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How to Use the EOSDA Agriculture Satellite Data MCP in Mastra AI

Build resilient, self-healing crop monitoring workflows using Mastra AI and live satellite feeds.

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EOSDA Agriculture Satellite Data MCP on Cursor AI Code Editor MCP Client EOSDA Agriculture Satellite Data MCP on Claude Desktop App MCP Integration EOSDA Agriculture Satellite Data MCP on OpenAI Agents SDK MCP Compatible EOSDA Agriculture Satellite Data MCP on Visual Studio Code MCP Extension Client EOSDA Agriculture Satellite Data MCP on GitHub Copilot AI Agent MCP Integration EOSDA Agriculture Satellite Data MCP on Google Gemini AI MCP Integration EOSDA Agriculture Satellite Data MCP on Lovable AI Development MCP Client EOSDA Agriculture Satellite Data MCP on Mistral AI Agents MCP Compatible EOSDA Agriculture Satellite Data MCP on Amazon AWS Bedrock MCP Support
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Connect EOSDA Agriculture Satellite Data MCP to Mastra AI

Create your Vinkius account to connect EOSDA Agriculture Satellite Data 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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Automated crop analysis workflows with Mastra AI

`create_vegetation_task` acts as the trigger for your automated agricultural monitoring pipelines powered by this MCP server. Your agent initiates the index calculation for a specific field and hands the task ID off to the workflow engine. If the external API experiences a temporary hiccup, Mastra's built-in retry logic keeps the pipeline alive. The system automatically backs off and retries until the calculation successfully registers.

Multi-dataset imagery search pipelines

`search_multi_dataset` allows your workflows to scan both Landsat and Sentinel databases in a single step. The tool outputs cloud cover metrics and scene IDs for your automated filters to evaluate. You can set up conditional branches that automatically reject scenes with more than twenty percent cloud cover. This ensures your downstream analysis only uses high-quality, clear imagery.

Dynamic index verification using MCP tools

`get_available_indices` provides your agent with a real-time list of supported vegetation calculations. Your workflow can query this list before launching a task to ensure the requested index is fully supported. This prevents runtime errors when agronomists request newer or specialized spectral analyses. The agent dynamically adjusts its parameters based on what the server currently supports.

Setup guide

Set up EOSDA Agriculture Satellite Data 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. 1

    Install dependencies

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

  2. 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. 3

    Discover and inject tools

    Call mcpClient.listTools() and spread the result into your agent's tools object. All EOSDA Agriculture Satellite Data tools become native Mastra tools.

  4. 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: "eosda-agriculture-satellite-data-mcp-client",
  servers: {
    "eosda-agriculture-satellite-data-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent EOSDA Agriculture Satellite Data transactions"
);
console.log(result.text);

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by EOSDA. 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 EOSDA Agriculture Satellite Data MCP in Mastra AI

Mastra uses its built-in workflow engine to manage API limits and temporary outages. If `create_vegetation_task` fails, the framework executes exponential retries automatically. This guarantees your agricultural monitoring pipelines don't break during peak processing times.
Yes, you can branch your logic based on cloud cover. Use `search_dataset` to check the cloud percentage, and if it is below your threshold, trigger `create_vegetation_task` to calculate NDVI.
You call `get_task_result` using the task ID generated from your initial request. The workflow engine can poll this endpoint at set intervals until the status shows as completed, then pass the download URL to your database.
The `get_available_datasets` tool returns all satellite sources currently configured on your account. Your agent can use this list to dynamically decide whether to pull Sentinel-2 or Landsat 8 data for a given region.
Your agricultural geometries and spatial coordinates are processed in a zero-trust, ephemeral V8 sandbox. This setup ensures that no geographic boundaries or crop index results are cached or exposed to third parties.

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