How to Use the InfoVetted MCP in LangChain

Q: How does a LangChain agent verify the background check API is online?

Your LangChain agent calls checkapiconnectivity before running any screening tasks. This prevents the LangChain chain from executing tools when the InfoVetted service is unreachable.

Automate background check pipelines in LangChain by chaining InfoVetted tools directly into your candidate onboarding workflows.

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Connect InfoVetted MCP to LangChain

Create your Vinkius account to connect InfoVetted to LangChain 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 LangChain

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Claude

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Chain-link screening contact creation

The `create_screening_contact` tool registers a new candidate directly into your LangChain onboarding graph. Your LangChain agent handles this initialization step, immediately passing the resulting InfoVetted contact ID to the next node. Instead of manual database entry, this LangChain output feeds directly into `create_contact_group` to organize your candidate pool. This setup means a single LangChain run registers the applicant and assigns their department using InfoVetted groups.

Automated checks with LangChain tracking

Running `create_new_vetting_check` triggers background screenings inside your custom LangGraph workflow. You track the exact latency and execution of these InfoVetted background checks using LangSmith. Your LangChain agent monitors the process by checking `get_vetting_request_status` at scheduled intervals. This automation ensures no InfoVetted background screening gets stuck in your LangChain pipeline.

Multi-server connectivity via MCP Server

The `check_api_connectivity` tool verifies the InfoVetted connection status before your LangChain agent triggers any API calls. You run this check to prevent your LangChain pipeline from wasting resources on offline endpoints. Combining this InfoVetted MCP Server with other tools in your LangChain stack keeps your background checks synchronized. If the InfoVetted API is offline, the LangChain chain halts execution immediately.

Setup guide

Set up InfoVetted MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes InfoVetted tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "infovetted-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent InfoVetted transactions"
    })
    print(result["messages"][-1].content)

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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 LangChain

If an InfoVetted API call fails, your LangChain chain uses standard try-catch blocks to catch the error. You can configure the LangChain agent to retry the `create_new_vetting_check` tool after a brief delay.

First, install the LangChain MCP adapters package. Then, initialize the client pointing to the InfoVetted endpoint to expose tools like `list_screening_contacts` to your LangChain agent.

Yes, you can combine this InfoVetted MCP Server with database or email tools in a single LangChain chain. This allows your LangChain agent to fetch a contact via `get_contact_details` and email them automatically.

Your LangChain agent calls `check_api_connectivity` before running any screening tasks. This prevents the LangChain chain from executing tools when the InfoVetted service is unreachable.

The InfoVetted MCP Server transmits candidate background check records directly to your LangChain environment. No screening data or criminal records are stored on Vinkius during these LangChain runs.

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