Connectto Markdown2doc, Heygen, Exa MCP Servers
Create powerful AI workflows by connecting multiple MCP servers including Markdown2doc, Heygen, Exa for enhanced automation capabilities in Klavis AI.
Markdown2doc
Convert markdown text to different file formats (pdf, docx, doc, html), based on Pandoc
Available Tools:
- convert_markdown_to_file
Heygen
HeyGen is an AI video generation platform that creates professional videos with AI avatars and voices. Generate avatar videos with customizable text, voices, and avatars, manage your video library, and track generation status
Available Tools:
- heygen_get_remaining_credits
- heygen_get_voices
- heygen_get_voice_locales
- +7 more tools
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
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"
markdown2doc_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.MARKDOWN2DOC,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
heygen_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.HEYGEN,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
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,
)
# Get tools from all MCP servers
markdown2doc_tools = klavis_client.mcp_server.list_tools(
server_url=markdown2doc_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.OPENAI,
)
heygen_tools = klavis_client.mcp_server.list_tools(
server_url=heygen_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.OPENAI,
)
exa_tools = klavis_client.mcp_server.list_tools(
server_url=exa_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.OPENAI,
)
# Combine all tools
all_tools = []
all_tools.extend(markdown2doc_tools)
all_tools.extend(heygen_tools)
all_tools.extend(exa_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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