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Connectto Google Sheets, Postgres, Google Docs MCP Servers

Create powerful AI workflows by connecting multiple MCP servers including Google Sheets, Postgres, Google Docs for enhanced automation capabilities in Klavis AI.

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Google Sheets icon

Google Sheets

featured

Google Sheets is a web-based spreadsheet application that allows users to create, edit, and collaborate on spreadsheets online

Available Tools:

  • google_sheets_create_spreadsheet
  • google_sheets_get_spreadsheet
  • google_sheets_write_to_cell
  • +1 more tools
Postgres icon

Postgres

featured

PostgreSQL is a powerful, open source object-relational database system

Available Tools:

  • query
Google Docs icon

Google Docs

featured

Google Docs is a word processor included as part of the free, web-based Google Docs Editors suite

Available Tools:

  • google_docs_get_document_by_id
  • google_docs_get_all_documents
  • google_docs_insert_text_at_end
  • +2 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"

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

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

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

# Get tools from all MCP servers
google_sheets_tools = klavis_client.mcp_server.list_tools(
    server_url=google_sheets_mcp_instance.server_url,
    connection_type=ConnectionType.STREAMABLE_HTTP,
    format=ToolFormat.GEMINI,
)
postgres_tools = klavis_client.mcp_server.list_tools(
    server_url=postgres_mcp_instance.server_url,
    connection_type=ConnectionType.STREAMABLE_HTTP,
    format=ToolFormat.GEMINI,
)
google_docs_tools = klavis_client.mcp_server.list_tools(
    server_url=google_docs_mcp_instance.server_url,
    connection_type=ConnectionType.STREAMABLE_HTTP,
    format=ToolFormat.GEMINI,
)

# Combine all tools
all_tools = []
all_tools.extend(google_sheets_tools.tools)
all_tools.extend(postgres_tools.tools)
all_tools.extend(google_docs_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

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