How to Use the Deterministic Fair-Share Tip Splitter MCP in Google ADK

Q: How do I install the Deterministic Fair-Share Tip Splitter in Google ADK?

Run pip install google-adk. Initialize an McpToolset with the server URL using StreamableHttpServerParameters, then append it to your agent's tool list.

Equip your Google ADK agents with deterministic math to calculate flawless restaurant bill splits.

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Connect Deterministic Fair-Share Tip Splitter MCP to Google ADK

Create your Vinkius account to connect Deterministic Fair-Share Tip Splitter 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 Deterministic Fair-Share Tip Splitter with Google ADK

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Google ADK integration for this MCP Server

The `split_bill` tool gives Gemini agents the ability to calculate proportional tax and tip distributions without hallucinating the numbers. You feed it a stringified JSON array of ordered items, the total tax, and the tip percentage. The tool outputs an exact, down-to-the-penny ledger for every diner. Because Gemini models handle massive context windows, you can dump months of receipt data into the prompt. The agent can pick out a specific dinner from a massive BigQuery dataset. It then extracts the items and runs the exact split calculations instantly.

Solving the fractional penny

The `split_bill` tool handles the classic rounding errors that plague financial apps. When a five-dollar tax splits three ways, the tool deterministically assigns the remaining two cents to the largest item totals. Nobody gets shortchanged. The sum always matches the restaurant's final charge. Enterprise agents running on Google Cloud need this kind of predictability. You cannot have a customer-facing Vertex AI bot guessing at math. This MCP Server forces the agent to rely on hardcoded logic instead of probabilistic text generation.

Cloud-native execution

Exposing the `split_bill` MCP tool to your agent requires just a few lines of Python. You initialize an `McpToolset` with the HTTP server parameters. Then you just pass it to your `LlmAgent`. You can use the `tool_names` filter to explicitly restrict which tools the agent can see. This keeps your enterprise deployment secure. It ensures the agent only executes the specific financial calculations you authorized.

Setup guide

Set up Deterministic Fair-Share Tip Splitter 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 Deterministic Fair-Share Tip Splitter 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="Deterministic Fair-Share Tip Splitter_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Deterministic Fair-Share Tip Splitter tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Deterministic Fair-Share Tip Splitter MCP in Google ADK

Run `pip install google-adk`. Initialize an `McpToolset` with the server URL using `StreamableHttpServerParameters`, then append it to your agent's tool list.

No, the tool itself just does math. However, your Gemini agent can pull receipt records from BigQuery, format them into the required JSON array, and pass them to the tool for calculation.

Even with massive token contexts, LLMs struggle with fractional penny distribution. The tool guarantees the final shares sum exactly to the receipt total without arbitrary rounding errors.

Yes, the framework supports both. For Vinkius-hosted endpoints, you will want to use the HTTP transport with your single endpoint token.

The server runs your JSON arrays and tax integers inside a zero-trust sandbox. Once the calculation finishes, the instance dies. No logs of your dinner orders or tip amounts ever hit a disk.

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