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

Q: Can I use Wakapi (WakaTime Alternative) for complex workflows with Pydantic AI?

You sure can. Because every API call, including those from getstats, is type-checked at runtime, your agents can handle multi-step processes without risking bad data.

Q: What happens to my coding time when I use Wakapi (WakaTime Alternative) with Pydantic AI?

The sendheartbeats tool sends the raw data, but Pydantic validates it on receipt. This ensures your agent is only working with clean, predictable usage metrics.

Q: Does Wakapi (WakaTime Alternative) support project listing validation with Pydantic AI?

Yes. When you call listprojects, the resulting list of projects must conform to your defined model. It's a strict check that prevents downstream errors.

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Connect Wakapi (WakaTime Alternative) MCP to Pydantic AI

Create your Vinkius account to connect Wakapi (WakaTime Alternative) to Pydantic AI 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 Pydantic AI

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Get Coding Stats with MCP Server

The `get_stats` tool pulls structured coding statistics, and Pydantic makes sure that response is perfect. If the API throws unexpected data, your agent fails loudly—no silent corruption. This means you get reliable metrics every time, which is critical when building production-grade AI pipelines.

Validate Activity Summaries for Pydantic AI

Use `get_summaries` to grab activity details. Because of the type validation layer, you're guaranteed that every field—from project names to dates—is exactly what your model expects. It eliminates the headache of working with messy, unstructured API responses.

Manage Project List Safely

Calling `list_projects` through the toolset delivers a list that validates against your Pydantic schema. You don't have to worry about missing or malformed project objects. Correctness over speed is what this framework guarantees, which is exactly what you need for mission-critical agents.

Setup guide

Set up Wakapi (WakaTime Alternative) MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "wakapi-wakatime-alternative-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Wakapi (WakaTime Alternative) tools.",
)

result = await agent.run("List recent Wakapi (WakaTime Alternative) transactions")
print(result.output)

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

The MCP Server sends data to your agent, and the Pydantic framework immediately validates it. If the data doesn't match the expected types, the agent stops and tells you exactly what went wrong.

You sure can. Because every API call, including those from `get_stats`, is type-checked at runtime, your agents can handle multi-step processes without risking bad data.

The `send_heartbeats` tool sends the raw data, but Pydantic validates it on receipt. This ensures your agent is only working with clean, predictable usage metrics.

Yes. When you call `list_projects`, the resulting list of projects must conform to your defined model. It's a strict check that prevents downstream errors.

The server deals with coding activity logs, including usage duration and project identifiers. Pydantic ensures these specific fields are always correctly typed for your agent.

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