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 LlamaIndex 8 tools — connect in under 2 minutes

Name: Checkr
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 8 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Checkr as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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 llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Checkr. "
            "You have 8 tools available."
        ),
    )

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

asyncio.run(main())

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* 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.

LlamaIndex agents combine Checkr tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

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 LlamaIndex 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 LlamaIndex via MCP

Follow these steps to integrate the Checkr MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

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

Why Use LlamaIndex with the Checkr MCP Server

LlamaIndex provides unique advantages when paired with Checkr through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Checkr tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Checkr tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Checkr, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Checkr tools were called, what data was returned, and how it influenced the final answer

Checkr + LlamaIndex Use Cases

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

Hybrid search: combine Checkr real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Checkr to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Checkr for fresh data

Analytical workflows: chain Checkr queries with LlamaIndex's data connectors to build multi-source analytical reports

Checkr MCP Tools for LlamaIndex (8)

These 8 tools become available when you connect Checkr to LlamaIndex 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 LlamaIndex

Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Checkr + LlamaIndex FAQ

Common questions about integrating Checkr MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Checkr tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.