How to Use the EOSDA Agriculture Satellite Data MCP in Vercel AI SDK

Q: How do I search multiple satellite constellations using EOSDA Agriculture Satellite Data in my Vercel AI SDK app?

Use the searchmultidataset tool to query Landsat and Sentinel data in a single call. Pass the coordinates and date range directly from your UI. Your AI client will parse the results and stream the scene details back to your frontend.

Q: Can I calculate NDVI on demand with EOSDA Agriculture Satellite Data and Vercel AI SDK?

Yes, you initiate this by calling createvegetationtask with your target coordinates and index type. Once the task starts, you use gettaskresult to pull the final processed GeoTIFF. The SDK streams the progress directly to your user interface.

Q: What is the best way to list available indices using the EOSDA Agriculture Satellite Data MCP server?

Call the getavailableindices tool inside your model stream. This returns all supported vegetation indices, like NDVI or NDRE, which your UI can then display as selectable options for the user.

Feed raw satellite imagery analyses directly into your Next.js user interface with Vercel AI SDK.

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Connect EOSDA Agriculture Satellite Data MCP to Vercel AI SDK

Create your Vinkius account to connect EOSDA Agriculture Satellite Data to Vercel AI SDK 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 EOSDA Agriculture Satellite Data with Vercel AI SDK

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Live satellite data streaming via Vercel AI SDK

`search_multi_dataset` lets your agent query Sentinel-2 and Landsat 8 imagery simultaneously based on user-defined coordinates. This MCP server returns scene IDs, cloud cover percentages, and direct download links right to your application. Instead of waiting for a slow backend process to finish, your user interface displays these satellite scenes as they stream in. Your users get immediate access to raw spatial data without staring at a blank loading screen.

Instant vegetation index task creation

`create_vegetation_task` initiates calculations for indices like NDVI or EVI over any specific agricultural boundary using this MCP server. Your AI client handles the heavy lifting by choosing the right coordinates and firing off the processing request. Because Vercel's framework processes these requests at the edge, the generated task ID is passed back instantly. You can immediately feed this ID into the next step of your UI workflow to keep the user informed.

Real-time status updates for crop analysis

`get_task_result` pulls the processed vegetation data and download URLs once the satellite analysis finishes. Your frontend displays the direct GeoTIFF links the second they become available. You don't have to build complex polling infrastructure on your own. The SDK handles the asynchronous updates, letting your interface render the crop health maps dynamically as the remote server finishes the job.

Setup guide

Set up EOSDA Agriculture Satellite Data MCP in Vercel AI SDK

Prerequisites

Node.js 18+ and a TypeScript project
ai + @modelcontextprotocol/sdk packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies

Run npm install ai @modelcontextprotocol/sdk plus your preferred model provider (e.g. @ai-sdk/openai).
2

Create the Streamable HTTP transport

Use StreamableHTTPClientTransport with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and use tools

Call mcpClient.tools() to auto-discover all EOSDA Agriculture Satellite Data tools. Pass them directly to generateText() or streamText() — no manual schema definitions needed.
4

Works with any model provider

Swap openai("gpt-4o") for any AI SDK provider — Anthropic, Google, Mistral. The MCP tools work identically across all supported models.

index.ts

import { experimental_createMCPClient as createMCPClient } from "ai";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const transport = new StreamableHTTPClientTransport(
  new URL("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
);

const mcpClient = await createMCPClient({ transport });
const tools = await mcpClient.tools();

const { text } = await generateText({
  model: openai("gpt-4o"),
  tools,
  prompt: "List recent EOSDA Agriculture Satellite Data transactions",
});

console.log(text);
await mcpClient.close();

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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 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 Vercel AI SDK

Use the `search_multi_dataset` tool to query Landsat and Sentinel data in a single call. Pass the coordinates and date range directly from your UI. Your AI client will parse the results and stream the scene details back to your frontend.

Yes, you initiate this by calling `create_vegetation_task` with your target coordinates and index type. Once the task starts, you use `get_task_result` to pull the final processed GeoTIFF. The SDK streams the progress directly to your user interface.

Call the `get_available_indices` tool inside your model stream. This returns all supported vegetation indices, like NDVI or NDRE, which your UI can then display as selectable options for the user.

It does not send giant binary images directly through the LLM. Instead, tools like `get_task_result` return secure download URLs for the processed GeoTIFFs, which your frontend can then render using standard mapping libraries.

Absolutely. Your spatial coordinates and boundary geometries are sent directly to the EOSDA API over encrypted connections. Vinkius runs the server in an isolated sandbox, meaning your raw agricultural coordinates are never stored or logged permanently.

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