How to Use the SEON MCP in LangChain

Q: Is the MCP Server useful for continuous risk scoring with LangChain?

Absolutely. You can set up chains that run checks like amlscreening immediately upon receiving new data, creating a constant stream of updated risk assessments for SEON.

Q: Does the LangChain client handle large volumes of transaction data?

Yes. By chaining tools together—like running gettransaction followed by checkfraud—you process detailed records efficiently, making it ideal for high-volume payment processing.

Build multi-step security pipelines with LangChain.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect SEON MCP to LangChain

Create your Vinkius account to connect SEON 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.

GDPR Free for Subscribers

Setup SEON with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

LangChain: Chaining Security Checks

You chain multiple checks together. Start by running `check_fraud` to get a baseline risk score, then use the IP data from `check_ip` as an input for more granular analysis. This lets your agent perform complex reasoning. It doesn't just run tools; it decides which tool runs next and why, linking the output of one step into the logic of the next.

MCP Server: Account Intelligence Flow

Gather all necessary identity points in sequence. First, use `get_account_info` to pull core user data. Then, run `check_phone` and `check_email` against that profile. The result feeds into a final assessment using `add_label`, flagging the record for review. This process maps out a complete digital footprint before any transaction happens. You build an end-to-end intelligence pipeline directly within your LangChain agent.

LangChain: Transaction Monitoring Logic

Monitor account lists and rules in real time. Use `list_rules` to see existing fraud parameters, then use `get_transaction` to pull the specific data point you're analyzing. You can combine these inputs to determine if a transaction breaks any known thresholds. This setup helps your agent write custom logic for risk scoring. It moves beyond simple checks and builds decision trees based on SEON’s core security tools.

Setup guide

Set up SEON 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 SEON 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({
    "seon-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 SEON transactions"
    })
    print(result["messages"][-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by SEON. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect SEON now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about SEON MCP in LangChain

The agent calls SEON's MCP Server tools directly. For example, it runs `check_fraud` and passes that resulting risk score as a variable into subsequent code steps for further processing.

You build multi-step reasoning pipelines. You're not just querying data; you're letting your agent decide the sequence of calls—maybe check the IP first, then the email, and finally add a label.

Absolutely. You can set up chains that run checks like `aml_screening` immediately upon receiving new data, creating a constant stream of updated risk assessments for SEON.

Yes. By chaining tools together—like running `get_transaction` followed by `check_fraud`—you process detailed records efficiently, making it ideal for high-volume payment processing.

This SEON server handles sensitive identity data points, including phone numbers and email addresses, which are accessed via `check_phone` and `check_email` tools. You need to manage those results carefully.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript