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Connectto Mixpanel, Markdown2doc, Tavily MCP Servers

Create powerful AI workflows by connecting multiple MCP servers including Mixpanel, Markdown2doc, Tavily for enhanced automation capabilities in Klavis AI.

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Mixpanel icon

Mixpanel

featured

Mixpanel is a powerful product analytics platform that helps teams understand user behavior, track events, analyze conversion funnels, measure retention, and make data-driven decisions with real-time insights and advanced segmentation capabilities

Available Tools:

  • mixpanel_send_events
  • mixpanel_get_events
  • mixpanel_get_event_properties
  • +6 more tools
Markdown2doc icon

Markdown2doc

featured

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

Available Tools:

  • convert_markdown_to_file
Tavily icon

Tavily

featured

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

Quick Setup Guide

Follow these steps to connect Fireworks AI 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 Fireworks AI and configure authentication settings.

3

Test & Deploy

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

Fireworks AI + KlavisAI Integration Snippets

View Documentation
import os
import json
from fireworks.client import Fireworks
from klavis import Klavis
from klavis.types import McpServerName, ConnectionType, ToolFormat

# Initialize clients
fireworks_client = Fireworks(api_key=os.getenv("FIREWORKS_API_KEY"))
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))

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

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,
)

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,
)

# Get all MCP tools
mixpanel_tools = klavis_client.mcp_server.list_tools(
    server_url=mixpanel_mcp_instance.server_url,
    connection_type=ConnectionType.STREAMABLE_HTTP,
    format=ToolFormat.OPENAI,
)
markdown2doc_tools = klavis_client.mcp_server.list_tools(
    server_url=markdown2doc_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,
)

# Combine all tools
all_tools = []
all_tools.extend(mixpanel_tools.tools)
all_tools.extend(markdown2doc_tools.tools)
all_tools.extend(tavily_tools.tools)

messages = [
    {"role": "system", "content": "You are a helpful assistant with access to multiple data sources."},
    {"role": "user", "content": user_message}
]

response = fireworks_client.chat.completions.create(
    model="accounts/fireworks/models/llama-v3p1-70b-instruct",
    messages=messages,
    tools=all_tools
)

Frequently Asked Questions

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