How to Use the Monetary Correction Engine MCP in Google ADK

Run local financial index corrections using this MCP Server within the Google ADK ecosystem.

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Connect Monetary Correction Engine MCP to Google ADK

Create your Vinkius account to connect Monetary Correction Engine 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 Monetary Correction Engine with Google ADK

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Connect Google ADK agents to local interest tools

Enterprise agents running on Google Cloud can now run local financial adjustments. By wrapping this MCP server in McpToolset, your Gemini models gain access to the `calculate_monetary_correction` tool. This allows long-context reasoning models to process massive financial documents while performing exact math. Let's look at the math. The agent reads historical indices from your local configuration files to calculate simple or compound interest. It boils down to executing precise calculations right next to your BigQuery data.

Filter specific financial tools for safety

You can restrict what tools your Gemini agent can run using the tool_names filter. This ensures your agent only invokes the `calculate_monetary_correction` tool when processing specific financial pipelines. Look, the thing is, enterprise agents don't need access to every utility. That said, keeping the toolset constrained to this specific calculation engine prevents unwanted agent actions.

Process historical corrections with 1M+ token context

Gemini models excel at holding vast amounts of historical data in memory. Your agent can read thousands of pages of financial reports, then call the `calculate_monetary_correction` tool to verify the reported interest. This calculation follows strict local execution logic, preventing any data leakage. Your cloud agent gets the speed of Gemini paired with the mathematical precision of our local engine.

Setup guide

Set up Monetary Correction Engine 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 Monetary Correction Engine 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="Monetary Correction Engine_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Monetary Correction Engine tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Monetary Correction Engine MCP in Google ADK

Instantiate McpToolset using your StreamableHttpServerParameters. Then, pass this toolset to your LlmAgent constructor to expose the `calculate_monetary_correction` tool.

Yes, you can. Your agent can retrieve historical values from BigQuery and then pass those numbers directly to the `calculate_monetary_correction` tool.

Yes, this MCP server supports both transport protocols. You can configure StreamableHttpServerParameters for remote execution or use Stdio for local processes.

This server relies on pre-loaded local JSON index files. You will need to update these local files manually when new monthly figures are released, as the `calculate_monetary_correction` tool does not make external API calls.

Your principal amounts and calculation parameters never leave your Google Cloud project boundary. The MCP server processes the math entirely within your secure VPC. No external calls are made during the correction process.

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