InfoVetted MCP Server for LlamaIndexGive LlamaIndex instant access to 12 tools to Cancel Active Vetting, Check Api Connectivity, Create Contact Group, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add InfoVetted as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The InfoVetted app connector for LlamaIndex is a standout in the Human Resources category — giving your AI agent 12 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 InfoVetted. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in InfoVetted?"
)
print(response)
asyncio.run(main())
* 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 InfoVetted MCP Server
Connect your InfoVetted account to any AI agent and manage background checks through natural conversation.
LlamaIndex agents combine InfoVetted tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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
- Vetting Requests — List all vetting requests, create new background checks, check status, and cancel active vettings
- Screening Contacts — Manage contacts for screening with full profile data, create new screening contacts, and inspect individual records
- Package Management — Browse available vetting packages and their included checks
- Result Tracking — Monitor check results with pass/fail status and compliance details
- Activity History — View submission and completion timelines
The InfoVetted MCP Server exposes 12 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.
All 12 InfoVetted tools available for LlamaIndex
When LlamaIndex connects to InfoVetted through Vinkius, your AI agent gets direct access to every tool listed below — spanning background-screening, identity-verification, employment-checks, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Cancel a background check
Verify InfoVetted API status
g., "Engineering Team"). Create a new organization group
Initiate a background check
Add a new individual for screening
Get details for a specific individual
Check status of a vetting process
List active webhooks
List organizational contact groups
List individuals being screened
). List available background check types
List all background check requests
Connect InfoVetted to LlamaIndex via MCP
Follow these steps to wire InfoVetted into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the InfoVetted MCP Server
LlamaIndex provides unique advantages when paired with InfoVetted through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine InfoVetted tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain InfoVetted tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query InfoVetted, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what InfoVetted tools were called, what data was returned, and how it influenced the final answer
InfoVetted + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the InfoVetted MCP Server delivers measurable value.
Hybrid search: combine InfoVetted real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query InfoVetted 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 InfoVetted for fresh data
Analytical workflows: chain InfoVetted queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for InfoVetted in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with InfoVetted immediately.
"Show all active vetting requests and create a new background check for a candidate."
"Check the status of Maria Silva's background check and list all screening contacts."
"Show completed vetting results and cancel the pending check for candidate #3."
Troubleshooting InfoVetted MCP Server with LlamaIndex
Common issues when connecting InfoVetted to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpInfoVetted + LlamaIndex FAQ
Common questions about integrating InfoVetted MCP Server with LlamaIndex.