How to Use the Kippy MCP in Vercel AI SDK

Stream live corporate performance metrics directly into your Next.js application using the Kippy MCP Server and Vercel AI SDK.

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Connect Kippy MCP to Vercel AI SDK

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

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Stream Live HR Data from the Kippy MCP Server

The Kippy MCP Server feeds raw corporate performance data straight to your frontend. You call `list_kpis` and `list_kpi_scores` from your server-side route. The SDK handles the transport layer. Your users see the numbers tick up in real time. Because Vercel AI SDK streams the output, nobody sits staring at a loading spinner while the system calculates quarterly targets.

Build Interactive Org Charts

Building an interactive org chart requires fast directory lookups. You wire up `list_users` and `list_teams` to your AI client. The model fetches the hierarchy on demand. If a manager asks for the engineering department's current headcount, the agent grabs the exact roster. You render the results instantly in a React component.

Surface Compliance Records Instantly

Vercel AI SDK makes surfacing compliance records trivial. Your agent triggers `list_audit_logs` to pull the latest system access events. You pair that with `list_appraisals` to verify who signed off on specific performance reviews. The edge function processes the JSON and streams a formatted report to the client.

Setup guide

Set up Kippy 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 Kippy 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 Kippy transactions",
});

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

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Common questions about Kippy MCP in Vercel AI SDK

You install the @ai-sdk/mcp package. Then you initialize it using createMCPClient with your HTTP transport endpoint.

Yes. You pass the Kippy tools into streamText. The SDK pushes the chunks directly to your UI components as they arrive.

The agent requests the data from the server. Once the server responds, the SDK parses the JSON and streams the resulting text to the user.

You always call mcpClient.close() when your edge function finishes. Leaving connections hanging will drain your serverless execution limits.

Appraisal records contain highly sensitive salary and performance data. The MCP protocol requires a valid authentication token, ensuring your Vercel AI SDK client only reads records the active user is authorized to view.

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