Is it safe to expose my company's payrolls to the AI?

Yes. This connector runs entirely on your local machine or trusted enclave natively. No employee data, payload information or generated queries are routed to our servers—your API interactions occur directly from your environment straight to SmartHR's endpoints.

Can the agent calculate average payroll over time automatically?

Absolutely. Just prompt your agent: 'List all payrolls from Q2, and display the average distribution'. The agent will call the `list_payrolls` endpoint, retrieve the raw figures, do the mathematical operation independently in context, and present you a very clear table or markdown summary.

How do I look up which internal department an employee works for?

You don't need to specify the exact tool. You can simply ask: 'What department is crew member [Name] in?'. Your AI will automatically use the `list_crews` tool and cross reference the results with `list_departments` to formulate a correct response.

SmartHR MCP Server for Pydantic AI 8 tools — connect in under 2 minutes

Name: SmartHR
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 8 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect SmartHR through 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 SmartHR "
            "(8 tools)."
        ),
    )

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

asyncio.run(main())

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* 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 SmartHR MCP Server

Connect your SmartHR directory to any AI agent and empower your team to query employee data, organizational hierarchies, and payroll records securely. Interact with your organization's human capital database through natural language without ever switching tabs.

Pydantic AI validates every SmartHR tool response against typed schemas, catching data inconsistencies at build time. Connect 8 tools through 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

Employee Tracking — Call the list_crews tool to retrieve the roster of all employees and cross-reference demographic metadata
Department Hierarchies — Ask your AI to list_departments or check list_positions to understand the internal structure
Deep Employee Profiles — Perform targeted queries using get_crew_details or list_crew_dependents to fetch highly sensitive contract details
Payroll Overviews — Securely query active or historical payroll ledgers to audit compensation scaling

The SmartHR MCP Server exposes 8 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 SmartHR to Pydantic AI via MCP

Follow these steps to integrate the SmartHR 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 8 tools from SmartHR with type-safe schemas

Why Use Pydantic AI with the SmartHR MCP Server

Pydantic AI provides unique advantages when paired with SmartHR 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 SmartHR integration code

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

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

SmartHR + Pydantic AI Use Cases

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

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

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

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

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

SmartHR MCP Tools for Pydantic AI (8)

These 8 tools become available when you connect SmartHR to Pydantic AI via MCP:

get_crew_details

Retrieves details for a specific employee

list_crew_dependents

Lists dependents for a specific employee

list_crews

Lists all employees (crews) in SmartHR

list_departments

Lists all organizational departments

list_employment_types

Lists all employment types

list_establishments

Lists business establishments or office locations

list_payrolls

Lists employee payroll records

list_positions

Lists all job positions or roles

Example Prompts for SmartHR in Pydantic AI

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

"Fetch the entire roster and list the top 3 job positions that occur most frequently."

"Retrieve the details for crew member `crew-8f192`, including their enrolled dependents."

"List all physical establishments the company operates."

Troubleshooting SmartHR MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

SmartHR + Pydantic AI FAQ

Common questions about integrating SmartHR 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 SmartHR MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.