How to Use the Uscreen MCP in LlamaIndex

Q: How do I improve my knowledge base using Uscreen and LlamaIndex?

The best approach is to index data from listtransactions and combine it with product descriptions from listproducts. This lets your agent answer complex questions that require both financial and catalog information.

Q: Can LlamaIndex help me query Uscreen content status?

Yes. You can index the results of listcontent and then ask semantic questions like, 'What assets are ready for monetization?' The system pulls answers directly from the indexed data.

Q: Is LlamaIndex suitable for managing Uscreen customer inquiries?

It's perfect. Indexing getcustomer results means your agent can answer specific questions about a user's account details, rather than just listing the raw data.

Q: What is the best way to use Uscreen data in RAG applications with LlamaIndex?

Use the MCP Server tools to pull foundational facts—like listsubscriptions details—and index those results. This makes your knowledge base constantly current and verifiable.

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. The tools getcustomer and listcustomers are the primary sources for this information.

Ground your knowledge base in live Uscreen data with LlamaIndex.

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Connect Uscreen MCP to LlamaIndex

Create your Vinkius account to connect Uscreen to LlamaIndex 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 Uscreen with LlamaIndex

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Indexing Transaction Records

Run `list_transactions` to pull payment records and feed them into an index. Instead of just running a query, your RAG application builds a searchable knowledge base from this raw data. Later queries can search the indexed transactions for specific dates or amounts. This process means you don't lose context; every transaction record becomes part of a unified, queryable source that grounds all subsequent AI answers.

Retrieving Collection Metadata

Use `list_collections` to get the names and IDs of available groups. Then, index this metadata so your agent can answer questions like, 'Which collections are related to premium content?' The search is grounded in the exact data provided by the MCP Server. This ability lets you combine API results with documents. You query the index, and it pulls answers based on the actual Uscreen collection details.

Searching Product Details

Index the output of `list_products` to create a product knowledge base. Your agent can then answer questions like, 'What are the features of our top-selling products?' without running a live search that might miss context. By indexing these details, you combine structured API data with unstructured documents, giving your AI client comprehensive answers grounded in verifiable Uscreen information.

Setup guide

Set up Uscreen MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Uscreen MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Uscreen tools.",
)
response = await agent.run("List recent Uscreen data")

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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 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.

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Common questions about Uscreen MCP in LlamaIndex

The best approach is to index data from `list_transactions` and combine it with product descriptions from `list_products`. This lets your agent answer complex questions that require both financial and catalog information.

Yes. You can index the results of `list_content` and then ask semantic questions like, 'What assets are ready for monetization?' The system pulls answers directly from the indexed data.

It's perfect. Indexing `get_customer` results means your agent can answer specific questions about a user's account details, rather than just listing the raw data.

Use the MCP Server tools to pull foundational facts—like `list_subscriptions` details—and index those results. This makes your knowledge base constantly current and verifiable.

The server touches Customer details. The tools `get_customer` and `list_customers` are the primary sources for this information.

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