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Connectto Attio, Linear, Firecrawl Web Search MCP Servers

Create powerful AI workflows by connecting multiple MCP servers including Attio, Linear, Firecrawl Web Search for enhanced automation capabilities in Klavis AI.

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Attio icon

Attio

featured

Attio is a next-generation CRM platform that helps teams build stronger relationships with their customers through powerful data management and automation

Available Tools:

  • attio_search_people
  • attio_search_companies
  • attio_search_deals
  • +2 more tools
Linear icon

Linear

featured

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 Web Search icon

Firecrawl Web Search

featured

Advanced web crawling, scraping, and search capabilities powered by Firecrawl

Available Tools:

  • firecrawl_scrape
  • firecrawl_map
  • firecrawl_crawl
  • +6 more tools

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

View Documentation
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"

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

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

# Get tools from all MCP servers
attio_tools = klavis_client.mcp_server.list_tools(
    server_url=attio_mcp_instance.server_url,
    connection_type=ConnectionType.STREAMABLE_HTTP,
    format=ToolFormat.GEMINI,
)
linear_tools = klavis_client.mcp_server.list_tools(
    server_url=linear_mcp_instance.server_url,
    connection_type=ConnectionType.STREAMABLE_HTTP,
    format=ToolFormat.GEMINI,
)
firecrawl_web_search_tools = klavis_client.mcp_server.list_tools(
    server_url=firecrawl_web_search_mcp_instance.server_url,
    connection_type=ConnectionType.STREAMABLE_HTTP,
    format=ToolFormat.GEMINI,
)

# Combine all tools
all_tools = []
all_tools.extend(attio_tools.tools)
all_tools.extend(linear_tools.tools)
all_tools.extend(firecrawl_web_search_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

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