How to Use the Checkr MCP in LlamaIndex

Index background check data directly into LlamaIndex for semantic hiring intelligence.

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Connect Checkr MCP to LlamaIndex

Create your Vinkius account to connect Checkr to LlamaIndex 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 Checkr with LlamaIndex

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Query Checkr data with LlamaIndex

Index the results of `list_background_reports` into your vector store. It turns raw API records into a searchable knowledge base for your HR team. Your agent retrieves relevant findings using semantic search rather than exact matches. This helps you find patterns across historical hiring data.

Ground answers in Checkr reports

Use this MCP Server to feed verified report data into your RAG pipeline. When you ask about a candidate's status, the system pulls the specific details from `get_candidate_details`. It stops your agent from guessing by providing the actual API response as context. You get accurate, documented answers every time.

Manage screening tasks in LlamaIndex

Call `start_background_check` directly from your RAG agents. It combines the retrieval of candidate history with the ability to take action on new applicants. This creates a loop where your agent learns from past reports while initiating new ones. It keeps your knowledge base current with live data.

Setup guide

Set up Checkr MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Checkr MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

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

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Checkr tools.",
)
response = await agent.run("List recent Checkr data")

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Common questions about Checkr MCP in LlamaIndex

Use the MCP tool spec to initialize the client and convert the tools to a list. Once done, pass them to your FunctionAgent to enable API access.

By indexing the output of the tools, LlamaIndex persists report data in your vector store. This allows you to query past candidate outcomes easily.

PII, such as Social Security numbers and birth dates, is handled in temporary memory during the indexing process. You control the access level of the vector store.

Yes, you can filter which tools the agent uses. It integrates into your existing indexing pipeline without needing structural changes.

Simply trigger the relevant tool when your agent needs fresh data. LlamaIndex then updates the vector store entries with the new report details.

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