Can I see if a background check has any issues through the agent?

Yes! Use the `get_report_details` tool. The agent will return the current status. If the status is 'Consider', it means some records were found that require your review.

How do I invite a new candidate to start their check?

First, use `create_new_candidate` to add them to your account, then use `start_background_check` with the appropriate package name to trigger the invitation email.

Where do I find my Checkr API Secret Key?

Log in to your Checkr dashboard and navigate to **Settings -> API**. You can generate and copy your Secret Key from the 'API Keys' section.

Checkr MCP Server for Pydantic AI 8 tools — connect in under 2 minutes

Name: Checkr
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 8 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Checkr through 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

Open in ChatGPT Open in Claude Open in Perplexity

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 Checkr "
            "(8 tools)."
        ),
    )

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

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Checkr MCP Server

Connect your Checkr account to any AI agent and take full control of your background screening and hiring compliance through natural conversation. Streamline how you screen candidates and verify credentials.

Pydantic AI validates every Checkr tool response against typed schemas, catching data inconsistencies at build time. Connect 8 tools through 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.

What you can do

Candidate Oversight — List and retrieve details for all candidates in your account natively
Report Intelligence — Access background check reports and their current status (Clear, Consider, Pending) flawlessly
Screening Automation — Create new candidate profiles and initiate background checks securely
Package Logistics — List and manage available screening packages like 'Pro' and 'Basic' flawlessly
Invitation Control — Monitor invitations sent to candidates to complete their own screening applications securely
Compliance Monitoring — Retrieve detailed report results and adverse action status directly within your workspace

The Checkr MCP Server exposes 8 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 Checkr to Pydantic AI via MCP

Follow these steps to integrate the Checkr 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 8 tools from Checkr with type-safe schemas

Why Use Pydantic AI with the Checkr MCP Server

Pydantic AI provides unique advantages when paired with Checkr 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 Checkr integration code

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

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

Checkr + Pydantic AI Use Cases

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

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

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

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

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

Checkr MCP Tools for Pydantic AI (8)

These 8 tools become available when you connect Checkr to Pydantic AI via MCP:

create_new_candidate

Create a new candidate profile

get_candidate_details

Get detailed information for a specific candidate

get_report_details

Get detailed information for a specific background report

list_background_reports

List background check reports

list_checkr_candidates

List candidates in the account

list_screening_invitations

List invitations sent to candidates

list_screening_packages

List available screening packages (e.g. Pro, Basic)

start_background_check

Initiate a background check for a candidate

Example Prompts for Checkr in Pydantic AI

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

"List the last 5 background checks in my Checkr account."

"Check the status of the candidate named 'Jane Smith'."

"What screening packages do I have available?"

Troubleshooting Checkr MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Checkr + Pydantic AI FAQ

Common questions about integrating Checkr 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 Checkr MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.