OpenAI

pip install openai klavis

import os

os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"  # Replace
os.environ["KLAVIS_API_KEY"] = "YOUR_KLAVIS_API_KEY"  # Replace

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

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}")

import json
from openai import OpenAI

def openai_with_mcp_server(mcp_server_url: str, user_query: str):
    openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

    messages = [
        {"role": "system", "content": "You are a helpful assistant. Use the available tools to answer the user's question."},
        {"role": "user", "content": f"{user_query}"}
    ]
    
    tools_info = klavis_client.mcp_server.list_tools(
        server_url=mcp_server_url,
        format=ToolFormat.OPENAI
    )
    
    max_iterations = 10
    iteration = 0
    
    while iteration < max_iterations:
        iteration += 1
        
        response = openai_client.chat.completions.create(
            model="gpt-4o-mini",
            messages=messages,
            tools=tools_info.tools,
            tool_choice="auto",
        )
        
        assistant_message = response.choices[0].message
        
        if assistant_message.tool_calls:
            messages.append({
                "role": "assistant",
                "content": assistant_message.content,
                "tool_calls": [
                    {
                        "id": tc.id,
                        "type": "function",
                        "function": {
                            "name": tc.function.name,
                            "arguments": tc.function.arguments
                        }
                    }
                    for tc in assistant_message.tool_calls
                ]
            })
            
            for tool_call in assistant_message.tool_calls:
                tool_name = tool_call.function.name
                tool_args = json.loads(tool_call.function.arguments)
                
                print(f"Calling: {tool_name}")
                print(f"Arguments: {json.dumps(tool_args, indent=2)}")
                
                function_result = klavis_client.mcp_server.call_tools(
                    server_url=mcp_server_url,
                    tool_name=tool_name,
                    tool_args=tool_args
                )
                                
                messages.append({
                    "role": "tool",
                    "tool_call_id": tool_call.id,
                    "content": str(function_result)
                })
            continue
        else:
            messages.append({"role": "assistant", "content": assistant_message.content})
            return assistant_message.content
    
    return "Max iterations reached without final response"

result = openai_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"\n🤖 Final Response: {result}")