Connectto Doc2markdown, Tavily, Monday MCP Servers
Create powerful AI workflows by connecting multiple MCP servers including Doc2markdown, Tavily, Monday for enhanced automation capabilities in Klavis AI.
Doc2markdown
Convert any file to markdown using markitdown
Available Tools:
- convert_document_to_markdown
Tavily
Tavily is an AI-powered search API designed for LLMs and AI agents. Get real-time web search results, extract content from URLs, crawl websites, and generate site maps with advanced filtering and parsing capabilities
Available Tools:
- tavily_search
- tavily_extract
- tavily_crawl
- +1 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
Quick Setup Guide
Follow these steps to connect Together AI to these MCP servers
Create Your Account
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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"))
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,
)
tavily_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.TAVILY,
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,
)
# Get all MCP tools
doc2markdown_tools = klavis_client.mcp_server.list_tools(
server_url=doc2markdown_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.OPENAI,
)
tavily_tools = klavis_client.mcp_server.list_tools(
server_url=tavily_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.OPENAI,
)
monday_tools = klavis_client.mcp_server.list_tools(
server_url=monday_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.OPENAI,
)
# Combine all tools
all_tools = []
all_tools.extend(doc2markdown_tools.tools)
all_tools.extend(tavily_tools.tools)
all_tools.extend(monday_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
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