Connectto Linear, Doc2markdown, Motion MCP Servers
Create powerful AI workflows by connecting multiple MCP servers including Linear, Doc2markdown, Motion 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
Doc2markdown
Convert any file to markdown using markitdown
Available Tools:
- convert_document_to_markdown
Motion
Motion is an intelligent project management and calendar application that automatically schedules your tasks, meetings, and projects to optimize your productivity and help you focus on what matters most
Available Tools:
- motion_get_workspaces
- motion_get_users
- motion_get_my_user
- +11 more tools
Quick Setup Guide
Follow these steps to connect OpenAI 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 OpenAI and configure authentication settings.
Test & Deploy
Verify your connections work correctly and start using your enhanced AI capabilities.
OpenAI + KlavisAI Integration Snippets
import json
import os
from openai import OpenAI
from klavis import Klavis
from klavis.types import McpServerName, ConnectionType, ToolFormat
# Initialize clients
openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))
# Constants
OPENAI_MODEL = "gpt-4o-mini"
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,
)
doc2markdown_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.DOC2MARKDOWN,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
motion_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.MOTION,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
# Get tools from all MCP servers
linear_tools = klavis_client.mcp_server.list_tools(
server_url=linear_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.OPENAI,
)
doc2markdown_tools = klavis_client.mcp_server.list_tools(
server_url=doc2markdown_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.OPENAI,
)
motion_tools = klavis_client.mcp_server.list_tools(
server_url=motion_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.OPENAI,
)
# Combine all tools
all_tools = []
all_tools.extend(linear_tools)
all_tools.extend(doc2markdown_tools)
all_tools.extend(motion_tools)
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": user_message}
]
response = openai_client.chat.completions.create(
model=OPENAI_MODEL,
messages=messages,
tools=all_tools if all_tools else None
)Frequently Asked Questions
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