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Prerequisites

Before we begin, you’ll need:

OpenAI API Key

Get your API key from OpenAI Console

Klavis AI API Key

Get your API key from Klavis AI

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Installation

First, install the required packages: 
pip install openai klavis agno

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Setup Environment Variables

import os

os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"  # Replace with your actual OpenAI API key
os.environ["KLAVIS_API_KEY"] = "YOUR_KLAVIS_API_KEY"  # Replace with your actual Klavis API key

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Step 1 - Create Strata MCP Server with Gmail and Slack

import webbrowser
from klavis import Klavis
from klavis.types import McpServerName, ToolFormat


klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))

response = klavis_client.mcp_server.create_strata_server(
    servers=[McpServerName.GMAIL, McpServerName.SLACK], 
    user_id="1234"
)

# Handle OAuth authorization for each services
if response.oauth_urls:
    for server_name, oauth_url in response.oauth_urls.items():
        webbrowser.open(oauth_url)
        print(f"Or please open this URL to complete {server_name} OAuth authorization: {oauth_url}")
OAuth Authorization Required: The code above will open browser windows for each service. Click through the OAuth flow to authorize access to your accounts.

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Step 2 - Create method to use MCP Server with Claude

This method handles multiple rounds of tool calls until a final response is ready. 
import json
import asyncio
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.mcp import MCPTools


async def agno_with_mcp_server(mcp_server_url: str, user_query: str):
    """Run an Agno agent with Klavis MCP server tools."""

    async with MCPTools(transport="streamable-http", url=mcp_server_url) as mcp_tools:
        agent = Agent(
            model=OpenAIChat(
                id="gpt-4o",
                api_key=os.getenv("OPENAI_API_KEY")
            ),
            instructions="You are a helpful AI assistant.",
            tools=[mcp_tools],
            markdown=True,
        )

        response = await agent.arun(user_query)
        return response.content

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Step 3 - Run!

async def main():
    result = await agno_with_mcp_server(
        mcp_server_url=response.strata_server_url,
        user_query="Check my latest 5 emails and summarize them in a Slack message to #general"
    )
    print(f"\nFinal Response: {result}")


if __name__ == "__main__":
    asyncio.run(main())
Perfect! You’ve integrated Agno with Klavis MCP servers.

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Next Steps

Integrations

Explore available MCP servers

API Reference

REST endpoints and schemas

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Useful Resources

Happy building! 🚀