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

Q: What specific metrics does Wakapi (WakaTime Alternative) provide for Mastra AI?

It provides coding statistics (getstats) and detailed summaries of activity. This allows your workflow to make decisions, like rerouting a failed transaction or escalating an alert.

Q: How does Wakapi (WakaTime Alternative) improve my complex workflows using Mastra AI?

It gives your agent the ability to pull project lists (listprojects) as part of its execution flow. This lets you build multi-step processes that handle specific, tracked organizational units.

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Connect Wakapi (WakaTime Alternative) MCP to Mastra AI

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Setup Wakapi (WakaTime Alternative) with Mastra AI

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Retrieve Activity Summary from MCP Server

Use `get_summaries` when your workflow needs a detailed breakdown of work. Your agent gets a comprehensive summary object it can then pass to the next step in the chain. This helps decision points determine the right path. Complex workflows rely on accurate context. Getting this activity summary lets Mastra AI make informed choices, like deciding whether to retry a failed payment or notify an admin.

Track Project Scope via MCP Server

Your multi-step workflow needs to know the scope of work. Calling `list_projects` returns every project tracked by the user, which Mastra AI can use for conditional branching. If a payment fails on 'Project Alpha', it knows exactly where to send the retry notice. It's key data for ensuring that automated retries or escalation alerts are always directed to the right place.

Send Heartbeats with Mastra AI

The agent tracks work by sending heartbeats. `send_heartbeats` allows you to feed an array of activity objects into your workflow engine. This feeds the system continuous data, keeping the tracking records current. This mechanism is essential for workflows that need to monitor long-running processes and detect deviations from normal operation.

Setup guide

Set up Wakapi (WakaTime Alternative) MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Wakapi (WakaTime Alternative) tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "wakapi-wakatime-alternative-mcp-client",
  servers: {
    "wakapi-wakatime-alternative-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Wakapi (WakaTime Alternative) Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Wakapi (WakaTime Alternative) tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Wakapi (WakaTime Alternative) transactions"
);
console.log(result.text);

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Common questions about Wakapi (WakaTime Alternative) MCP in Mastra AI

The MCP Server exposes tools your agent calls, allowing Mastra AI to use the results in conditional logic. For example, if a workflow needs stats, it can call `get_stats` and then act based on that data.

It provides coding statistics (`get_stats`) and detailed summaries of activity. This allows your workflow to make decisions, like rerouting a failed transaction or escalating an alert.

Absolutely. If your automated process fails—say, the payment gateway times out—your workflow can call `get_summaries` to get context and then execute a specific retry strategy.

It gives your agent the ability to pull project lists (`list_projects`) as part of its execution flow. This lets you build multi-step processes that handle specific, tracked organizational units.

The server touches coding activity heartbeats and derived usage stats, which are critical for calculating overall productivity metrics within your workflow.

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