How to Use the Cleared (ClearedIn) MCP in LangChain

Q: How does Cleared (ClearedIn) pass background screening data to LangChain?

The MCP server exposes listbackgroundscreenings which your agent calls during execution. LangChain receives structured JSON payloads representing active screening states, which you can immediately feed into subsequent prompt templates.

Q: Can I trace Cleared (ClearedIn) tool calls inside LangChain?

Yes, every tool call like getverificationdetails goes through the LangChain adapter. LangSmith logs the inputs, outputs, and execution time, giving you a full audit trail of your identity checks.

Q: What happens if a signature request is pending?

Your agent invokes getsignaturedetails to check the status. The returned state allows your pipeline to branch, halting execution or sending a reminder depending on the signature phase.

Get your Cleared (ClearedIn) identity verification and background check data directly into LangChain using this MCP Server.

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Connect Cleared (ClearedIn) MCP to LangChain

Create your Vinkius account to connect Cleared (ClearedIn) 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 Cleared (ClearedIn) with LangChain

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Run Cleared audits inside your LangChain pipelines

Your agent runs `list_cleared_audit_logs` to inspect security anomalies before executing sensitive code paths. This tool pulls raw audit logs from Cleared so your chain can verify user actions on the fly. By feeding this log data directly into the next chain link, you build self-auditing workflows. LangSmith traces every single log retrieval, mapping out the exact latency and token usage of your security checks.

Trace identity verifications with LangSmith

The `list_identity_verifications` tool pulls active verification requests directly into your LangGraph state. You pass this list to a decision node that checks which users need immediate follow-up. If a verification is stuck, the chain triggers `get_verification_details` to extract the exact blocker. You get a clear, step-by-step trace of the decision tree in your debugger without writing custom polling code.

Chain background screenings using this MCP Server

We use `list_background_screenings` to fetch pending background check statuses and pipe them to down-chain processing steps. Your LangChain agent evaluates the screening results to decide if an account requires manual review. When a screening shows anomalies, `get_screening_details` fetches the raw report points. The chain handles this data dynamically, passing the structured outputs straight to your database integrations.

Setup guide

Set up Cleared (ClearedIn) 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 Cleared (ClearedIn) 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({
    "cleared-clearedin-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 Cleared (ClearedIn) 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 Cleared (ClearedIn). 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 Cleared (ClearedIn) MCP in LangChain

The MCP server exposes `list_background_screenings` which your agent calls during execution. LangChain receives structured JSON payloads representing active screening states, which you can immediately feed into subsequent prompt templates.

Yes, every tool call like `get_verification_details` goes through the LangChain adapter. LangSmith logs the inputs, outputs, and execution time, giving you a full audit trail of your identity checks.

You initialize the MultiServerMCPClient with the Cleared endpoint alongside your other servers. The agent dynamically routes queries to `get_signature_details` or other tools based on the conversation context.

Your agent invokes `get_signature_details` to check the status. The returned state allows your pipeline to branch, halting execution or sending a reminder depending on the signature phase.

Cleared data stays inside the Vinkius V8 Isolate Sandbox during transit. The MCP server never stores your identity verifications or background screenings, pulling them on-demand via encrypted HTTPS tunnels directly to your local runtime.

Use it with your favorite AI tools

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