How to Use the Everhour Time Tracking MCP in Pydantic AI

Q: How do I configure Everhour Time Tracking for Pydantic AI?

Install the framework with pip install "pydantic-ai-slimmcp". Create an MCPToolset using your HTTP endpoint, and pass it to your Agent via the toolsets parameter.

Use the Everhour Time Tracking MCP Server with Pydantic AI to enforce strict type validation on your team's logged hours.

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Connect Everhour Time Tracking MCP to Pydantic AI

Create your Vinkius account to connect Everhour Time Tracking 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 Everhour Time Tracking with Pydantic AI

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Validate time logs with Pydantic AI

The `list_team_time_records` tool lists time records for the team within a specific date range. Pydantic AI intercepts this payload and forces it through your defined schemas. If Everhour returns an unexpected string instead of a float, the framework throws a loud validation error. For immediate checks, `quick_time_tracking_audit` retrieves a high-level summary of recent time entries and active projects. Your agent knows exactly what data structure to expect, eliminating hallucinated fields entirely.

Enforce budget constraints via MCP Server

The `list_projects_within_budget` tool identifies projects that are currently within their assigned time or monetary budget. You define the exact Pydantic model for a healthy project, and the agent strictly validates the API response against it. If a project looks off, `get_project_detailed_data` gets detailed settings and budget information for a specific project. Your agent reads the exact numbers and fails the run if the data types mismatch, preventing corrupted financial reports.

Map users and running timers safely

The `get_everhour_user_metadata` tool retrieves metadata and profile information for the current Everhour user. Your Pydantic AI agent guarantees every user record has the required fields before passing it to your identity system. When tracking active work, `get_currently_running_timer` retrieves the task and start time for any currently active timer. The framework ensures the timestamp format is perfectly valid before your code processes it.

Setup guide

Set up Everhour Time Tracking 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": {
        "everhour-time-tracking-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Everhour Time Tracking transactions")
print(result.output)

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Common questions about Everhour Time Tracking MCP in Pydantic AI

Install the framework with pip install "pydantic-ai-slim[mcp]". Create an MCPToolset using your HTTP endpoint, and pass it to your Agent via the toolsets parameter.

It guarantees data correctness. When you pull records using list_team_time_records, you know the data perfectly matches your Python types before your code touches it.

No. You need to use the unified MCPToolset approach. The older HTTP class is deprecated and will cause issues.

Pydantic AI fails loudly. Instead of silently passing a null value into your database, it throws a validation error so you can fix the issue immediately.

This integration processes precise billing client records and invoicing configurations. Authentication requires a single endpoint token, and the server runs in a stateless, managed environment that retains zero memory of your financial queries.

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