How to Use the Hurma MCP in Pydantic AI

Ensure type-safe HR automation by pairing the Hurma MCP Server with Pydantic AI for strict runtime validation.

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Connect Hurma MCP to Pydantic AI

Create your Vinkius account to connect Hurma 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 Hurma with Pydantic AI

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Validate HR actions with Pydantic AI

Every response from `get_vacation_balance` is checked against your Pydantic models. If the data is malformed, the agent stops before it can act. This prevents silent failures when querying leave requests. You get a clean, typed object every time the tool returns a result.

Manage departments using Pydantic AI

Run `list_departments` to get a structured list of your company units. The agent maps these directly to your internal business logic. Use the validated output to assign tasks or filter employees. You never have to worry about missing or unexpected fields in the response.

Automate records with Pydantic AI

The agent uses `create_candidate` to add new applicants while enforcing schema rules. Any input mismatch triggers an immediate validation error. This keeps your recruitment database clean and consistent. You only deal with data that matches your defined structures.

Setup guide

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

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

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

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Common questions about Hurma MCP in Pydantic AI

It runs the server output through your Pydantic models. If the data doesn't match your schema, the agent throws an error immediately.

Yes. Add the MCPToolset to your agent definition, and it will handle the communication with the Hurma server.

It handles everything from employee profiles and leave requests to recruitment stages and custom field definitions.

We enforce strict security protocols for every session. Your employee records are never exposed to unauthorized processes or external systems.

It does. Because the agent relies on typed models, it cannot hallucinate fields that don't exist in the Hurma API response.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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