How to Use the Land Rent Viability Calculator MCP in Google ADK

Q: How do I restrict tools in the Land Rent Viability Calculator using the Google ADK?

Use the toolnames filter when setting up your McpToolset. This allows you to expose only specific tools to your enterprise agent.

Run enterprise agricultural lease calculations inside BigQuery using the Google ADK.

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Connect Land Rent Viability Calculator MCP to Google ADK

Create your Vinkius account to connect Land Rent Viability Calculator to Google ADK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Land Rent Viability Calculator with Google ADK

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Enterprise Cost Modeling with Google ADK

This MCP exposes the `calculate_costs` tool to calculate total farming production costs directly from your enterprise data. Your agent pulls land expense metrics and outputs a structured cost profile. This works natively with your Google Cloud datasets. You can query thousands of land records and let the agent calculate the overhead for each tract.

Long-Context Profitability Analysis

The `evaluate_profitability` tool checks the financial health of agricultural leases by comparing regional yield history against lease terms. Your agent processes these complex datasets in a single run. Because your agent handles massive context, you can feed entire historical harvest ledgers into the run. The agent then runs the math to verify if the lease rate is sustainable.

Predictive Threshold Identification

Use the `identify_thresholds` tool to determine the exact crop prices and soil productivity levels needed to keep a lease viable. Your agent identifies these critical limits to protect your investments. You can restrict which tools are active by using the tool_names filter. This ensures your agent only runs safety-approved calculations on your cloud infrastructure.

Setup guide

Set up Land Rent Viability Calculator 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 Land Rent Viability Calculator 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="Land Rent Viability Calculator_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Land Rent Viability Calculator tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Land Rent Viability Calculator MCP in Google ADK

Install the package with pip install google-adk. Then, initialize McpToolset with your Vinkius HTTP URL and pass it to your LlmAgent instance.

Yes, your agent can pull historical yield data directly from BigQuery. This MCP then processes those metrics to calculate the viability of the lease.

Use the tool_names filter when setting up your McpToolset. This allows you to expose only specific tools to your enterprise agent.

This MCP supports both Stdio and Streamable HTTP transports. Choose the HTTP transport for cloud-run agent deployments.

All calculations run over encrypted HTTPS connections. Vinkius secures the gateway, and your historical harvest ledgers are never cached or used for training.

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