How to Use the Timeero MCP in Pydantic AI

Build type-safe field service agents with Pydantic AI and Timeero, ensuring every piece of payroll data is validated at runtime.

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Connect Timeero MCP to Pydantic AI

Create your Vinkius account to connect Timeero to Pydantic AI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Timeero with Pydantic AI

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Type-safe timesheet validation

Every response from `list_timeero_timesheets` is checked against your Pydantic schemas. If the API returns malformed GPS data or missing timestamps, the agent stops immediately. This prevents bad data from corrupting your records. By using `get_timeero_timesheet`, your agent ensures only clean, verified information is processed for your payroll calculations.

Strict control over field assignments

Use `list_timeero_jobs` and `get_timeero_job` to pull work orders into your agent logic. Because the framework forces type validation, you never deal with unexpected field types or empty payloads. This makes your scheduling logic predictable. Your agent verifies every job detail against your requirements before it ever attempts to update a schedule.

Reliable API and user management

Call `check_timeero_status` to verify the connection health before your agent executes critical tasks. If the service is unreachable, the agent fails safely rather than guessing. Use `list_timeero_users` and `get_timeero_user` to manage your team database. The strict type checking ensures you always have the correct user objects for your reporting tools.

Setup guide

Set up Timeero 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": {
        "timeero-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Timeero transactions")
print(result.output)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Timeero. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Timeero MCP in Pydantic AI

It provides runtime validation for all 11 tools. If Timeero returns unexpected data, your agent throws an error instead of continuing with bad information.

Yes. All tool outputs are automatically mapped to Pydantic models. You get full IDE autocompletion and type safety when working with `list_timeero_tasks` or any other tool.

Security is handled at the MCP server level. Only the authorized Pydantic AI agent can communicate with the server using your private endpoint token.

You can. The framework is model-agnostic, so you can point your agent at local or hosted models while maintaining strict type safety on Timeero data.

It exposes sensitive operational data including employee timesheets, GPS coordinates, job assignments, and user contact details, all protected by strict server-side authentication.

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