Connectto ClickUp, Mem0, Google Jobs MCP Servers
Create powerful AI workflows by connecting multiple MCP servers including ClickUp, Mem0, Google Jobs for enhanced automation capabilities in Klavis AI.
ClickUp
ClickUp is a comprehensive project management and productivity platform that helps teams organize tasks, manage projects, and collaborate effectively with customizable workflows and powerful tracking features
Available Tools:
- clickup_get_teams
- clickup_get_workspaces
- clickup_get_spaces
- +18 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
Google Jobs
Google Jobs is a comprehensive job search platform that aggregates listings from across the web. Search for jobs by location, company, employment type, and more, with detailed information about requirements, benefits, and application processes
Available Tools:
- google_jobs_search
- google_jobs_get_details
- google_jobs_search_by_company
- +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"
clickup_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.CLICKUP,
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,
)
google_jobs_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.GOOGLE_JOBS,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
# Get tools from all MCP servers
clickup_tools = klavis_client.mcp_server.list_tools(
server_url=clickup_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,
)
google_jobs_tools = klavis_client.mcp_server.list_tools(
server_url=google_jobs_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.GEMINI,
)
# Combine all tools
all_tools = []
all_tools.extend(clickup_tools.tools)
all_tools.extend(mem0_tools.tools)
all_tools.extend(google_jobs_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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