How to Use the Wakapi (WakaTime Alternative) MCP in Google ADK

Q: What does Wakapi (WakaTime Alternative) do with my coding time using Google ADK?

The sendheartbeats tool logs your coding session duration. This data flows into the agent system, ready to be analyzed against existing BigQuery datasets.

Q: Does Wakapi (WakaTime Alternative) support project tracking with Google ADK?

You can use listprojects through the toolset. This helps your agents maintain a precise map of all tracked work, which is key for enterprise governance.

Build enterprise agents using Google ADK for Wakapi (WakaTime Alternative).

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Connect Wakapi (WakaTime Alternative) MCP to Google ADK

Create your Vinkius account to connect Wakapi (WakaTime Alternative) to Google ADK 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 Google ADK

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Calculate Coding Metrics via MCP Server

The `get_stats` tool lets your agent retrieve precise coding statistics. This goes beyond basic counts; it gives structured data on user performance over time. When you're building enterprise agents, you need this depth of information to feed into BigQuery analysis models.

Analyze Activity Summaries with Google ADK

Need a full picture of what happened? Call `get_summaries` to pull comprehensive activity reports. This data is perfect for long-context reasoning within Gemini. It allows your agents to understand historical trends, making them much more valuable than simple command-line scripts.

List and Reference Projects in Your Workflow

Your agent can check available projects using `list_projects`. This tool confirms all tracked initiatives within Wakapi. It keeps the scope of your multi-agent system tight. This function ensures that when an agent executes, it's doing so against a known, verified project list.

Setup guide

Set up Wakapi (WakaTime Alternative) MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Wakapi (WakaTime Alternative) tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Wakapi (WakaTime Alternative)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Wakapi (WakaTime Alternative) tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Wakapi (WakaTime Alternative) MCP in Google ADK

You pass the MCP Server toolset to your LlmAgent. Since you're already working on Google Cloud, this keeps everything native and leverages BigQuery integration easily.

Yes. The 1M+ token context window of Gemini models handles the complex data streams from the MCP Server, allowing you to process massive amounts of activity logs.

The `send_heartbeats` tool logs your coding session duration. This data flows into the agent system, ready to be analyzed against existing BigQuery datasets.

You can use `list_projects` through the toolset. This helps your agents maintain a precise map of all tracked work, which is key for enterprise governance.

The server handles coding activity logs, including usage duration, project names, and user IDs. This specific type of productivity metric is what your agents will act upon.

Use it with your favorite AI tools

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