How to Use the Together AI MCP in Google ADK

Q: What if I need to generate images with Together AI and my agents using Google ADK?

You can use the generateimage tool directly. Just provide a model ID and a descriptive text prompt, andTogether AI returns an image that your agent's workflow can incorporate.

Q: Can Together AI list all available models when building agents with Google ADK?

Yes. The listavailablemodels tool allows the agent to fetch a complete directory of every model running on Together AI before deployment, ensuring you pick the right one.

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Connect Together AI MCP to Google ADK

Create your Vinkius account to connect Together AI to Google ADK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Together AI with Google ADK

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Process Conversational Data

Your agent calls the `chat_completion` tool for natural conversation. You send it a model ID and an array of messages, letting Together AI manage the chat state. This mechanism lets your enterprise agents maintain long-term context across complex user interactions.

Search Private Data Stores

To connect to company data, your agent uses `generate_embeddings`. You provide a model ID and text inputs. Together AI returns vector embeddings that you can query in BigQuery. This function allows the Gemini models within Google ADK to reason over private, proprietary knowledge.

Manage Model Customization

When standard models fall short, your team needs fine-tuning. The `create_finetune_job` tool lets your agent start a new job using a base model ID and a training file ID. This keeps your agents highly specialized for the specific data residing in Google Cloud.

Setup guide

Set up Together AI MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Together AI tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Together AI_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Together AI tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Together AI MCP in Google ADK

Google ADK supports Gemini models that can hold over 1M tokens of context. The MCP Server allows the agent to access various tools, making sure its memory capacity is always maximized for complex tasks.

You can use the `generate_image` tool directly. Just provide a model ID and a descriptive text prompt, andTogether AI returns an image that your agent's workflow can incorporate.

Yes. The `list_available_models` tool allows the agent to fetch a complete directory of every model running on Together AI before deployment, ensuring you pick the right one.

This server handles text prompts, model IDs, training file IDs, and descriptive image prompts. It is designed to act on enterprise data structured within the Google Cloud ecosystem.

Monitoring happens through your existing BigQuery setup, as the MCP tools are designed for native integration with Vertex AI and Google Cloud infrastructure. You track job execution metrics there.

Use it with your favorite AI tools

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