Prerequisites
Before we begin, you’ll need:
Installation
First, install the required packages:
pip install anthropic klavis
Setup Environment Variables
import os
os.environ["ANTHROPIC_API_KEY"] = "YOUR_ANTHROPIC_API_KEY" # Replace with your actual Anthropic API key
os.environ["KLAVIS_API_KEY"] = "YOUR_KLAVIS_API_KEY" # Replace with your actual Klavis API key
Step 1 - Create Strata MCP Server with Gmail and Slack
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}")
OAuth Authorization Required: The code above will open browser windows for each service. Click through the OAuth flow to authorize access to your accounts.
Step 2 - Create method to use MCP Server with Claude
This method handles multiple rounds of tool calls until a final response is ready, allowing the AI to chain tool executions for complex tasks.
import json
from anthropic import Anthropic
def claude_with_mcp_server(mcp_server_url: str, user_query: str):
claude_client = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))
messages = [
{"role": "user", "content": f"{user_query}"}
]
mcp_server_tools = klavis_client.mcp_server.list_tools(
server_url=mcp_server_url,
format=ToolFormat.ANTHROPIC
)
max_iterations = 10
iteration = 0
while iteration < max_iterations:
iteration += 1
response = claude_client.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=4000,
system="You are a helpful assistant. Use the available tools to answer the user's question.",
messages=messages,
tools=mcp_server_tools.tools
)
messages.append({"role": "assistant", "content": response.content})
if response.stop_reason == "tool_use":
tool_results = []
for content_block in response.content:
if content_block.type == "tool_use":
function_name = content_block.name
function_args = content_block.input
print(f"🔧 Calling: {function_name}, with args: {function_args}")
result = klavis_client.mcp_server.call_tools(
server_url=mcp_server_url,
tool_name=function_name,
tool_args=function_args
)
tool_results.append({
"type": "tool_result",
"tool_use_id": content_block.id,
"content": str(result)
})
messages.append({"role": "user", "content": tool_results})
continue
else:
return response.content[0].text
return "Max iterations reached without final response"
Step 3 - Run!
result = claude_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}")
Perfect! You’ve integrated Claude with Klavis MCP servers.
Next Steps
Useful Resources
Happy building! 🚀