How to Use the Greenspark MCP in Vercel AI SDK

Embed climate action directly into your Vercel AI SDK interface. Trigger tree planting and carbon offsets as your AI streams data.

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Connect Greenspark MCP to Vercel AI SDK

Create your Vinkius account to connect Greenspark 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 Greenspark with Vercel AI SDK

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Real-time impact streaming with AI SDK

Feed `create_impact` results into your frontend state to show users their climate contribution as it happens. Since your AI SDK stream handles the data, you avoid the lag of traditional API calls. Your users watch the impact manifest in the UI while the agent processes their transaction. This creates a tangible connection between their digital activity and real-world environmental projects.

Calculate carbon footprints inside AI SDK flows

Use `estimate_footprint` to analyze transaction data before finalizing a user request. You get the carbon cost immediately, allowing your agent to present options for offset projects. Connecting this tool to your generation loop ensures that climate data is always current. You aren't just showing static numbers; you're providing live, calculated evidence of environmental impact.

Monitor environmental projects through Vercel

Call `list_projects` to populate selection menus within your application components. Your agents can then pull specific project details using `get_project` to inform users exactly where their contributions land. This keeps your application logic tight and focused. By pulling data directly into the SDK environment, you eliminate the need for secondary backend services to manage climate records.

Setup guide

Set up Greenspark 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 Greenspark 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 Greenspark 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 Greenspark. 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 Greenspark MCP in Vercel AI SDK

Install the MCP client packages and point them to your endpoint. You then pass the tool definitions directly into your generateText or streamText functions.

Yes, it is built to run on the edge. You just need to ensure your environment handles the MCP transport protocol correctly during the request lifecycle.

We handle account data through encrypted endpoints. Only your authorized agent can trigger impacts or view your specific transaction history.

The server pushes data through the MCP stream. Your frontend reacts to these tool results as they arrive.

You catch the error in your tool execution logic. Since it is just a standard function call, you handle it like any other API request.

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