InfoVetted MCP Server for Pydantic AIGive Pydantic AI instant access to 12 tools to Cancel Active Vetting, Check Api Connectivity, Create Contact Group, and more
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect InfoVetted through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
Ask AI about this App Connector for Pydantic AI
The InfoVetted app connector for Pydantic AI 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 pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to InfoVetted "
"(12 tools)."
),
)
result = await agent.run(
"What tools are available in InfoVetted?"
)
print(result.data)
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.
Pydantic AI validates every InfoVetted tool response against typed schemas, catching data inconsistencies at build time. Connect 12 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
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 Pydantic AI 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 Pydantic AI
When Pydantic AI 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 Pydantic AI via MCP
Follow these steps to wire InfoVetted into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install Pydantic AI
pip install pydantic-aiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use Pydantic AI with the InfoVetted MCP Server
Pydantic AI provides unique advantages when paired with InfoVetted through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your InfoVetted integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your InfoVetted connection logic from agent behavior for testable, maintainable code
InfoVetted + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the InfoVetted MCP Server delivers measurable value.
Type-safe data pipelines: query InfoVetted with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple InfoVetted tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query InfoVetted and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock InfoVetted responses and write comprehensive agent tests
Example Prompts for InfoVetted in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI
Common issues when connecting InfoVetted to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiInfoVetted + Pydantic AI FAQ
Common questions about integrating InfoVetted MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.