How to Use the Breezy HR MCP in Pydantic AI

Q: How do I register the Breezy HR MCP server in Pydantic AI?

Use the MCPToolset constructor with your Vinkius HTTP URL. Pass the resulting toolset into your Agent constructor's toolsets argument to instantly expose all recruiting capabilities.

Q: Can my Pydantic AI agent fetch task templates from Breezy HR?

Yes. Your agent can call listtasktemplates to get a structured list of available tasks. This data is validated at runtime, making it easy to programmatically assign checklists to candidates.

Build type-safe recruiting agents with Pydantic AI to safely manage your Breezy HR candidates and positions.

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Connect Breezy HR MCP to Pydantic AI

Create your Vinkius account to connect Breezy HR 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 Breezy HR with Pydantic AI

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Type-safe candidate creation

Silent data corruption can break your applicant tracking system. When your Pydantic AI agent runs `create_candidate`, every field is validated against strict schemas before the API call is ever made. If the agent attempts to pass an invalid email format or a missing phone number, the system fails immediately. You get clean, structured candidate records in your database without manual verification.

Error-free pipeline moves using MCP Server

Moving an applicant to the wrong stage can ruin their hiring experience. This MCP server lets your agent fetch valid stages using `list_stages` to ensure the target phase actually exists. When executing `move_candidate`, the agent validates the candidate ID and stage ID against Pydantic models. You avoid runtime API errors and keep your pipeline progression completely reliable.

Structured position auditing

Keeping track of open roles requires highly structured data. Your agent can run `list_positions` to pull all active jobs and parse them into clean Python objects. For deeper inspections, the agent calls `get_position` to fetch specific job requirements. Because the data is validated at runtime, your application can safely feed this information into downstream reporting tools.

Setup guide

Set up Breezy HR 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": {
        "breezy-hr-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Breezy HR. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Breezy HR MCP in Pydantic AI

Use the `MCPToolset` constructor with your Vinkius HTTP URL. Pass the resulting toolset into your `Agent` constructor's `toolsets` argument to instantly expose all recruiting capabilities.

If the server returns unexpected data, the framework raises a validation error. This prevents your agent from operating on bad data or hallucinating candidate details.

Yes. Your agent can call `list_task_templates` to get a structured list of available tasks. This data is validated at runtime, making it easy to programmatically assign checklists to candidates.

Yes. Pydantic AI is model-agnostic, meaning you can connect this toolset to local LLMs or commercial APIs. The validation layer remains active regardless of which model you choose.

Your hiring data, including candidate resumes and contact details, is processed in memory and never stored. All communication goes through encrypted, ephemeral MCP sandboxes to prevent leaks.

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