How to Use the Commodity Price Converter MCP in Google ADK

Q: How to set up Commodity Price Converter for Google ADK?

Install the framework and define your McpToolset using the server URL. Pass this toolset into your LlmAgent to start executing operations.

Connect the Commodity Price Converter MCP to your Google ADK framework for enterprise-grade agricultural data processing.

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Connect Commodity Price Converter MCP to Google ADK

Create your Vinkius account to connect Commodity Price Converter 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 Commodity Price Converter with Google ADK

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Perplexity Vinkius runs on Gemini

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Integrate cost metrics in Google ADK

Use `calculate_cost_per_hectare` to get production cost data in BRL. Your agent fetches current market prices and land yield stats to perform these calculations on demand. This fits into your existing Google Cloud infrastructure. You can chain these results with your BigQuery datasets for deeper analysis.

Automate unit conversions

The `convert_price_units` tool provides instant currency and unit normalization. Your agent receives a comprehensive list of values to normalize diverse global pricing feeds. This reduces the need for custom conversion logic in your code. It keeps your agent focused on strategy rather than unit arithmetic.

Analyze historical market data

The `get_historical_market_trends` tool retrieves recorded price averages for any given month. Your agent queries this to identify patterns in commodity pricing. This enables long-context reasoning over historical trends. You can feed this data into your Gemini models to improve forecasting accuracy.

Setup guide

Set up Commodity Price Converter 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 Commodity Price Converter 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="Commodity Price Converter_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Commodity Price Converter tools via MCP.",
    tools=mcp_tools,
)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Commodity Price Converter. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Commodity Price Converter MCP in Google ADK

Install the framework and define your `McpToolset` using the server URL. Pass this toolset into your `LlmAgent` to start executing operations.

The integration is native, supporting both Stdio and HTTP transports. You can filter tool access by name to keep your agent focused on specific tasks.

Absolutely. You can pass the output from these tools directly into your BigQuery workflows. This allows your agents to act on combined financial and historical datasets.

Yes, it complements the long-context capabilities of your agents. You can pull extensive historical data into your agent memory for deeper reasoning.

The MCP operates via secure HTTP or Stdio channels. Your sensitive market price and yield data are processed in memory and never persisted by the tool itself.

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