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

Q: Can Wakapi (WakaTime Alternative) handle historical queries using LlamaIndex?

Yes. Since LlamaIndex indexes the data from tools like getsummaries, you can query past sessions or configurations and get answers grounded in actual API history, not guesses.

Index coding activity results into a searchable knowledge graph with LlamaIndex.

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Connect Wakapi (WakaTime Alternative) MCP to LlamaIndex

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

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Index project data using `list_projects`

`list_projects` pulls all tracked projects, which LlamaIndex indexes immediately. Instead of just seeing a list, you create searchable chunks of knowledge about every single repository. Later, your RAG application can query this index and say, 'What were the key goals for my project X?' and get an answer grounded in that indexed data.

Query past activity with `get_summaries`

The detailed text from `get_summaries` becomes part of a searchable knowledge base. This means you can ask complex questions about your history, like 'How did my coding focus shift between Q1 and Q2?' The answer won't be pulled from memory; it comes directly from the indexed data returned by this MCP Server.

Analyze metrics using `get_stats`

`get_stats` gives you raw coding statistics, but LlamaIndex turns those numbers into context. You can query things like, 'What was my average daily commit count in Python last month?' The resulting data structure is then indexed, allowing semantic search over numerical and textual facts.

Setup guide

Set up Wakapi (WakaTime Alternative) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Wakapi (WakaTime Alternative) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Wakapi (WakaTime Alternative) tools.",
)
response = await agent.run("List recent Wakapi (WakaTime Alternative) data")

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Common questions about Wakapi (WakaTime Alternative) MCP in LlamaIndex

LlamaIndex uses the MCP output to build a vector store. The tool calls aren't just executed; their results are embedded and made searchable, giving you deep context.

Yes. Since LlamaIndex indexes the data from tools like `get_summaries`, you can query past sessions or configurations and get answers grounded in actual API history, not guesses.

The core power here is turning transient API data into permanent knowledge. The output of an MCP tool becomes a piece of your searchable, unified index.

You build RAG applications where live API data and documents meet in the same place. This combination lets you query across both your internal documents and external MCP tool results.

It touches coding statistics, project metadata, and narrative summaries. All of these structured and unstructured texts are indexed into the vector store.

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