How to Use the InfoVetted MCP in LlamaIndex

Q: How does LlamaIndex store data fetched from InfoVetted?

LlamaIndex indexes the output of tools like listscreeningcontacts directly into your local vector database. This keeps your InfoVetted candidate profiles searchable without sending them to external servers.

Q: Can I filter which InfoVetted tools are exposed to my LlamaIndex MCP Server?

Yes, you can restrict access using the LlamaIndex allowedtools filter when initializing the InfoVetted MCP Server. This ensures your LlamaIndex agent only calls safe tools like getcontactdetails using the MCP protocol.

Q: How do I update my LlamaIndex vector store when an InfoVetted check status changes?

You configure your LlamaIndex pipeline to poll getvettingrequeststatus and re-index the updated record. This keeps your LlamaIndex knowledge base perfectly synchronized with InfoVetted.

Q: How does LlamaIndex confirm the background check API is online?

Your LlamaIndex agent calls checkapiconnectivity before trying to index any records. This step prevents LlamaIndex from running failed query loops when InfoVetted is offline.

Index background check results in LlamaIndex to query historical candidate screening data without hallucination.

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Connect InfoVetted MCP to LlamaIndex

Create your Vinkius account to connect InfoVetted 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 InfoVetted with LlamaIndex

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Indexing candidate background records

The `list_screening_contacts` tool extracts current candidate records to build a searchable LlamaIndex vector store. Your LlamaIndex agent queries this indexed InfoVetted data to retrieve past applicant histories instantly. To enrich this index, LlamaIndex pulls deep files using `get_contact_details`. This integration replaces manual spreadsheet lookups with grounded, queryable InfoVetted data within LlamaIndex.

Querying live vetting status via MCP Server

The `list_vetting_requests` tool pulls the complete log of background checks into your LlamaIndex RAG pipeline. This dynamic context allows your LlamaIndex query engine to answer InfoVetted status questions without hallucination. Whenever a user asks LlamaIndex about a candidate, the system calls `get_vetting_request_status` to fetch live updates. Your LlamaIndex agent gets answers backed by real-time InfoVetted data.

Dynamic check selection with LlamaIndex

The `list_supported_check_types` tool queries available screenings to index them as metadata within LlamaIndex. Your LlamaIndex agent matches candidate profiles against these InfoVetted types to select the correct check. Once matched, LlamaIndex executes `create_new_vetting_check` to run the screening. This automated mapping eliminates complex hardcoded routing in your LlamaIndex workflow.

Setup guide

Set up InfoVetted 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 InfoVetted 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 InfoVetted tools.",
)
response = await agent.run("List recent InfoVetted data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by InfoVetted. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about InfoVetted MCP in LlamaIndex

LlamaIndex indexes the output of tools like `list_screening_contacts` directly into your local vector database. This keeps your InfoVetted candidate profiles searchable without sending them to external servers.

Yes, you can restrict access using the LlamaIndex allowed_tools filter when initializing the InfoVetted MCP Server. This ensures your LlamaIndex agent only calls safe tools like `get_contact_details` using the MCP protocol.

You configure your LlamaIndex pipeline to poll `get_vetting_request_status` and re-index the updated record. This keeps your LlamaIndex knowledge base perfectly synchronized with InfoVetted.

Your LlamaIndex agent calls `check_api_connectivity` before trying to index any records. This step prevents LlamaIndex from running failed query loops when InfoVetted is offline.

Candidate details and criminal background records pass directly from InfoVetted to your local LlamaIndex environment. This MCP Server processes all screening data in a zero-trust, ephemeral sandbox.

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