How to Use the Farmonaut MCP in Mastra AI

Build automated, resilient crop monitoring workflows by connecting Farmonaut to Mastra AI.

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Connect Farmonaut MCP to Mastra AI

Create your Vinkius account to connect Farmonaut to Mastra AI 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 Farmonaut with Mastra AI

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Automated Water Stress Workflows

The `get_ndwi` tool measures vegetation water content and soil moisture anomalies to detect early crop stress. In Mastra AI, you wire this tool into a workflow that triggers automatically when water indices drop below a specific threshold. If the initial check fails, Mastra AI uses its built-in retry engine to call `get_soil_moisture` at different root depths. The workflow then branches: if deep soil is dry, it executes `get_crop_advisory` to draft an irrigation plan; if not, it logs a minor alert.

All-Weather Field Analysis with MCP

The `get_sar_analysis` tool bypasses cloud cover using synthetic aperture radar to check crop structure and soil wetness. This MCP Server integration allows your Mastra AI agent to monitor fields during heavy rain seasons without failing due to optical satellite limits. The agent evaluates the backscatter values and automatically decides whether to schedule a follow-up optical scan. If the radar data indicates flood risks, Mastra AI initiates an immediate escalation workflow to alert field managers.

Guarding Compliance with Deforestation Alerts

The `get_deforestation_alerts` tool tracks tree cover loss and land clearing using historical satellite imagery. Your Mastra AI agent runs this tool on a cron schedule to audit field boundaries for conservation compliance. When land changes are detected, Mastra AI triggers a conditional branch to cross-reference the area with registered field boundaries from `get_fields`. It flags unauthorized clearing and drafts a compliance report without human intervention.

Setup guide

Set up Farmonaut MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Farmonaut tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "farmonaut-mcp-client",
  servers: {
    "farmonaut-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Farmonaut Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Farmonaut tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Farmonaut transactions"
);
console.log(result.text);

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Common questions about Farmonaut MCP in Mastra AI

Register the MCP client in your Mastra AI configuration and spread the tools into your agent. You can then build workflows that trigger `get_ndvi` checks and branch based on vegetation health scores.

Yes. Mastra AI features automatic retries with exponential backoff. If the `get_soil_moisture` tool faces rate limits or transient errors, the framework retries the call before executing your irrigation logic.

Mastra AI connects to the server endpoint over SSE or HTTP. It lists all 12 tools, including `get_crop_advisory`, enabling your agents to make decisions based on real-time satellite indices.

Absolutely. You can configure Mastra AI to pause the workflow when `add_field` is called, requiring a manager to approve the GeoJSON boundaries before writing them to the database.

Your crop types and planting dates are handled in ephemeral Vinkius sandboxes. Mastra AI routes the payloads directly to the target API, meaning your proprietary crop rotations and planting schedules are never stored or logged.

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