Connectto Linear, LinkedIn, Mem0 MCP Servers
Create powerful AI workflows by connecting multiple MCP servers including Linear, LinkedIn, Mem0 for enhanced automation capabilities in Klavis AI.
Linear
Linear is a modern issue tracking and project management tool designed for high-performance teams to build better software faster
Available Tools:
- linear_get_teams
- linear_get_issues
- linear_get_issue_by_id
- +9 more tools
LinkedIn is a business and employment-oriented online service
Available Tools:
- linkedin_get_profile_info
- linkedin_create_post
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 Together AI 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 Together AI and configure authentication settings.
Test & Deploy
Verify your connections work correctly and start using your enhanced AI capabilities.
Together AI + KlavisAI Integration Snippets
import os
import json
from together import Together
from klavis import Klavis
from klavis.types import McpServerName, ConnectionType, ToolFormat
# Initialize clients
together_client = Together(api_key=os.getenv("TOGETHER_API_KEY"))
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))
linear_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.LINEAR,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
linkedin_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.LINKEDIN,
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 all MCP tools
linear_tools = klavis_client.mcp_server.list_tools(
server_url=linear_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.OPENAI,
)
linkedin_tools = klavis_client.mcp_server.list_tools(
server_url=linkedin_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.OPENAI,
)
mem0_tools = klavis_client.mcp_server.list_tools(
server_url=mem0_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.OPENAI,
)
# Combine all tools
all_tools = []
all_tools.extend(linear_tools.tools)
all_tools.extend(linkedin_tools.tools)
all_tools.extend(mem0_tools.tools)
messages = [
{"role": "system", "content": "You are a helpful AI assistant with access to multiple data sources."},
{"role": "user", "content": user_message}
]
response = together_client.chat.completions.create(
model="meta-llama/Llama-2-70b-chat-hf",
messages=messages,
tools=all_tools
)Frequently Asked Questions
Everything you need to know about connecting to these MCP servers
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