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Connectto Calendly, Firecrawl Deep Research, Resend MCP Servers

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

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Calendly icon

Calendly

coming soon

Manage scheduling and appointments with your agents.

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
Resend icon

Resend

featured

Resend is a modern email API for sending and receiving emails programmatically

Available Tools:

  • resend_send_email
  • resend_create_audience
  • resend_get_audience
  • +12 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"))

calendly_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.CALENDLY,
    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,
)

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

# Get all MCP tools
calendly_tools = klavis_client.mcp_server.list_tools(
    server_url=calendly_mcp_instance.server_url,
    connection_type=ConnectionType.STREAMABLE_HTTP,
    format=ToolFormat.OPENAI,
)
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.OPENAI,
)
resend_tools = klavis_client.mcp_server.list_tools(
    server_url=resend_mcp_instance.server_url,
    connection_type=ConnectionType.STREAMABLE_HTTP,
    format=ToolFormat.OPENAI,
)

# Combine all tools
all_tools = []
all_tools.extend(calendly_tools.tools)
all_tools.extend(firecrawl_deep_research_tools.tools)
all_tools.extend(resend_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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