How do I use multiple MCP servers (Vercel, Fireflies) with Google Gemini?

Google Gemini supports using multiple MCP servers simultaneously through function calling. You can configure each Vercel, Fireflies server independently and combine their tools in a single Gemini model instance. This enables powerful multi-service AI applications where Gemini can access data and perform actions across Vercel, Fireflies in a single conversation flow. Klavis AI simplifies multi-server management with centralized configuration and unified tool formatting for Gemini applications.

What are the benefits of using Vercel and Fireflies together with Gemini?

Combining Vercel and Fireflies in your Gemini applications creates powerful synergies for advanced AI capabilities. Each service brings unique data sources and actions that complement each other, enabling comprehensive information retrieval, cross-platform automation, and enhanced AI responses with multi-source validation and real-time data access.

Do I need special packages for Gemini MCP server integration?

Yes, you'll need the @google/generative-ai package for JavaScript/TypeScript or google-generativeai for Python, along with the Klavis SDK to manage MCP server connections. Klavis AI provides detailed package requirements and automated installation scripts for each supported MCP server, along with Gemini-specific configuration examples and troubleshooting guides to ensure smooth setup.

Can I use MCP servers with Gemini's function calling?

Absolutely! MCP servers are designed to work seamlessly with Gemini's function calling capabilities. You can register MCP server tools as Gemini functions, allowing the model to call these tools during conversations to perform actions and retrieve data from external services as part of its reasoning and response generation process.

How do I handle authentication for MCP servers with Gemini?

Authentication for MCP servers in Gemini applications is handled through environment variables and the Klavis SDK. You'll need your Google AI API key for Gemini and your Klavis API key for MCP server access. Each MCP server may require additional authentication (API keys, OAuth tokens, etc.). Klavis AI handles the authentication flow automatically once you provide the necessary credentials, and provides secure credential management, encrypted storage, and pre-configured authentication templates for all supported services.

Can I use these MCP servers data in Gemini conversations?

Yes, you can use these MCP servers as data sources in Gemini conversations. This allows Gemini to access real-time external data and perform actions across different platforms during conversations, enabling dynamic responses based on current information from your connected services. Klavis AI provides optimized data retrieval strategies and response formatting specifically designed for MCP server integration with Gemini.

What types of Gemini applications benefit most from MCP server integration?

Gemini applications that benefit most include: conversational AI assistants needing real-time data access, customer support chatbots requiring integration with multiple platforms, business automation tools that need to interact with various services, and AI agents that perform complex workflows across multiple systems. MCP servers extend Gemini beyond text generation to dynamic, action-capable AI applications.

How does MCP server integration affect Gemini performance?

MCP server integration adds network latency for external API calls, but Gemini's efficient function calling helps manage this impact. The actual performance depends on the MCP server response times and your application's caching strategy. Best practices include using appropriate timeouts, implementing caching for frequently accessed data, and designing graceful fallbacks for when external services are unavailable. Klavis AI provides performance monitoring tools and optimization recommendations to ensure your Gemini + MCP server applications run efficiently.

Why choose Klavis AI for Gemini MCP server connections?

Klavis AI provides the most comprehensive platform for Gemini MCP server integration with features like pre-built Gemini tool formatters, automated setup scripts, detailed documentation with Gemini-specific examples, secure credential management, and performance optimization guides. Our platform is specifically designed to make Gemini + MCP server integration as seamless as possible for developers building advanced conversational AI and automation applications. With Klavis AI's centralized management dashboard, you can monitor all your MCP server connections, track performance metrics, and troubleshoot issues from a single interface.

Connectto Vercel, Fireflies MCP Servers

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Vercel

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Vercel is a cloud platform for static sites and serverless functions that enables developers to host websites and web services. Deploy projects, manage domains, configure environment variables, and monitor deployments with ease

Fireflies

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Fireflies.ai is an AI meeting assistant that automatically records, transcribes, and summarizes your meetings across Zoom, Google Meet, Teams, and other platforms. Extract action items, key insights, and searchable notes from all your conversations.

Quick Setup Guide

Follow these steps to connect Google Gemini to these MCP servers

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Configure Connections

Add your desired MCP servers to Gemini and configure authentication settings.

Test & Deploy

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

Google Gemini + KlavisAI Integration Snippets

View Documentation

import os
from google import genai
from klavis import Klavis
from klavis.types import McpServerName, ConnectionType, ToolFormat

# Initialize clients
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))
client = genai.Client(api_key=os.getenv("GOOGLE_API_KEY"))

user_message = "Your query here"

vercel_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.VERCEL,
    user_id="1234",
    connection_type=ConnectionType.STREAMABLE_HTTP,
)

fireflies_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.FIREFLIES,
    user_id="1234",
    connection_type=ConnectionType.STREAMABLE_HTTP,
)

# Get tools from all MCP servers
vercel_tools = klavis_client.mcp_server.list_tools(
    server_url=vercel_mcp_instance.server_url,
    connection_type=ConnectionType.STREAMABLE_HTTP,
    format=ToolFormat.GEMINI,
)
fireflies_tools = klavis_client.mcp_server.list_tools(
    server_url=fireflies_mcp_instance.server_url,
    connection_type=ConnectionType.STREAMABLE_HTTP,
    format=ToolFormat.GEMINI,
)

# Combine all tools
all_tools = []
all_tools.extend(vercel_tools.tools)
all_tools.extend(fireflies_tools.tools)

response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents=user_message,
    config=genai.types.GenerateContentConfig(
        tools=all_tools,
    ),
)

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