Can I initiate a background check through the AI agent?

Yes. Use `create_new_vetting_check` with the contact ID and vetting package to initiate a background check. Use `create_screening_contact` first if the person isn't in your system. Track progress with `get_vetting_request_status`.

Can I track the status of active vetting requests?

Yes. Use `list_vetting_requests` to see all requests with their current status. Use `get_vetting_request_status` for detailed progress on a specific check. Use `cancel_active_vetting` to stop a check that's no longer needed.

Can I manage screening contacts and their data?

Yes. Use `list_screening_contacts` to browse all contacts, `get_screening_contact` for individual profiles, and `create_screening_contact` to add new people to the system. Each contact can have multiple vetting requests associated.

INSTANT PLUG AND PLAY

InfoVetted MCP Server for Google ADKGive Google ADK instant access to 12 tools to Cancel Active Vetting, Check Api Connectivity, Create Contact Group, and more

Name: InfoVetted
Availability: InStock
Author: Vinkius

Built by Vinkius GDPR 12 Tools SDK

Google Agent Development Kit (ADK) is Google's framework for building production AI agents. Add InfoVetted as an MCP tool provider through Vinkius and your ADK agents can call every tool with full schema introspection.

Use with Your AI Agent

Ask AI about this App Connector for Google ADK

Open in ChatGPT Open in Claude Open in Perplexity

The InfoVetted app connector for Google ADK 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

python

from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import (
    StreamableHTTPConnectionParams,
)

# Your Vinkius token. get it at cloud.vinkius.com
mcp_tools = McpToolset(
    connection_params=StreamableHTTPConnectionParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    )
)

agent = Agent(
    model="gemini-2.5-pro",
    name="infovetted_agent",
    instruction=(
        "You help users interact with InfoVetted "
        "using 12 available tools."
    ),
    tools=[mcp_tools],
)

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 InfoVetted MCP Server

Connect your InfoVetted account to any AI agent and manage background checks through natural conversation.

Google ADK natively supports InfoVetted as an MCP tool provider. declare Vinkius Edge URL and the framework handles discovery, validation, and execution automatically. Combine 12 tools with Gemini's long-context reasoning for complex multi-tool workflows, with production-ready session management and evaluation built in.

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 Google ADK 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 Google ADK

When Google ADK 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_active_vetting

Cancel a background check

check_api_connectivity

Verify InfoVetted API status

create_contact_group

g., "Engineering Team"). Create a new organization group

create_new_vetting_check

Initiate a background check

create_screening_contact

Add a new individual for screening

get_contact_details

Get details for a specific individual

get_vetting_request_status

Check status of a vetting process

list_configured_webhooks

List active webhooks

list_contact_groups

List organizational contact groups

list_screening_contacts

List individuals being screened

list_supported_check_types

). List available background check types

list_vetting_requests

List all background check requests

Connect InfoVetted to Google ADK via MCP

Follow these steps to wire InfoVetted into Google ADK. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.

Install Google ADK

Run pip install google-adk

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Create the agent

Save the code above and integrate into your ADK workflow

Explore tools

The agent will discover 12 tools from InfoVetted via MCP

Why Use Google ADK with the InfoVetted MCP Server

Google ADK provides unique advantages when paired with InfoVetted through the Model Context Protocol.

Google ADK natively supports MCP tool servers. declare a tool provider and the framework handles discovery, validation, and execution

Built on Gemini models, ADK provides long-context reasoning ideal for complex multi-tool workflows with InfoVetted

Production-ready features like session management, evaluation, and deployment come built-in. not bolted on

Seamless integration with Google Cloud services means you can combine InfoVetted tools with BigQuery, Vertex AI, and Cloud Functions

InfoVetted + Google ADK Use Cases

Practical scenarios where Google ADK combined with the InfoVetted MCP Server delivers measurable value.

Enterprise data agents: ADK agents query InfoVetted and cross-reference results with internal databases for comprehensive analysis

Multi-modal workflows: combine InfoVetted tool responses with Gemini's vision and language capabilities in a single agent

Automated compliance checks: schedule ADK agents to query InfoVetted regularly and flag policy violations or configuration drift

Internal tool platforms: build self-service agent platforms where teams connect their own MCP servers including InfoVetted

Example Prompts for InfoVetted in Google ADK

Ready-to-use prompts you can give your Google ADK 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 Google ADK

Common issues when connecting InfoVetted to Google ADK through the Vinkius, and how to resolve them.

McpToolset not found

Update: pip install --upgrade google-adk

InfoVetted + Google ADK FAQ

Common questions about integrating InfoVetted MCP Server with Google ADK.

How does Google ADK connect to MCP servers?

Import the MCP toolset class and pass the server URL. ADK discovers and registers all tools automatically, making them available to your agent's tool-use loop.

Can ADK agents use multiple MCP servers?

Yes. Declare multiple MCP tool providers in your agent configuration. ADK merges all tool schemas and the agent can call tools from any server in a single turn.

Which Gemini models work best with MCP tools?

Gemini 2.0 Flash and Pro models both support function calling required for MCP tools. Flash is recommended for latency-sensitive use cases, Pro for complex reasoning.