How to Use the Trolley Global Payouts & Tax Compliance MCP in Google ADK

Process global payouts and manage compliance with Google ADK using the MCP Server.

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Connect Trolley Global Payouts & Tax Compliance MCP to Google ADK

Create your Vinkius account to connect Trolley Global Payouts & Tax Compliance 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 Trolley Global Payouts & Tax Compliance with Google ADK

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Checking Global Balances on the MCP Server

Use your agent to get a real-time snapshot of funds by calling `list_balances`. This function pulls all account balances into the Gemini model's context for reasoning. It’s perfect when you need an immediate financial overview. It provides enough data that BigQuery can consume and analyze immediately, letting you see available capital before launching a payout.

Managing Payees with Google ADK

Need to verify who gets paid? Your agent handles this by running `list_recipients` or getting specific data on one person using `get_recipient`. This ensures the correct payee ID is used for every transaction. Since this runs on Google Cloud, you can immediately pipe these recipient records into BigQuery for historical analysis.

Handling Tax Forms with Google ADK

Compliance requires documentation. You can get tax forms associated with a person by calling `get_tax_forms`. The agent executes this tool, returning the necessary files or data points. This keeps your payout process compliant and gives you structured data that lives natively in the Google ecosystem.

Setup guide

Set up Trolley Global Payouts & Tax Compliance 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 Trolley Global Payouts & Tax Compliance 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="Trolley Global Payouts & Tax Compliance_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Trolley Global Payouts & Tax Compliance tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Trolley Global Payouts & Tax Compliance MCP in Google ADK

You pass the MCP toolset to your LlmAgent. The agent then calls tools like `list_invoices` or `get_tax_forms`, and Gemini processes the returned data using its long-context reasoning.

You can list payments, invoices, balances, and recipients. The agent uses these tools to gather all necessary transactional information for your BigQuery analysis.

Yes. You can retrieve the required documentation by calling `get_tax_forms` after identifying a recipient. This structured data is ready to be stored in BigQuery.

It handles payments, invoices, and balances. The toolset allows your agent to cycle through listing all payment history or getting current account totals.

The server handles financial transactions (payments, invoices), balances, recipient details, and associated tax forms. All these records are designed for deep analysis in BigQuery.

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