How to Use the Tuputech Moderation MCP in LlamaIndex

Q: How does Tuputech Moderation help LlamaIndex with compliance?

By indexing the results of checks like scantextpolitics, you create a history log. Your RAG application can then answer specific questions about policy adherence without needing to rerun the live scan.

Index moderation results with LlamaIndex. Make Tuputech Moderation outputs part of your searchable knowledge base.

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Connect Tuputech Moderation MCP to LlamaIndex

Create your Vinkius account to connect Tuputech Moderation 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 Tuputech Moderation with LlamaIndex

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Indexing multi-modal content checks for LlamaIndex

Don't just process the data—store it. When you run a scan, like `scan_image_terrorism`, the output details get indexed into your vector store. This means past moderation reports become searchable knowledge. Later, instead of re-running the check, you can query your index: 'What were our findings on image misuse last week?' The answer is grounded in actual API data.

Using MCP Server for RAG applications with LlamaIndex

You're building a knowledge graph where the moderation results are nodes. If you run `scan_text_politics` on a document, that result is stored alongside the document itself. This allows semantic search against compliance records. It moves beyond simple retrieval. You can combine 'document content' with 'policy violation history' to give users deeper context.

Detecting spam and abuse for LlamaIndex

The `scan_text_abuse` tool output is perfect for indexing policy violations. When a user submits text, you run the scan, get the violation score, and index that result against the submission details. This lets your application quickly answer questions like, 'Which users have triggered abuse flags in the last 30 days?'

Setup guide

Set up Tuputech Moderation 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 Tuputech Moderation 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 Tuputech Moderation tools.",
)
response = await agent.run("List recent Tuputech Moderation data")

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Common questions about Tuputech Moderation MCP in LlamaIndex

By indexing the results of checks like `scan_text_politics`, you create a history log. Your RAG application can then answer specific questions about policy adherence without needing to rerun the live scan.

Run the MCP Server tools first, taking the outputs. Then, use LlamaIndex to index those structured results alongside your documents. This makes API data queryable.

Yes. You can process videos and images through the MCP Server and store their metadata (e.g., 'porn score', 'deepfake probability') in a unified, queryable index.

Yes. You combine your existing document stores (PDFs, databases) with the live API data from the MCP Server into a single knowledge index.

This server supports indexing results derived from text, images, audio streams, and video streams. You can build an index across all these media types.

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