Connectto Google Drive, Exa, Mem0 MCP Servers
Create powerful AI workflows by connecting multiple MCP servers including Google Drive, Exa, Mem0 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
Exa
Exa is an AI-powered search engine designed for AI applications. Use neural search to understand meaning and context, find similar content, get direct answers with citations, and conduct comprehensive research with structured analysis
Available Tools:
- exa_search
- exa_get_contents
- exa_find_similar
- +2 more tools
Mem0
Mem0 is an intelligent memory layer for AI applications that provides long-term memory storage and retrieval. Store code snippets, implementation details, and programming knowledge for seamless context retention across conversations
Available Tools:
- mem0_add_memory
- mem0_get_all_memories
- mem0_search_memories
- +2 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,
)
exa_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.EXA,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
mem0_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.MEM0,
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,
)
exa_tools = klavis_client.mcp_server.list_tools(
server_url=exa_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.GEMINI,
)
mem0_tools = klavis_client.mcp_server.list_tools(
server_url=mem0_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(exa_tools.tools)
all_tools.extend(mem0_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
Everything you need to know about connecting to these MCP servers
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