How to Use the intelliHR MCP in Pydantic AI

Run type-safe HR workflows with Pydantic AI validating every intelliHR API response at runtime.

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Connect intelliHR MCP to Pydantic AI

Create your Vinkius account to connect intelliHR 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 intelliHR with Pydantic AI

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Validate employee profiles in Pydantic AI

The `get_person` tool fetches detailed records for a specific employee, which Pydantic AI immediately validates against strict schema definitions. If the intelliHR API returns a malformed field or a missing email, the framework fails loudly and instantly. This strict runtime validation prevents your agent from passing bad data to downstream databases. Your Python code remains clean because the framework handles the schema verification automatically before your agent can act on the employee record.

Type-safe role mapping using this MCP Server

The `list_jobs` tool pulls all active employee job assignments into your Pydantic AI environment. This tool works in tandem with `list_positions` to ensure every active worker has a valid, defined title. By running this MCP Server, you guarantee that your agent only processes jobs that match your exact Python models. Any mismatch in job codes or department IDs is caught at the network boundary, keeping your system's data clean.

Audit organization structures and business entities

The `list_business_entities` tool retrieves your registered corporate entities, mapping them directly to Pydantic models. Your agent can then run `list_users` to verify which platform users belong to which legal entity. This double-check prevents unauthorized users from accessing sensitive corporate data. Pydantic AI ensures that the user IDs returned by the server match your internal authorization schemas before any data is displayed.

Setup guide

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

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

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

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Common questions about intelliHR MCP in Pydantic AI

You initialize the connection using the MCPToolset class pointing to your Vinkius HTTP endpoint. Pass this toolset directly into your Pydantic AI Agent constructor to give it access to tools like `list_people`.

The framework raises a validation error immediately, halting execution. This prevents your Pydantic AI agent from hallucinating or propagating corrupted data when querying tools like `list_remuneration`.

Yes, Pydantic AI is model-agnostic. You can connect this server to agents running local models or commercial APIs, and the runtime validation will still enforce schemas on tools like `list_skills`.

No, you should use the unified MCPToolset class. It simplifies connection management and handles the SSE protocol required to talk to Vinkius-hosted servers.

The integration never stores your employee records, pay rates, or training history. Your Vinkius endpoint acts as a secure proxy, and Pydantic AI validates the data structure in your local memory, meaning sensitive details are never written to disk or shared with third parties.

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