How to Use the Ninehire MCP in Pydantic AI

Build correct and type-safe hiring agents with Pydantic AI, ensuring every response from Ninehire matches your data models.

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Connect Ninehire MCP to Pydantic AI

Create your Vinkius account to connect Ninehire 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 Ninehire with Pydantic AI

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Prevent Bad Data in Your Pipeline

This is for when you can't afford silent failures. Pydantic AI wraps every call to the Ninehire MCP server in a Pydantic model. If your agent calls `get_applicant_profile` and the API returns an unexpected field or a wrong data type, your code will raise a `ValidationError`. You'll know immediately that something is wrong. This prevents corrupted data from getting into your system and stops your agent from making decisions based on malformed information. It's about building defensively.

Model Your Hiring Process

Define your hiring workflow with Pydantic models, then use tools from this MCP server to populate them. Create models for jobs, applicants, and evaluations. Your agent can then use `get_job_details` and `get_applicant_profile` to fetch data that is guaranteed to fit your predefined structures. This approach makes your code predictable. You can see exactly what data `list_candidate_evaluations` should return, and Pydantic AI enforces it at runtime. It's a clean way to separate your business logic from the API calls.

Work With Any LLM

Pydantic AI is model-agnostic. You can use it with OpenAI, Gemini, Anthropic, or even a local model you're running yourself. The Ninehire tools will work the same way regardless of the LLM you choose for reasoning. This gives you the freedom to swap out the 'brain' of your agent without having to rewrite all your tool-using logic. Your agent's ability to `register_new_applicant` or `list_job_postings` remains constant, backed by Pydantic's validation.

Setup guide

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

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

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

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Common questions about Ninehire MCP in Pydantic AI

Pydantic AI gives you runtime type safety. It guarantees that the data your agent gets from Ninehire tools like `get_applicant_profile` is exactly what your Python code expects. This prevents a whole class of bugs caused by unexpected API responses.

You define a Pydantic model for a single applicant, and then use `List[ApplicantModel]` in your code. Pydantic AI will automatically validate each object in the list returned by the Ninehire tool, ensuring every single one is correct.

Absolutely. You can write a small script that uses Pydantic AI to call `list_job_postings` from your Ninehire account via this MCP Server, with full confidence that the data is clean and validated.

Fewer surprises and easier debugging. When a tool call fails, you get a clear `ValidationError` that tells you exactly what was wrong with the data from Ninehire. It saves you from digging through API logs to find a mismatched field name.

Pydantic AI's role is validation, not storage. It inspects the applicant and job data from Ninehire tools like `get_job_details` at runtime. The secure connection is managed by the underlying MCP client and Vinkius, so your data's privacy relies on the encrypted transport layer.

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