Connectto Google Drive, Jira, Plai MCP Servers
Create powerful AI workflows by connecting multiple MCP servers including Google Drive, Jira, Plai for enhanced automation capabilities in Klavis AI.
Google Drive
Google Drive is a cloud storage service
Available Tools:
- google_drive_search_documents
- google_drive_search_and_retrieve_documents
- google_drive_get_file_tree_structure
Jira
Jira is a project management and issue tracking tool developed by Atlassian
Available Tools:
- jira_search
- jira_get_issue
- jira_search_fields
- +11 more tools
Plai
Plai is an AI-powered advertising platform that simplifies creating, managing, and optimizing Facebook, Instagram, and LinkedIn ad campaigns. It provides tools for lead generation, campaign insights, and automated ad management to help businesses scale their digital marketing efforts effectively.
Available Tools:
- plai_create_user_profile
- plai_get_user_profile
- plai_create_link
- +7 more tools
Quick Setup Guide
Follow these steps to connect Google Gemini to these MCP servers
Create Your Account
Sign up for KlavisAI to access our MCP server management platform.
Configure Connections
Add your desired MCP servers to Gemini and configure authentication settings.
Test & Deploy
Verify your connections work correctly and start using your enhanced AI capabilities.
Google Gemini + KlavisAI Integration Snippets
import os
from google import genai
from klavis import Klavis
from klavis.types import McpServerName, ConnectionType, 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"
google_drive_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.GOOGLE_DRIVE,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
jira_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.JIRA,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
plai_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.PLAI,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
# Get tools from all MCP servers
google_drive_tools = klavis_client.mcp_server.list_tools(
server_url=google_drive_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.GEMINI,
)
jira_tools = klavis_client.mcp_server.list_tools(
server_url=jira_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.GEMINI,
)
plai_tools = klavis_client.mcp_server.list_tools(
server_url=plai_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.GEMINI,
)
# Combine all tools
all_tools = []
all_tools.extend(google_drive_tools.tools)
all_tools.extend(jira_tools.tools)
all_tools.extend(plai_tools.tools)
response = client.models.generate_content(
model="gemini-2.5-flash",
contents=user_message,
config=genai.types.GenerateContentConfig(
tools=all_tools,
),
)Frequently Asked Questions
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