How to Use the Jobvite MCP in Pydantic AI

Build type-safe recruitment agents using Pydantic AI to validate every Jobvite status change at runtime.

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Connect Jobvite MCP to Pydantic AI

Create your Vinkius account to connect Jobvite 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 Jobvite with Pydantic AI

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Strict Type Safety for Jobvite Profiles

The `get_candidate` tool retrieves candidate profiles with strict schema validation enforced by your Python code. If the Jobvite API returns unexpected null fields or modified schemas, Pydantic AI catches it immediately, preventing corrupted data from entering your local variables. This MCP Server ensures that write operations like `create_candidate` conform exactly to your data models. Your agent cannot send malformed payloads, saving you from API errors and broken pipelines.

Validate Job Requisitions Before Posting

The `create_job` tool lets your agent draft and publish new openings while validating department codes and salary ranges. By matching the output against your Pydantic models, you ensure that every requisition pulled via `list_jobs` or created from scratch is perfectly formatted. The toolset integrates directly into your agent definition. It queries `get_job` to verify existing listings and guarantees that your automated hiring workflows never violate internal compliance rules.

Safe Status Transitions via Pydantic AI

The `update_application_status` tool changes candidate stages only when all validation checks pass. Your agent inspects the application history with `list_applications`, runs your custom validation logic, and updates the status. Because Pydantic AI is model-agnostic, you can switch LLMs behind the scenes without rewriting your integration logic. The tool schemas for `update_candidate` remain completely stable and validated.

Setup guide

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

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

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

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Common questions about Jobvite MCP in Pydantic AI

Install the mcp extra for Pydantic AI and import MCPToolset. Initialize it with your Vinkius HTTP URL and pass it to your Agent's toolsets list.

The framework raises a validation error at runtime instead of letting your agent proceed with bad data. This protects your downstream processes from silent failures when calling `get_candidate`.

Yes, the framework is model-agnostic. You can connect your local models to the server to run tools like `list_jobs` without changing your configuration.

Yes, the MCPToolset supports both streamable HTTP and SSE transports. Vinkius handles the server-side hosting so your client can connect reliably.

Your Jobvite API credentials and candidate profiles are handled inside an ephemeral, zero-trust sandbox. No personal data is stored on Vinkius, ensuring your hiring data remains private.

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