How to Use the Wakapi (WakaTime Alternative) MCP in Vercel AI SDK

Q: How does running Wakapi (WakaTime Alternative) improve my workflow using Vercel AI SDK?

It lets you pull specific coding statistics (getstats) directly into your Next.js app's UI, making the data appear live. You don't wait for a backend API call to finish; the results stream straight through.

Show real-time code metrics to users with Vercel AI SDK.

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Connect Wakapi (WakaTime Alternative) MCP to Vercel AI SDK

Create your Vinkius account to connect Wakapi (WakaTime Alternative) 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 Wakapi (WakaTime Alternative) with Vercel AI SDK

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Fetch User Coding Stats via MCP Server

Need to know how much time a user spent on specific tasks? Call `get_stats` and your agent gets the raw coding metrics. This data lets you display usage stats live in the UI, not after some annoying loading spinner. When the results appear, they flow directly into React or Next.js components via streaming text. You build a product where the user sees the AI working instantly.

List Projects tracked by MCP Server

Before analyzing activity, you should know what projects exist. Use `list_projects` to pull all tracked project names and IDs. Your agent receives this list immediately so it can scope its analysis correctly. This is perfect for a dashboard where the user needs to select a context before asking questions. You pass the available options directly into your front-end state.

Send Heartbeats with Vercel AI SDK

Your agent tracks work by sending heartbeats. Calling `send_heartbeats` lets you send either a single object or an array of activity objects to Wakapi. This keeps the data pipeline open and ensures accurate tracking. This mechanism is crucial for continuous monitoring. The data it sends enables your AI client to keep track of time spent without manual intervention from the user.

Setup guide

Set up Wakapi (WakaTime Alternative) 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 Wakapi (WakaTime Alternative) 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 Wakapi (WakaTime Alternative) 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 Wakapi. 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 Wakapi (WakaTime Alternative) MCP in Vercel AI SDK

The MCP Server exposes tools your agent calls directly. The results of these tool calls stream back to your client, allowing you to display time metrics or project lists in real-time on the user's screen.

It handles raw coding activity heartbeats. This includes objects detailing time spent, project names, and general usage stats for the connected user.

It lets you pull specific coding statistics (`get_stats`) directly into your Next.js app's UI, making the data appear live. You don't wait for a backend API call to finish; the results stream straight through.

Yes, since it uses standard HTTP transport, your client can integrate calling tools into automated testing or deployment pipelines. The MCP Server simply acts as a callable endpoint.

The server primarily touches coding activity heartbeats, which are structured objects containing time metrics and associated project identifiers.

Use it with your favorite AI tools

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