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Connectto Asana, OneDrive, Slack MCP Servers

Create powerful AI workflows by connecting multiple MCP servers including Asana, OneDrive, Slack for enhanced automation capabilities in Klavis AI.

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Asana icon

Asana

featured

Asana is a web and mobile application designed to help teams organize, track, and manage their work. It provides project management tools, task assignment, collaboration features, and progress tracking to boost team productivity

Available Tools:

  • asana_create_task
  • asana_get_task
  • asana_search_tasks
  • +16 more tools
OneDrive icon

OneDrive

coming soon

OneDrive is a file hosting service and synchronization service operated by Microsoft

Slack icon

Slack

featured

Slack is a messaging app for business that connects people to the information they need

Available Tools:

  • slack_list_channels
  • slack_post_message
  • slack_reply_to_thread
  • +6 more tools

Quick Setup Guide

Follow these steps to connect Google Gemini to these MCP servers

1

Create Your Account

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

2

Configure Connections

Add your desired MCP servers to Gemini and configure authentication settings.

3

Test & Deploy

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

Google Gemini + KlavisAI Integration Snippets

View Documentation
import os
import google.generativeai as genai
from klavis import Klavis
from klavis.types import McpServerName, ConnectionType, ToolFormat

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

# Constants
GEMINI_MODEL = "gemini-2.5-flash"
user_message = "Your query here"

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

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

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

# Get tools from all MCP servers
asana_tools = klavis_client.mcp_server.list_tools(
    server_url=asana_mcp_instance.server_url,
    connection_type=ConnectionType.STREAMABLE_HTTP,
    format=ToolFormat.GEMINI,
)
onedrive_tools = klavis_client.mcp_server.list_tools(
    server_url=onedrive_mcp_instance.server_url,
    connection_type=ConnectionType.STREAMABLE_HTTP,
    format=ToolFormat.GEMINI,
)
slack_tools = klavis_client.mcp_server.list_tools(
    server_url=slack_mcp_instance.server_url,
    connection_type=ConnectionType.STREAMABLE_HTTP,
    format=ToolFormat.GEMINI,
)

# Combine all tools
all_tools = []
all_tools.extend(asana_tools.tools)
all_tools.extend(onedrive_tools.tools)
all_tools.extend(slack_tools.tools)

model = genai.GenerativeModel(
    model_name=GEMINI_MODEL,
    tools=all_tools
)

chat = model.start_chat()
response = chat.send_message(user_message)

Frequently Asked Questions

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