How to Use the HTML XSS Sanitizer MCP in LlamaIndex

Scrub legacy documents and web scrapes before indexing them into your LlamaIndex MCP knowledge base.

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Connect HTML XSS Sanitizer MCP to LlamaIndex

Create your Vinkius account to connect HTML XSS Sanitizer 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 HTML XSS Sanitizer with LlamaIndex

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Clean Ingested Data for RAG Applications

Calling `sanitizeHtml_html` during your ingestion pipeline neutralizes threats from web scrapers. Before LlamaIndex chunks and embeds that data, you need to strip out malicious scripts that could execute when rendered in your frontend. You get clean HTML that your frontend can render without risk.

Index Safe Content with LlamaIndex

The `sanitizeHtml_html` tool fixes poisoned HTML before it enters your vector store. If your vector store contains poisoned data, your application is compromised the moment a user queries it. It removes dangerous attributes and iframe injections so your LlamaIndex agents can process the clean text safely.

Secure Your LlamaIndex MCP Server Setup

Your LlamaIndex agent routes third-party API payloads through `sanitizeHtml_html` to sanitize formatting. Connecting external tools introduces risk if you trust the output blindly. The agent only stores the scrubbed result in your knowledge base.

Setup guide

Set up HTML XSS Sanitizer 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 HTML XSS Sanitizer 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 HTML XSS Sanitizer tools.",
)
response = await agent.run("List recent HTML XSS Sanitizer 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 Sanitize HTML. 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 HTML XSS Sanitizer MCP in LlamaIndex

Install llama-index-tools-mcp. Set up a BasicMCPClient and use McpToolSpec to load the tools for your agent.

No. It only removes executable code and dangerous tags. The core text and safe formatting remain intact for your vector store.

Run it immediately after pulling raw HTML from external sources, right before you chunk the text for indexing.

You send individual HTML strings to the tool. Your agent will iterate through your documents and clean them one by one.

The server evaluates the text payload in isolation and discards it. Zero data is retained, meaning your proprietary document scrapes remain entirely private.

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