How do I get Jobtoolz API credentials?

Log in to your Jobtoolz account, navigate to Settings > API, and find your API Key.

Does it support departments and locations?

Yes, you can list all configured departments and office locations using this MCP.

Can I see pipeline stages?

Yes, the list_stages tool provides access to your recruitment pipeline configuration.

Jobtoolz MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Jobtoolz
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Jobtoolz through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Jobtoolz "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Jobtoolz?"
    )
    print(result.data)

asyncio.run(main())

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About Jobtoolz MCP Server

Empower your AI agents with Jobtoolz's recruitment management platform. This MCP server allows you to list jobs, track candidates, manage pipeline stages, and view departments and locations directly through the Jobtoolz API. Ideal for automating hiring workflows and candidate engagement.

Pydantic AI validates every Jobtoolz tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

The Jobtoolz MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Jobtoolz to Pydantic AI via MCP

Follow these steps to integrate the Jobtoolz MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Jobtoolz with type-safe schemas

Why Use Pydantic AI with the Jobtoolz MCP Server

Pydantic AI provides unique advantages when paired with Jobtoolz through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your Jobtoolz integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Jobtoolz connection logic from agent behavior for testable, maintainable code

Jobtoolz + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Jobtoolz MCP Server delivers measurable value.

Type-safe data pipelines: query Jobtoolz with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Jobtoolz tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Jobtoolz and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Jobtoolz responses and write comprehensive agent tests

Jobtoolz MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Jobtoolz to Pydantic AI via MCP:

get_candidate

Returns contact details, application history, and custom field values. Use this for deep-dive vetting of an applicant. Retrieves details for a specific candidate

get_job

Returns descriptions, requirements, and internal status. Essential for detailed analysis of a specific role. Retrieves details for a specific job

list_candidates

Includes candidate names, IDs, and current pipeline status. Use this to monitor applicant flow and identify recent entries. Lists all candidates

list_departments

Useful for filtering jobs and candidates by business unit (e.g., Sales, R&D). Lists all departments

list_jobs

Returns job titles, IDs, and departments. Use this to identify open positions and locate job IDs for candidate management. Lists all active jobs

list_locations

Useful for identifying jobs in specific geographical regions. Lists all office locations

list_sources

g., "Company Website", "Indeed") configured in Jobtoolz. Useful for auditing the origins of candidate traffic. Lists all recruitment sources

list_stages

g., "Applied", "Interview", "Offer"). Essential for understanding the company's hiring process. Lists all configured pipeline stages

list_tags

Useful for identifying valid tags before performing a tagged search. Lists all configured tags

list_users

Useful for identifying account administrators or hiring managers. Lists all organization users

Example Prompts for Jobtoolz in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Jobtoolz immediately.

"List all open jobs in Jobtoolz."

"Show me the details for candidate ID '123'."

"Check the available recruitment sources."

Troubleshooting Jobtoolz MCP Server with Pydantic AI

Common issues when connecting Jobtoolz to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Jobtoolz + Pydantic AI FAQ

Common questions about integrating Jobtoolz MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your Jobtoolz MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.