How do I get a Deputy API Token?

Log in to your Deputy account, navigate to **Account Settings** → **API**, and generate a new Permanent Token for your integration.

What is the subdomain?

It is the specific URL part of your Deputy portal, for example: 'mycompany.au.deputy.com'. You need to include the full host string.

Can the agent approve timesheets?

This integration currently focuses on listing and auditing workforce data. Approving timesheets or modifying rosters should be managed through the Deputy dashboard or mobile app.

Deputy MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Deputy
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Deputy through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Deputy "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Deputy?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Deputy MCP Server

Integrate Deputy, the ultimate workforce management solution, directly into your AI workflow. Manage your employee directory, monitor real-time shift rosters, track submitted timesheets, and handle leave requests using natural language.

Pydantic AI validates every Deputy tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Workforce Visibility — List and retrieve detailed profiles for all employees in your Deputy organization.
Roster Monitoring — Track current and upcoming shift rosters to ensure proper coverage across locations.
Timesheet Tracking — Review submitted timesheets, including actual start and end times and approval statuses.
Leave Management — List and monitor employee leave and time-off requests pending approval.

The Deputy MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Deputy to Pydantic AI via MCP

Follow these steps to integrate the Deputy MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Deputy with type-safe schemas

Why Use Pydantic AI with the Deputy MCP Server

Pydantic AI provides unique advantages when paired with Deputy through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your Deputy integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Deputy connection logic from agent behavior for testable, maintainable code

Deputy + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Deputy MCP Server delivers measurable value.

Type-safe data pipelines: query Deputy with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Deputy tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Deputy and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Deputy responses and write comprehensive agent tests

Deputy MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Deputy to Pydantic AI via MCP:

get_authenticated_user

Retrieve metadata for the current authenticated API user

get_employee_profile

Get detailed information for a specific employee

list_active_rosters

List all current and upcoming shift rosters

list_business_locations

List all physical business locations (companies) configured in Deputy

list_completed_timesheets

List timesheets submitted by employees

list_currently_active_shifts

Identify employees who are currently clocked in (mock logic)

list_leave_requests

List all employee leave and time-off requests

list_pending_leave_approvals

List only the leave requests that are awaiting manager approval

list_workforce_employees

List all employees in your Deputy organization

search_employees_by_name

Search for an employee by their display name

Example Prompts for Deputy in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Deputy immediately.

"List all employees currently clocked in."

"Show me the roster for the 'Downtown Kitchen' location tomorrow."

"Are there any pending leave requests?"

Troubleshooting Deputy MCP Server with Pydantic AI

Common issues when connecting Deputy to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Deputy + Pydantic AI FAQ

Common questions about integrating Deputy MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your Deputy MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.