How do I use multiple MCP servers (Google Sheets, Gong, Firecrawl Deep Research) with LlamaIndex?

LlamaIndex supports connecting to multiple MCP servers simultaneously. You can configure each Google Sheets, Gong, Firecrawl Deep Research server independently and use their tools together in your LlamaIndex applications. This enables powerful multi-source RAG systems where your agents can query data from Google Sheets, Gong, Firecrawl Deep Research and combine insights for comprehensive responses. Klavis AI simplifies multi-server management with centralized configuration and unified connection handling for LlamaIndex applications.

What are the benefits of using Google Sheets and Gong and Firecrawl Deep Research together in LlamaIndex?

Combining Google Sheets and Gong and Firecrawl Deep Research in your LlamaIndex applications creates powerful synergies for advanced RAG systems. Each service brings unique data sources and capabilities that complement each other, enabling comprehensive knowledge retrieval, cross-platform data correlation, and enhanced AI responses with multi-source validation.

Do I need special LlamaIndex packages for MCP server integration?

Yes, you'll need the LlamaIndex MCP tools package and any service-specific packages for the MCP servers you want to connect to. Klavis AI provides detailed package requirements and automated installation scripts for each supported MCP server, along with LlamaIndex-specific configuration examples and troubleshooting guides to ensure smooth setup.

Can I use MCP servers with LlamaIndex agents and tools?

Absolutely! MCP servers are designed to work seamlessly with LlamaIndex's agent framework and tool calling capabilities. You can incorporate MCP server tools into your LlamaIndex agents, allowing them to perform actions and retrieve data from external services as part of their reasoning and response generation process.

How do I handle authentication for MCP servers in LlamaIndex?

Authentication for MCP servers in LlamaIndex is handled through environment variables and configuration files. Each MCP server may require different authentication methods (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 index data from these MCP servers in LlamaIndex?

Yes, you can use these MCP servers as data sources for LlamaIndex indexing. This allows you to create comprehensive knowledge bases that include data from external services, enabling your RAG applications to provide answers based on both your local documents and real-time external data. Klavis AI provides optimized indexing strategies and data processing pipelines specifically designed for MCP server integration with LlamaIndex.

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

LlamaIndex applications that benefit most include: enterprise RAG systems needing real-time data access, customer support chatbots requiring integration with multiple platforms, research and analysis tools that need to pull data from various sources, and AI agents that perform complex workflows across multiple services. MCP servers extend LlamaIndex beyond static document processing to dynamic, real-time AI applications.

How does MCP server integration affect LlamaIndex performance?

MCP server integration adds network latency for external API calls, but the impact is typically minimal when properly configured. LlamaIndex's asynchronous capabilities help manage multiple MCP server calls efficiently. Best practices include caching frequently accessed data, using appropriate timeouts, and implementing fallback strategies for when external services are unavailable. Klavis AI provides performance monitoring tools and optimization recommendations to ensure your LlamaIndex + MCP server applications run at peak efficiency.

Why choose Klavis AI for LlamaIndex MCP server connections?

Klavis AI provides the most comprehensive platform for LlamaIndex MCP server integration with features like pre-built LlamaIndex connectors, automated setup scripts, detailed documentation with LlamaIndex-specific examples, secure credential management, and performance optimization guides. Our platform is specifically designed to make LlamaIndex + MCP server integration as seamless as possible for developers building advanced RAG and agent 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 Google Sheets, Gong, Firecrawl Deep Research MCP Servers

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

Get Started Free View All MCP Servers

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

Gong

featured

Gong is a revenue intelligence platform that captures and analyzes all revenue-related interactions to help sales teams close more deals. It provides conversation analytics, deal insights, and sales performance tracking through call recordings and transcripts

Available Tools:

gong_get_transcripts_by_user
gong_get_extensive_data
gong_get_call_transcripts
+2 more tools

Firecrawl Deep Research

featured

A personal research assistant that analyze sources across the web, based on Firecrawl

Available Tools:

firecrawl_deep_research

Connect Using Klavis UI

The easiest way to connect these MCP servers to your AI clients

View Step by Step Guide

Navigate to Klavis Home

Visit the Klavis home page and you will see a list of MCP servers available in Klavis.

Authorize Your Servers

Click the "Authorize" button next to your chosen servers. Once servers are authorized, you will see a Green Checkmark status.

Add to Your AI Client

Click "Add to Cursor", "Add to VS Code", "Add to Claude" or "Add to Other Clients" button to connect the MCP server to your preferred AI client.

Connect Using API

Programmatically connect your AI agents to these MCP servers

Get Your API Key

Configure Connections

Use the code examples below to add your desired MCP servers to your AI client and configure authentication settings.

Test & Deploy

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

Integrate in minutes, Scale to millions

View Documentation

import os
from klavis import Klavis
from klavis.types import McpServerName
from llama_index.tools.mcp import (
    BasicMCPClient,
    aget_tools_from_mcp_url,
)
from llama_index.core.agent.workflow import FunctionAgent

# Initialize clients
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))

# Create strata server with all MCP servers
response = klavis_client.mcp_server.create_strata_server(
    servers=[McpServerName.GOOGLE_SHEETS, McpServerName.GONG, McpServerName.FIRECRAWL_DEEP_RESEARCH],
    user_id="1234"
)

mcp_server_url = response.strata_server_url

# Get tools from the strata server
strata_tools = await aget_tools_from_mcp_url(
    mcp_server_url, 
    client=BasicMCPClient(mcp_server_url)
)

# Create agent with all tools
agent = FunctionAgent(
    name="strata_agent",
    tools=strata_tools,
    llm=llm,
)

Code Examples for claude

import os
from anthropic import Anthropic
from klavis import Klavis
from klavis.types import McpServerName, 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"

# Create strata server with all MCP servers
response = klavis_client.mcp_server.create_strata_server(
    servers=[McpServerName.GOOGLE_SHEETS, McpServerName.GONG, McpServerName.FIRECRAWL_DEEP_RESEARCH],
    user_id="1234"
)

mcp_server_url = response.strata_server_url

# Get tools from the strata server
mcp_tools = klavis_client.mcp_server.list_tools(
    server_url=mcp_server_url,
    format=ToolFormat.ANTHROPIC,
)

messages = [
    {"role": "user", "content": user_message}
]

response = anthropic_client.messages.create(
    model=CLAUDE_MODEL,
    max_tokens=4000,
    messages=messages,
    tools=mcp_tools.tools
)

import Anthropic from '@anthropic-ai/sdk';
import { KlavisClient, Klavis } from 'klavis';

// Initialize clients
const anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });
const klavisClient = new KlavisClient({ apiKey: process.env.KLAVIS_API_KEY });

const CLAUDE_MODEL = "claude-3-5-sonnet-20241022";
const userMessage = "Your message here";

// Create strata server with all MCP servers
const strataResponse = await klavisClient.mcpServer.createStrataServer({
    servers: [Klavis.McpServerName.GoogleSheets, Klavis.McpServerName.Gong, Klavis.McpServerName.FirecrawlDeepResearch],
    userId: "1234"
});

const mcpServerUrl = strataResponse.strataServerUrl;

// Get tools from the strata server
const mcpTools = await klavisClient.mcpServer.listTools({
    serverUrl: mcpServerUrl,
    format: Klavis.ToolFormat.Anthropic,
});

const response = await anthropic.messages.create({
    model: CLAUDE_MODEL,
    max_tokens: 4000,
    messages: [{ role: 'user', content: userMessage }],
    tools: mcpTools.tools,
});

Code Examples for openai

import os
from openai import OpenAI
from klavis import Klavis
from klavis.types import McpServerName, ToolFormat

# Initialize clients
openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))

# Constants
OPENAI_MODEL = "gpt-4o-mini"
user_message = "Your query here"

# Create strata server with all MCP servers
response = klavis_client.mcp_server.create_strata_server(
    servers=[McpServerName.GOOGLE_SHEETS, McpServerName.GONG, McpServerName.FIRECRAWL_DEEP_RESEARCH],
    user_id="1234"
)

mcp_server_url = response.strata_server_url

# Get tools from the strata server
mcp_tools = klavis_client.mcp_server.list_tools(
    server_url=mcp_server_url,
    format=ToolFormat.OPENAI,
)

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": user_message}
]

response = openai_client.chat.completions.create(
    model=OPENAI_MODEL,
    messages=messages,
    tools=mcp_tools
)

import OpenAI from 'openai';
import { KlavisClient, Klavis } from 'klavis';

// Initialize clients
const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
const klavisClient = new KlavisClient({ apiKey: process.env.KLAVIS_API_KEY });

const OPENAI_MODEL = "gpt-4o-mini";
const userMessage = "Your query here";

// Create strata server with all MCP servers
const strataResponse = await klavisClient.mcpServer.createStrataServer({
    servers: [Klavis.McpServerName.GoogleSheets, Klavis.McpServerName.Gong, Klavis.McpServerName.FirecrawlDeepResearch],
    userId: "1234"
});

const mcpServerUrl = strataResponse.strataServerUrl;

// Get tools from the strata server
const mcpTools = await klavisClient.mcpServer.listTools({
    serverUrl: mcpServerUrl,
    format: Klavis.ToolFormat.Openai,
});

const response = await openai.chat.completions.create({
    model: OPENAI_MODEL,
    messages: [
        { role: 'system', content: 'You are a helpful assistant.' },
        { role: 'user', content: userMessage }
    ],
    tools: mcpTools,
});

Code Examples for gemini

import os
from google import genai
from klavis import Klavis
from klavis.types import McpServerName, 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"

# Create strata server with all MCP servers
response = klavis_client.mcp_server.create_strata_server(
    servers=[McpServerName.GOOGLE_SHEETS, McpServerName.GONG, McpServerName.FIRECRAWL_DEEP_RESEARCH],
    user_id="1234"
)

mcp_server_url = response.strata_server_url

# Get tools from the strata server
mcp_tools = klavis_client.mcp_server.list_tools(
    server_url=mcp_server_url,
    format=ToolFormat.GEMINI,
)

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

import { GoogleGenAI } from '@google/genai';
import { KlavisClient, Klavis } from 'klavis';

// Initialize clients
const ai = new GoogleGenAI({ apiKey: process.env.GOOGLE_API_KEY });
const klavisClient = new KlavisClient({ apiKey: process.env.KLAVIS_API_KEY });

const userMessage = "Your query here";

// Create strata server with all MCP servers
const strataResponse = await klavisClient.mcpServer.createStrataServer({
    servers: [Klavis.McpServerName.GoogleSheets, Klavis.McpServerName.Gong, Klavis.McpServerName.FirecrawlDeepResearch],
    userId: "1234"
});

const mcpServerUrl = strataResponse.strataServerUrl;

// Get tools from the strata server
const mcpTools = await klavisClient.mcpServer.listTools({
    serverUrl: mcpServerUrl,
    format: Klavis.ToolFormat.Gemini,
});

const response = await ai.models.generateContent({
    model: "gemini-2.5-flash",
    contents: userMessage,
    tools: mcpTools.tools,
});

Code Examples for langchain

import os
import asyncio
from klavis import Klavis
from klavis.types import McpServerName
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

# Initialize clients
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))
llm = ChatOpenAI(model="gpt-4o-mini", api_key=os.getenv("OPENAI_API_KEY"))

# Create strata server with all MCP servers
response = klavis_client.mcp_server.create_strata_server(
    servers=[McpServerName.GOOGLE_SHEETS, McpServerName.GONG, McpServerName.FIRECRAWL_DEEP_RESEARCH],
    user_id="1234"
)

mcp_server_url = response.strata_server_url

mcp_client = MultiServerMCPClient({
    "strata": {
        "transport": "streamable_http",
        "url": mcp_server_url
    }
})

tools = asyncio.run(mcp_client.get_tools())

agent = create_react_agent(
    model=llm,
    tools=tools,
)

response = asyncio.run(agent.ainvoke({
    "messages": [{"role": "user", "content": "Your query here"}]
}))

import { KlavisClient, Klavis } from 'klavis';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { ChatOpenAI } from "@langchain/openai";
import { createReactAgent } from "@langchain/langgraph/prebuilt";

// Initialize clients
const klavisClient = new KlavisClient({ apiKey: process.env.KLAVIS_API_KEY });
const llm = new ChatOpenAI({ model: "gpt-4o-mini", apiKey: process.env.OPENAI_API_KEY });

// Create strata server with all MCP servers
const strataResponse = await klavisClient.mcpServer.createStrataServer({
    servers: [Klavis.McpServerName.GoogleSheets, Klavis.McpServerName.Gong, Klavis.McpServerName.FirecrawlDeepResearch],
    userId: "1234"
});

const mcpServerUrl = strataResponse.strataServerUrl;

const mcpClient = new MultiServerMCPClient({
    "strata": {
        transport: "streamable_http",
        url: mcpServerUrl
    }
});

const tools = await mcpClient.getTools();

const agent = createReactAgent({
    llm: llm,
    tools: tools,
});

const response = await agent.invoke({
    messages: [{ role: "user", content: "Your query here" }]
});

Code Examples for crewai

import os
from crewai import Agent, Task, Crew, Process
from crewai_tools import MCPServerAdapter
from klavis import Klavis
from klavis.types import McpServerName

# Initialize clients
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))

# Create strata server with all MCP servers
response = klavis_client.mcp_server.create_strata_server(
    servers=[McpServerName.GOOGLE_SHEETS, McpServerName.GONG, McpServerName.FIRECRAWL_DEEP_RESEARCH],
    user_id="1234"
)

mcp_server_url = response.strata_server_url

# Initialize MCP tools from strata server
with MCPServerAdapter(mcp_server_url) as mcp_tools:
    # Create agent with access to all MCP server tools
    strata_agent = Agent(
        role="Multi-Service Specialist",
        goal="Handle tasks across multiple services and data sources",
        backstory="You are an expert at coordinating and analyzing data from multiple services",
        tools=mcp_tools,
        reasoning=True,
        verbose=False
    )
    
    # Define Task
    research_task = Task(
        description="Gather and analyze comprehensive data from all available sources",
        expected_output="Complete analysis with structured summary and key insights",
        agent=strata_agent,
        markdown=True
    )
    
    # Create and execute the crew
    crew = Crew(
        agents=[strata_agent],
        tasks=[research_task],
        verbose=False,
        process=Process.sequential
    )
    
    result = crew.kickoff()

// CrewAI currently only supports Python. Please use the Python example.

Code Examples for fireworks-ai

import os
from fireworks.client import Fireworks
from klavis import Klavis
from klavis.types import McpServerName, ToolFormat

# Initialize clients
fireworks_client = Fireworks(api_key=os.getenv("FIREWORKS_API_KEY"))
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))

user_message = "Your query here"

# Create strata server with all MCP servers
response = klavis_client.mcp_server.create_strata_server(
    servers=[McpServerName.GOOGLE_SHEETS, McpServerName.GONG, McpServerName.FIRECRAWL_DEEP_RESEARCH],
    user_id="1234"
)

mcp_server_url = response.strata_server_url

# Get tools from the strata server
mcp_tools = klavis_client.mcp_server.list_tools(
    server_url=mcp_server_url,
    format=ToolFormat.OPENAI,
)

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": user_message}
]

response = fireworks_client.chat.completions.create(
    model="accounts/fireworks/models/llama-v3p1-70b-instruct",
    messages=messages,
    tools=mcp_tools.tools
)

import Fireworks from 'fireworks-ai';
import { KlavisClient, Klavis } from 'klavis';

// Initialize clients
const fireworks = new Fireworks({ apiKey: process.env.FIREWORKS_API_KEY });
const klavisClient = new KlavisClient({ apiKey: process.env.KLAVIS_API_KEY });

const userMessage = "Your query here";

// Create strata server with all MCP servers
const strataResponse = await klavisClient.mcpServer.createStrataServer({
    servers: [Klavis.McpServerName.GoogleSheets, Klavis.McpServerName.Gong, Klavis.McpServerName.FirecrawlDeepResearch],
    userId: "1234"
});

const mcpServerUrl = strataResponse.strataServerUrl;

// Get tools from the strata server
const mcpTools = await klavisClient.mcpServer.listTools({
    serverUrl: mcpServerUrl,
    format: Klavis.ToolFormat.Openai,
});

const response = await fireworks.chat.completions.create({
    model: "accounts/fireworks/models/llama-v3p1-70b-instruct",
    messages: [
        { role: "system", content: "You are a helpful assistant." },
        { role: "user", content: userMessage }
    ],
    tools: mcpTools.tools,
});

Code Examples for together-ai

import os
from together import Together
from klavis import Klavis
from klavis.types import McpServerName, ToolFormat

# Initialize clients
together_client = Together(api_key=os.getenv("TOGETHER_API_KEY"))
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))

user_message = "Your query here"

# Create strata server with all MCP servers
response = klavis_client.mcp_server.create_strata_server(
    servers=[McpServerName.GOOGLE_SHEETS, McpServerName.GONG, McpServerName.FIRECRAWL_DEEP_RESEARCH],
    user_id="1234"
)

mcp_server_url = response.strata_server_url

# Get tools from the strata server
mcp_tools = klavis_client.mcp_server.list_tools(
    server_url=mcp_server_url,
    format=ToolFormat.OPENAI,
)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant with access to various tools."},
    {"role": "user", "content": user_message}
]

response = together_client.chat.completions.create(
    model="meta-llama/Llama-2-70b-chat-hf",
    messages=messages,
    tools=mcp_tools.tools
)

import Together from 'together-ai';
import { KlavisClient, Klavis } from 'klavis';

// Initialize clients
const togetherClient = new Together({ apiKey: process.env.TOGETHER_API_KEY });
const klavisClient = new KlavisClient({ apiKey: process.env.KLAVIS_API_KEY });

const userMessage = "Your query here";

// Create strata server with all MCP servers
const strataResponse = await klavisClient.mcpServer.createStrataServer({
    servers: [Klavis.McpServerName.GoogleSheets, Klavis.McpServerName.Gong, Klavis.McpServerName.FirecrawlDeepResearch],
    userId: "1234"
});

const mcpServerUrl = strataResponse.strataServerUrl;

// Get tools from the strata server
const mcpTools = await klavisClient.mcpServer.listTools({
    serverUrl: mcpServerUrl,
    format: Klavis.ToolFormat.Openai,
});

const response = await togetherClient.chat.completions.create({
    model: "meta-llama/Llama-2-70b-chat-hf",
    messages: [
        { role: "system", content: "You are a helpful AI assistant with access to various tools." },
        { role: "user", content: userMessage }
    ],
    tools: mcpTools.tools,
});

Frequently Asked Questions

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ConnectLlamaIndexto Google Sheets, Gong, Firecrawl Deep Research MCP Servers

Google Sheets

Available Tools:

Gong

Available Tools:

Firecrawl Deep Research

Available Tools:

Connect Using Klavis UI

Navigate to Klavis Home

Authorize Your Servers

Add to Your AI Client

Connect Using API

Get Your API Key

Configure Connections

Test & Deploy

Integrate in minutes, Scale to millions

Frequently Asked Questions

Ready to Get Started?

Connectto Google Sheets, Gong, Firecrawl Deep Research MCP Servers