True
    )
    
    # Create and execute the crew
    crew = Crew(
        agents=[strata_agent],
        tasks=[research_task],
        verbose=False,
        process=Process.sequential
    )
    
    result = crew.kickoff()

// CrewAI currently only supports Python. Please use the Python example.

Code Examples for fireworks-ai

import os
from fireworks.client import Fireworks
from klavis import Klavis
from klavis.types import McpServerName, ToolFormat

# Initialize clients
fireworks_client = Fireworks(api_key=os.getenv("FIREWORKS_API_KEY"))
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))

user_message = "Your query here"

# Create strata server with all MCP servers
response = klavis_client.mcp_server.create_strata_server(
    servers=[McpServerName.CALENDLY, McpServerName.ZENDESK],
    user_id="1234"
)

mcp_server_url = response.strata_server_url

# Get tools from the strata server
mcp_tools = klavis_client.mcp_server.list_tools(
    server_url=mcp_server_url,
    format=ToolFormat.OPENAI,
)

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": user_message}
]

response = fireworks_client.chat.completions.create(
    model="accounts/fireworks/models/llama-v3p1-70b-instruct",
    messages=messages,
    tools=mcp_tools.tools
)

import Fireworks from 'fireworks-ai';
import { KlavisClient, Klavis } from 'klavis';

// Initialize clients
const fireworks = new Fireworks({ apiKey: process.env.FIREWORKS_API_KEY });
const klavisClient = new KlavisClient({ apiKey: process.env.KLAVIS_API_KEY });

const userMessage = "Your query here";

// Create strata server with all MCP servers
const strataResponse = await klavisClient.mcpServer.createStrataServer({
    servers: [Klavis.McpServerName.Calendly, Klavis.McpServerName.Zendesk],
    userId: "1234"
});

const mcpServerUrl = strataResponse.strataServerUrl;

// Get tools from the strata server
const mcpTools = await klavisClient.mcpServer.listTools({
    serverUrl: mcpServerUrl,
    format: Klavis.ToolFormat.Openai,
});

const response = await fireworks.chat.completions.create({
    model: "accounts/fireworks/models/llama-v3p1-70b-instruct",
    messages: [
        { role: "system", content: "You are a helpful assistant." },
        { role: "user", content: userMessage }
    ],
    tools: mcpTools.tools,
});

Code Examples for together-ai

import os
from together import Together
from klavis import Klavis
from klavis.types import McpServerName, ToolFormat

# Initialize clients
together_client = Together(api_key=os.getenv("TOGETHER_API_KEY"))
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))

user_message = "Your query here"

# Create strata server with all MCP servers
response = klavis_client.mcp_server.create_strata_server(
    servers=[McpServerName.CALENDLY, McpServerName.ZENDESK],
    user_id="1234"
)

mcp_server_url = response.strata_server_url

# Get tools from the strata server
mcp_tools = klavis_client.mcp_server.list_tools(
    server_url=mcp_server_url,
    format=ToolFormat.OPENAI,
)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant with access to various tools."},
    {"role": "user", "content": user_message}
]

response = together_client.chat.completions.create(
    model="meta-llama/Llama-2-70b-chat-hf",
    messages=messages,
    tools=mcp_tools.tools
)

import Together from 'together-ai';
import { KlavisClient, Klavis } from 'klavis';

// Initialize clients
const togetherClient = new Together({ apiKey: process.env.TOGETHER_API_KEY });
const klavisClient = new KlavisClient({ apiKey: process.env.KLAVIS_API_KEY });

const userMessage = "Your query here";

// Create strata server with all MCP servers
const strataResponse = await klavisClient.mcpServer.createStrataServer({
    servers: [Klavis.McpServerName.Calendly, Klavis.McpServerName.Zendesk],
    userId: "1234"
});

const mcpServerUrl = strataResponse.strataServerUrl;

// Get tools from the strata server
const mcpTools = await klavisClient.mcpServer.listTools({
    serverUrl: mcpServerUrl,
    format: Klavis.ToolFormat.Openai,
});

const response = await togetherClient.chat.completions.create({
    model: "meta-llama/Llama-2-70b-chat-hf",
    messages: [
        { role: "system", content: "You are a helpful AI assistant with access to various tools." },
        { role: "user", content: userMessage }
    ],
    tools: mcpTools.tools,
});

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