How do I use multiple MCP servers (Google Sheets, Salesforce, Gong) with Together AI?

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

What are the benefits of using Google Sheets and Salesforce and Gong together with Together AI?

Combining Google Sheets and Salesforce and Gong with your Together AI applications creates powerful synergies for advanced AI systems. Each service brings unique data sources and capabilities that complement each other, enabling comprehensive information retrieval, cross-platform data correlation, and enhanced AI responses with multi-source validation using Together AI's high-performance inference.

Do I need special packages for Together AI MCP server integration?

Yes, you'll need the Together AI SDK 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 Together AI-specific configuration examples and troubleshooting guides to ensure smooth setup.

Can I use function calling with Together AI and MCP servers?

Absolutely! MCP servers are designed to work seamlessly with Together AI's function calling capabilities. You can use MCP server tools with any Together AI model that supports function calling, allowing your AI models 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 with Together AI?

Authentication for MCP servers with Together AI 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.

Which Together AI models are best for these MCP servers?

For these MCP servers, we recommend using Together AI models with strong function calling capabilities such as Llama 2 70B, Mixtral 8x7B, or Code Llama for coding tasks. These models provide excellent performance for tool use and can effectively leverage the capabilities of external MCP servers. Klavis AI provides model-specific optimization guides and performance benchmarks to help you choose the best Together AI model for your use case.

What types of AI applications benefit most from Together AI + MCP server integration?

AI applications that benefit most include: chatbots and virtual assistants needing real-time data access, automated workflow systems that interact with multiple platforms, content generation tools that need to pull data from various sources, and AI agents that perform complex multi-step tasks. Together AI's high-performance inference combined with MCP servers extends your applications beyond static responses to dynamic, real-time AI interactions.

How does MCP server integration affect Together AI performance?

MCP server integration adds minimal latency when properly configured, thanks to Together AI's optimized inference and efficient function calling. The impact is typically negligible compared to the enhanced capabilities gained. 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 Together AI + MCP server applications run at peak efficiency.

Can I use streaming responses with Together AI and MCP servers?

Yes, Together AI's streaming capabilities work well with MCP server integrations. You can stream AI responses while making function calls to MCP servers, providing a smooth user experience. The streaming continues after function calls are completed, allowing for real-time updates and interactive AI experiences. Klavis AI provides examples and best practices for implementing streaming with MCP server function calls.

Why choose Klavis AI for Together AI MCP server connections?

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

Connectto Google Sheets, Salesforce, Gong MCP Servers

Create powerful AI workflows by connecting multiple MCP servers including Google Sheets, Salesforce, Gong 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

Salesforce

featured

Salesforce is the world's leading customer relationship management (CRM) platform that helps businesses connect with customers, partners, and potential customers

Available Tools:

salesforce_get_accounts
salesforce_create_account
salesforce_update_account
+24 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

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 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.SALESFORCE, McpServerName.GONG],
    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
)

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.SALESFORCE, McpServerName.GONG],
    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.Salesforce, Klavis.McpServerName.Gong],
    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.SALESFORCE, McpServerName.GONG],
    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.Salesforce, Klavis.McpServerName.Gong],
    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.SALESFORCE, McpServerName.GONG],
    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.Salesforce, Klavis.McpServerName.Gong],
    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.SALESFORCE, McpServerName.GONG],
    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.Salesforce, Klavis.McpServerName.Gong],
    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 llamaindex

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.SALESFORCE, McpServerName.GONG],
    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,
)

import { KlavisClient, Klavis } from 'klavis';
import { mcp } from "@llamaindex/tools";
import { agent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/llm";

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

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

const mcpServerUrl = strataResponse.strataServerUrl;

// Create MCP server connection
const strataServer = mcp({
    url: mcpServerUrl,
    verbose: true,
});

// Get tools from strata server
const strataTools = await strataServer.tools();

// Create agent with all tools
const strataAgent = agent({
    name: "strata_agent",
    llm: openai({ model: "gpt-4o" }),
    tools: strataTools,
});

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.SALESFORCE, McpServerName.GONG],
    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.SALESFORCE, McpServerName.GONG],
    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.Salesforce, Klavis.McpServerName.Gong],
    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,
});

Frequently Asked Questions

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ConnectTogether AIto Google Sheets, Salesforce, Gong MCP Servers

Google Sheets

Available Tools:

Salesforce

Available Tools:

Gong

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, Salesforce, Gong MCP Servers