How to Use the AssessTEAM MCP in Vercel AI SDK

Q: How do I stream AssessTEAM timesheet data directly to React components?

Use streamText from the Vercel AI SDK and pass the listtimesheets tool in the tools object. The model will fetch the raw records, and you can render them live in your UI as they stream in.

Q: What happens if an employee tries to log hours for a project they aren't on?

The createtimesheetentry tool checks active assignments via listprojects first. If there is a mismatch, the model catches the error and explains the issue directly to the user in the chat interface.

Q: How is employee performance evaluation data kept secure when using this server?

All evaluation records fetched via listperformanceevaluations run inside an isolated V8 sandbox on Vinkius. Your sensitive HR data is never stored on our servers and is wiped from memory as soon as the execution finishes.

Stream timesheet updates and employee performance reviews directly into your Next.js UI using the Vercel AI SDK.

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Connect AssessTEAM MCP to Vercel AI SDK

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

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Live profitability dashboards via Vercel AI SDK

Your Next.js app can now show project margins without making users wait for heavy page loads. By passing the `get_profitability_report` tool to `streamText`, your UI renders financial metrics piece by piece as the model processes the data. This setup lets you build custom client portals that query project costs on the fly. Your React components update instantly as the model pulls active data from `list_projects` to match timesheets with budgets.

Log hours instantly in your React components

Stop building complex forms for logging hours. Let your users type natural sentences like "Logged three hours on the redesign" and let `create_timesheet_entry` handle the rest. The Vercel AI SDK feeds this intent directly to the AssessTEAM MCP Server, running the execution inside a secure edge function. Because it supports streaming UI, the user sees their logged hours confirmed in real-time. The SDK calls `list_timesheets` immediately after to refresh their history list without a full page reload.

Interactive review interfaces with Vercel AI SDK

Building interface components for HR managers gets easier when your frontend can talk directly to your evaluation pipeline. Using `list_performance_evaluations`, this MCP Server makes it easy to fetch and display historical performance data inside a streaming chat window. The model matches team structures via `list_organizational_teams` to show managers exactly who needs a review next. The entire flow runs on the edge, keeping your React frontend fast and responsive.

Setup guide

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

You do not need to manage API keys or OAuth flows inside your Next.js codebase. Vinkius handles the authorization layer, giving you a single secure endpoint token to pass into your `createMCPClient` setup.

Yes, the Vercel AI SDK client is fully compatible with Edge Functions. You can call tools like `list_employees` directly from edge middleware or API routes without cold-start delays.

Use `streamText` from the Vercel AI SDK and pass the `list_timesheets` tool in the tools object. The model will fetch the raw records, and you can render them live in your UI as they stream in.

The `create_timesheet_entry` tool checks active assignments via `list_projects` first. If there is a mismatch, the model catches the error and explains the issue directly to the user in the chat interface.

All evaluation records fetched via `list_performance_evaluations` run inside an isolated V8 sandbox on Vinkius. Your sensitive HR data is never stored on our servers and is wiped from memory as soon as the execution finishes.

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