How to Use the Soil Correction Planner MCP in Google ADK

Q: Can I restrict which tools the Soil Correction Planner exposes to Google ADK?

Yes. You can apply a toolnames filter when configuring the toolset to limit the agent to specific calculations.

Connect Soil Correction Planner to Google ADK and turn BigQuery soil data into multi-year remediation plans.

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Connect Soil Correction Planner MCP to Google ADK

Create your Vinkius account to connect Soil Correction Planner 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 Soil Correction Planner with Google ADK

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Sequence Multi-Year Amendments

Soil Correction Planner dictates the exact timing for primary pH correction via `get_lime_sequence`. Your agent pulls historical field data from BigQuery and feeds it into the tool to map out the three-year calcium balance. Structural remediation takes time. The agent triggers `get_gypsum_sequence` to address deeper sodicity issues, using its massive context window to cross-reference decades of previous soil tests.

Optimize Trace Elements via MCP Server

Calling `get_micronutrient_sequence` prevents induced deficiencies by calculating exact requirements for the upcoming crop cycle. Aggressive pH shifts often cause trace element lock-up if left unmanaged. Your agent ingests the chemical stoichiometry and adjusts the plan dynamically. It ensures the soil biology remains stable while the heavy amendments do their work.

Forecast Lifecycle Expenditures

The agent hits `calculate_total_program_cost` to evaluate the economic efficiency of your proposed timing against current market pricing. Fixing soil structure requires significant capital. You can write the final financial forecast straight back to your cloud database. The enterprise system keeps the agronomy team and the finance department aligned on the three-year budget.

Setup guide

Set up Soil Correction Planner 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 Soil Correction Planner 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="Soil Correction Planner_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Soil Correction Planner tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Soil Correction Planner MCP in Google ADK

Install the framework package. Wrap your endpoint in a StreamableHttpServerParameters object, pass it to McpToolset, and add it to your LlmAgent tools array.

Yes. You can apply a tool_names filter when configuring the toolset to limit the agent to specific calculations.

The tools calculate linear baseline targets. Your agent uses its long context window to adjust these recommendations based on historical biological feedback loops.

The endpoint integrates directly into any standard Google agent workflow. It functions as a standard HTTP target that your deployed models query on demand.

Micronutrient profiles and historical yield data never train external models. The MCP calculates the stoichiometry statelessly and returns the sequence over a secure connection.

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