How to Use the Humaans MCP in Pydantic AI

Build type-safe Humaans integrations with Pydantic AI for guaranteed data integrity and zero-corruption agent logic.

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Connect Humaans MCP to Pydantic AI

Create your Vinkius account to connect Humaans 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 Humaans with Pydantic AI

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Validate Humaans responses in Pydantic AI

Every response from the HRIS is checked against your Pydantic schemas at runtime. If the API returns unexpected values, the agent stops immediately. Use `list_leaves` or `list_public_holidays` with total confidence. You'll never deal with silent corruption or hallucinated fields in your agent's decision-making.

Use Humaans tools with any model in Pydantic AI

The framework is model-agnostic, meaning you can swap between OpenAI, Anthropic, or local models. The MCP server provides a consistent interface for all of them. Call `get_employee` and get back a structured, validated object every time. This consistency makes your code easier to maintain and test.

Implement strict Humaans schema enforcement

Define the exact structure you expect from `list_departments` or `list_roles` using Pydantic models. The agent rejects any tool output that doesn't match your spec. This strictness is perfect for production environments where data quality is non-negotiable. It forces the agent to handle errors explicitly rather than guessing.

Setup guide

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

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

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

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Common questions about Humaans MCP in Pydantic AI

It ensures that every piece of data retrieved from the HRIS is type-checked. If the schema changes or the API returns garbage, your agent fails safely instead of continuing with bad data.

Install the slim MCP package, define your Pydantic models, and add the server URL to your MCPToolset. The framework handles the validation logic automatically.

Yes, it supports both Streamable HTTP and SSE. You can choose the transport that provides the best reliability for your specific environment.

Absolutely. Since the toolset is model-agnostic, you can connect your local models to the Humaans server without changing any of your integration code.

We enforce strict validation on all document metadata retrieved from the server. This ensures that only well-formed, authorized paths enter your agent's logic, keeping your sensitive HR files secure.

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