How to Use the Wakapi (WakaTime Alternative) MCP in LangChain

Q: How does Wakapi (WakaTime Alternative) integrate with LangChain?

The LangChain framework treats every MCP tool call—like getstats or listprojects—as a distinct function. Your agent decides the order and inputs for these calls, creating a traceable chain of thought.

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Connect Wakapi (WakaTime Alternative) MCP to LangChain

Create your Vinkius account to connect Wakapi (WakaTime Alternative) to LangChain 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 LangChain

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Determine user metrics with `get_stats`

The `get_stats` tool pulls raw coding statistics for a specified user. This lets the agent calculate specific metrics, like time spent per language or most active days. You can chain this output: first, use `list_projects` to narrow down which projects' stats matter, and then pass those project IDs into `get_stats`. The agent handles that logic automatically.

Summarize activity using `get_summaries`

Calling `get_summaries` returns a detailed narrative of the user's coding period. Instead of just raw numbers, you get context about what happened—which is useful for reports. The agent can use this summary text to answer complex questions. For example, it might combine the summary with project details from `list_projects` to explain *why* productivity dropped on a certain date.

Manage tracked projects via `list_projects`

`list_projects` quickly provides a roster of all coding efforts tracked in Wakapi. This tool is the starting point for most reporting actions, giving you context about scope. The agent can check this list first to ensure that any stats or summaries it pulls back are relevant to currently active projects. It acts as a filter before deeper analysis.

Setup guide

Set up Wakapi (WakaTime Alternative) MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Wakapi (WakaTime Alternative) tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "wakapi-wakatime-alternative-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Wakapi (WakaTime Alternative) transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Wakapi (WakaTime Alternative) MCP in LangChain

The LangChain framework treats every MCP tool call—like `get_stats` or `list_projects`—as a distinct function. Your agent decides the order and inputs for these calls, creating a traceable chain of thought.

Yep. Because LangChain is built for chaining, you can't just ask one question. You build a pipeline: the agent runs tool A, takes its output, and feeds it into tool B to get the final answer.

The server exposes coding statistics, project lists, and activity summaries. The agent uses these discrete data types to build its reasoning process when running with LangChain.

Absolutely. Every step—the inputs, the outputs, and the time taken for each tool call—is logged, giving you full visibility into how your agent arrived at its conclusion.

It primarily touches structured coding statistics and project metadata. These are the core data types used for all reporting via the `get_stats` tool.

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