Connectto Linear, Firecrawl Deep Research, Resend MCP Servers
Create powerful AI workflows by connecting multiple MCP servers including Linear, Firecrawl Deep Research, Resend with Claude's advanced reasoning capabilities in Klavis AI.
Linear
Linear is a modern issue tracking and project management tool designed for high-performance teams to build better software faster
Available Tools:
- linear_get_teams
- linear_get_issues
- linear_get_issue_by_id
- +9 more tools
Firecrawl Deep Research
A personal research assistant that analyze sources across the web, based on Firecrawl
Available Tools:
- firecrawl_deep_research
Resend
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 Claude to these MCP servers
Create Your Account
Sign up for KlavisAI to access our MCP server management platform and get your API keys.
Configure Connections
Add your desired MCP servers to Claude and configure authentication settings with your Anthropic API key.
Test & Deploy
Verify your connections work correctly with Claude's function calling and start using your enhanced AI capabilities.
Claude + KlavisAI Integration Snippets
import os
import json
from anthropic import Anthropic
from klavis import Klavis
from klavis.types import McpServerName, ConnectionType, ToolFormat
# Initialize clients
anthropic_client = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))
# Constants
CLAUDE_MODEL = "claude-3-5-sonnet-20241022"
user_message = "Your message here"
linear_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.LINEAR,
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 tools from all MCP servers
linear_tools = klavis_client.mcp_server.list_tools(
server_url=linear_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.ANTHROPIC,
)
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.ANTHROPIC,
)
resend_tools = klavis_client.mcp_server.list_tools(
server_url=resend_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.ANTHROPIC,
)
# Combine all tools
all_tools = []
all_tools.extend(linear_tools.tools)
all_tools.extend(firecrawl_deep_research_tools.tools)
all_tools.extend(resend_tools.tools)
messages = [
{"role": "user", "content": user_message}
]
response = anthropic_client.messages.create(
model=CLAUDE_MODEL,
max_tokens=4000,
messages=messages,
tools=all_tools
)Frequently Asked Questions
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