How to Use the Abacus AI (Enterprise AI Cloud) MCP in Google ADK

Connect your Google ADK agents to your Abacus AI machine learning models.

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Connect Abacus AI (Enterprise AI Cloud) MCP to Google ADK

Create your Vinkius account to connect Abacus AI (Enterprise AI Cloud) 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 Abacus AI (Enterprise AI Cloud) with Google ADK

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Control Abacus AI projects through Google ADK

Initialize your workspace by calling `create_project`. You can organize your model training efforts directly through your agent's interface. Use `list_projects` to pull your current directory of work. It helps the agent orient itself before starting new training tasks.

Train and deploy models with this MCP Server

Initiate your training runs by calling `train_model`. Your agent will manage the job lifecycle until the model is ready for traffic. Push models to production with `create_deployment`. Once live, you can monitor the health of these endpoints using `describe_model`.

Run predictions within Google ADK environments

Map your incoming data to existing models using `get_prediction`. It allows your agent to leverage your cloud-trained models for real-time decisions. Validate your input sources with `describe_dataset` before running any models. This ensures your agent is using the right data structures.

Setup guide

Set up Abacus AI (Enterprise AI Cloud) 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 Abacus AI (Enterprise AI Cloud) 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="Abacus AI (Enterprise AI Cloud)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Abacus AI (Enterprise AI Cloud) tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Abacus AI (Enterprise AI Cloud) MCP in Google ADK

Use the McpToolset class inside your Python environment. Provide the Vinkius HTTP URL to the server parameters to establish the connection.

It does. You can even filter specific tool names if you want to limit what your agent can touch in your ML cloud.

Yes. Since the Gemini models in the ADK handle massive token counts, you can pass large dataset descriptions back and forth without losing context.

It supports both Stdio and HTTP. You choose the one that fits your local or cloud agent deployment needs.

Every request is authenticated via your endpoint token. We never log your model weights or sensitive dataset metadata during these interactions.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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