How to Use the Clockify MCP in Pydantic AI

Q: What's the setup process for Pydantic AI and Clockify?

It's straightforward. After you pip install "pydantic-ai-slimmcp", you create an MCPToolset instance with the server URL from Vinkius. Then you just pass that toolset to your Pydantic AI Agent.

Add type-safe Clockify tools to any agent. With Pydantic AI, you get validated, error-free time tracking data every single time.

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Connect Clockify MCP to Pydantic AI

Create your Vinkius account to connect Clockify 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 Clockify with Pydantic AI

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Create Time Entries That Can't Fail Silently

Use the `add_new_time_entry` tool to have your agent log work in Clockify. Your agent can also `list_workspace_projects` first to make sure it's logging time against a valid project ID. Here's the key: Pydantic AI validates the response from the Clockify API against a Pydantic model. If the API ever returns something unexpected—a missing field, a wrong data type—your code will raise a `ValidationError` immediately. No silent data corruption.

Build Correct-by-Construction Reports

Your agent can pull all the data needed for billing or internal audits. It can `list_user_time_entries` for a specific period and combine it with `list_workspace_clients` to prepare invoices. Because every piece of data is parsed into a Pydantic model, you know it's correct. You don't have to write defensive code checking if a key exists or if a value is null. If the agent gives you the data, it's already been validated.

A Model-Agnostic Clockify MCP Server

This MCP Server gives your agent a consistent set of tools for interacting with Clockify. It can find users with `list_workspace_users`, stop timers with `stop_current_timer`, and get basic profile info with `get_my_clockify_profile`. Pydantic AI is model-agnostic, so you can use these tools with OpenAI, Anthropic, Gemini, or even local models. The validation layer works the same way regardless of which LLM is driving your agent. Your code for handling Clockify data remains consistent.

Setup guide

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

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

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

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Common questions about Clockify MCP in Pydantic AI

Pydantic AI automatically validates every response from this MCP server against a corresponding Pydantic model. If the Clockify API returns data that doesn't match the expected structure, your agent will raise an error instead of working with bad data. It's correctness by default.

Yes. Pydantic AI is designed to be model-agnostic. As long as you can connect your local model to the framework, you can use this Clockify MCP server to give it tools for time tracking.

It's straightforward. After you `pip install "pydantic-ai-slim[mcp]"`, you create an `MCPToolset` instance with the server URL from Vinkius. Then you just pass that toolset to your Pydantic AI `Agent`.

This is where Pydantic AI shines. If an API change causes the server to return data in a new format, Pydantic AI's validation will catch it immediately and raise an error. Your agent won't silently fail or process bad data.

Vinkius processes all requests through single-use, sandboxed containers. Your agent's requests for time entries, projects, and user info are encrypted and authenticated with your token. The server is stateless and doesn't retain any of your data.

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