Connectto Markdown2doc, Cloudflare, Firecrawl Deep Research MCP Servers

Create powerful AI workflows by connecting multiple MCP servers including Markdown2doc, Cloudflare, Firecrawl Deep Research for enhanced automation capabilities in Klavis AI.

Markdown2doc icon

Markdown2doc

featured

Convert markdown text to different file formats (pdf, docx, doc, html), based on Pandoc

Available Tools:

  • convert_markdown_to_file
Cloudflare icon

Cloudflare

featured

Cloudflare provides content delivery network services, DDoS protection, and security.

Firecrawl Deep Research icon

Firecrawl Deep Research

featured

A personal research assistant that analyze sources across the web, based on Firecrawl

Available Tools:

  • firecrawl_deep_research

Quick Setup Guide

Follow these steps to connect Google Gemini to these MCP servers

1

Create Your Account

Sign up for KlavisAI to access our MCP server management platform.

2

Configure Connections

Add your desired MCP servers to Gemini and configure authentication settings.

3

Test & Deploy

Verify your connections work correctly and start using your enhanced AI capabilities.

Google Gemini + KlavisAI Integration Snippets

import os
import google.generativeai as genai
from klavis import Klavis
from klavis.types import McpServerName, ConnectionType, ToolFormat

# Initialize clients
genai.configure(api_key=os.getenv("GOOGLE_AI_API_KEY"))
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))

# Constants
GEMINI_MODEL = "gemini-2.5-flash"
user_message = "Your query here"

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,
)

cloudflare_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.CLOUDFLARE,
    user_id="1234",
    platform_name="Klavis",
    connection_type=ConnectionType.STREAMABLE_HTTP,
)

firecrawl_deep_research_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.FIRECRAWL_DEEP_RESEARCH,
    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.GEMINI,
)
cloudflare_tools = klavis_client.mcp_server.list_tools(
    server_url=cloudflare_mcp_instance.server_url,
    connection_type=ConnectionType.STREAMABLE_HTTP,
    format=ToolFormat.GEMINI,
)
firecrawl_deep_research_tools = klavis_client.mcp_server.list_tools(
    server_url=firecrawl_deep_research_mcp_instance.server_url,
    connection_type=ConnectionType.STREAMABLE_HTTP,
    format=ToolFormat.GEMINI,
)

# Combine all tools
all_tools = []
all_tools.extend(markdown2doc_tools.tools)
all_tools.extend(cloudflare_tools.tools)
all_tools.extend(firecrawl_deep_research_tools.tools)

model = genai.GenerativeModel(
    model_name=GEMINI_MODEL,
    tools=all_tools
)

chat = model.start_chat()
response = chat.send_message(user_message)

Frequently Asked Questions

Everything you need to know about connecting to these MCP servers

Ready to Get Started?

Join developers who are already using KlavisAI to power their Google Gemini applications with these MCP servers.

Start For Free