How to Use the Centaur Analytics MCP in Vercel AI SDK

Q: How do I stream Centaur Analytics sensor histories using the Vercel AI SDK?

You call getco2history or gettemperaturehistory within the streamText function. The SDK handles the incoming data stream, allowing your UI to build historical trend lines in real-time.

Stream real-time grain moisture and CO2 metrics directly into your React components with Vercel AI SDK.

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Connect Centaur Analytics MCP to Vercel AI SDK

Create your Vinkius account to connect Centaur Analytics 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 Centaur Analytics with Vercel AI SDK

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Real-time Spoilage Feeds in Next.js

`get_current_readings` pulls live CO2, moisture, and temperature data from the bin sensors straight into your edge-rendered UI. Your Vercel AI SDK client renders these values as they arrive, letting users see instant condition updates without waiting for a full page load. You pair this with `get_sensor_health` to show signal strength and battery levels right next to the sensor readings. When a probe goes offline, the MCP client updates immediately, keeping elevator operators informed of hardware status.

Predictive Grain Quality via Vercel AI SDK MCP Server

`get_quality_forecast` uses simulation models to project grain conditions over the coming weeks, outputting raw data that your frontend maps to interactive charts. By passing this tool to the Vercel AI SDK client, you let users ask conversational questions about future bin states and see the forecast render on the fly. If the forecast shows a dip, the UI uses `get_spoilage_predictions` to display risk levels and recommended actions. Operators get a clear timeline of when grain might degrade, straight from the chat interface.

Automated Facility Health Audits

`get_facility_overview` aggregates status metrics across all storage bins to give your Vercel AI SDK dashboard an instant snapshot of total inventory health via this MCP Server. Your application calls this tool at the start of a session to populate high-level KPIs before the user even types a query. For deeper dives, the client uses `get_bins` to list individual silos and their fill levels. This structure lets users click any bin in the list to trigger localized detail queries instantly.

Setup guide

Set up Centaur Analytics 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 Centaur Analytics 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 Centaur Analytics transactions",
});

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Centaur Analytics. 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 Centaur Analytics MCP in Vercel AI SDK

You call `get_co2_history` or `get_temperature_history` within the `streamText` function. The SDK handles the incoming data stream, allowing your UI to build historical trend lines in real-time.

Yes, the connection works over standard HTTP transport which is fully compatible with Next.js edge runtimes. You spin up the client, grab your grain metrics, and close the session to keep execution times low.

Vinkius handles the authentication handshake behind the scenes. You only need to pass a single endpoint token to your SDK configuration, keeping your physical grain sensor credentials safe from the client side.

When a sensor goes dark, the tool pulls the last known ping so your UI can throw a warning. You can call `get_sensor_health` directly to check if it's a dead battery or a weak signal.

Yes, because your actual sensor readings, CO2 levels, and temperature data are processed inside an isolated V8 sandbox on Vinkius. The SDK only receives the final structured JSON payload, keeping your facility's physical telemetry private.

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