How to Use the Uscreen MCP in LangChain

Q: How do I check my Uscreen subscription status using LangChain?

You run listsubscriptions first to get all active plans. Then, you pass a specific plan ID into your agent's next step for detailed analysis. This lets your LangChain setup verify which subscriptions are current and what they entail.

Q: Can LangChain handle Uscreen data when debugging payment flows?

Absolutely. You can combine getcustomer with listtransactions. This allows your LangChain agent to trace a user's journey from account creation to their last successful payment, giving you full visibility into the flow.

Q: If I need to know about a user's account, which specific Uscreen data type does the MCP Server touch?

The server touches Customer details. Tools like getcustomer and listcustomers allow you to fetch and process all of this core customer record information.

Build complex, multi-step Uscreen workflows using LangChain.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Uscreen MCP to LangChain

Create your Vinkius account to connect Uscreen 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 Uscreen with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Multi-Step Customer Verification

The `get_customer` tool pulls basic user details. You can then chain that data to check the customer's full payment history by calling `list_transactions`. This allows your agent to perform complex, multi-stage reasoning about account status. LangChain handles this whole process: it uses the output of checking a customer ID as direct input for listing their transactions. You build an accurate user profile without needing multiple API calls in separate scripts.

Tracking Content Availability

Need to know what content is live? The `list_content` tool gives you a list of all assets. Next, pass the ID from that list to `get_content` to fetch specific metadata about it. This sequential process lets your agent verify if an asset exists and meets required criteria. This chain structure means the output—the content ID—is immediately available to the next step in the pipeline. It's perfect for validating complex deployment rules across multiple Uscreen assets.

Listing Product Catalogs

Use `list_products` to get a full view of your store items. Then, take the product IDs and feed them into `get_collection` to check which groups they belong to. This two-step process verifies both availability and proper categorization. By chaining these MCP tool calls together, you create an agent that doesn't just list data; it actually validates relationships between products and collections. It gives you a complete picture of the catalog structure.

Setup guide

Set up Uscreen 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 Uscreen 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({
    "uscreen-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 Uscreen 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 Uscreen. 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 Uscreen 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 Uscreen MCP in LangChain

You run `list_subscriptions` first to get all active plans. Then, you pass a specific plan ID into your agent's next step for detailed analysis. This lets your LangChain setup verify which subscriptions are current and what they entail.

You should focus on chaining tools that depend on each other. For example, getting a customer ID from `list_customers` and then using that ID to check their profile via `get_customer` is the ideal chain structure.

Absolutely. You can combine `get_customer` with `list_transactions`. This allows your LangChain agent to trace a user's journey from account creation to their last successful payment, giving you full visibility into the flow.

Yes. Since LangChain is built for complex integration, it can take data pulled from an MCP Server like Uscreen and pass it to databases or other third-party tools in the same reasoning chain.

The server touches Customer details. Tools like `get_customer` and `list_customers` allow you to fetch and process all of this core customer record information.

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