Connectto Exa, Monday, Mem0 MCP Servers
Create powerful AI workflows by connecting multiple MCP servers including Exa, Monday, Mem0 for enhanced automation capabilities in Klavis AI.
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
Monday
Monday.com is a work operating system that powers teams to run projects and workflows with confidence. Create boards, manage items, customize columns, organize groups, and collaborate with team members in a visual workspace
Available Tools:
- monday_get_users_by_name
- monday_get_boards
- monday_create_board
- +9 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 LlamaIndex 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 LlamaIndex and configure authentication settings.
Test & Deploy
Verify your connections work correctly and start using your enhanced AI capabilities.
LlamaIndex + KlavisAI Integration Snippets
import os
from klavis import Klavis
from klavis.types import McpServerName, ConnectionType
from llama_index.tools.mcp import (
BasicMCPClient,
get_tools_from_mcp_url,
aget_tools_from_mcp_url,
)
from llama_index.core.agent.workflow import FunctionAgent, AgentWorkflow
# Initialize clients
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))
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,
)
monday_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.MONDAY,
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,
)
exa_tools = await aget_tools_from_mcp_url(
exa_mcp_instance.server_url,
client=BasicMCPClient(exa_mcp_instance.server_url)
)
monday_tools = await aget_tools_from_mcp_url(
monday_mcp_instance.server_url,
client=BasicMCPClient(monday_mcp_instance.server_url)
)
mem0_tools = await aget_tools_from_mcp_url(
mem0_mcp_instance.server_url,
client=BasicMCPClient(mem0_mcp_instance.server_url)
)
exa_agent = FunctionAgent(
name="exa_agent",
tools=exa_tools,
llm=llm,
)
monday_agent = FunctionAgent(
name="monday_agent",
tools=monday_tools,
llm=llm,
)
mem0_agent = FunctionAgent(
name="mem0_agent",
tools=mem0_tools,
llm=llm,
)
workflow = AgentWorkflow(
agents=[exa_agent, monday_agent, mem0_agent],
root_agent="exa_agent",
)Frequently Asked Questions
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