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Clean raw HTML into LLM-ready text before agents spend tokens.
About
Clean raw HTML into LLM-ready text before agents spend tokens.
Security Report
10.0
Low Risk10.0Low RiskValid MCP server (3 strong, 4 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry.
4 files analyzed · 1 issue found
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What You'll Need
Set these up before or after installing:
Apify API token for the Refinery HTML cleaner actorRequired
Environment variable: APIFY_TOKEN
How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-cameronlares-refinery-mcp": {
"env": {
"APIFY_TOKEN": "your-apify-token-here"
},
"args": [
"-y",
"@larelabs/refinery-mcp"
],
"command": "npx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
Refinery MCP
Clean HTML before your agent burns tokens.
Refinery MCP wraps the Refinery Apify Actor as an MCP server so Claude, Cursor, and other agents can turn raw HTML or URLs into clean LLM-ready text plus word_count.
flowchart LR
A[Agent needs web context] --> B[Fetch URL or raw HTML]
B --> C[Refinery MCP]
C --> D[Refinery Apify Actor]
D --> E[Clean text + word_count]
E --> F[RAG / embeddings / LLM context]
The Problem
Agents are getting good at fetching web pages. The problem is what they fetch:
<html>
<head>
<script>gtag("event", "page_view")</script>
<style>.nav,.cookie,.footer{display:block}</style>
</head>
<body>
<nav>Home · Pricing · Login · Docs · Blog · Careers</nav>
<aside>Subscribe to our newsletter</aside>
<article>
<h1>How ACME cut support ticket routing time by 63%</h1>
<p>ACME routes 40,000 monthly support tickets through an AI triage system.</p>
<p>The team reduced retrieval noise by cleaning HTML before chunking.</p>
</article>
<footer>Legal · Privacy · Cookie settings · LinkedIn · X</footer>
</body>
</html>
The model does not need most of that. It needs this:
How ACME cut support ticket routing time by 63%
ACME routes 40,000 monthly support tickets through an AI triage system.
The team reduced retrieval noise by cleaning HTML before chunking.
Refinery MCP gives your agent a tool for that middle step:
fetch page -> refine HTML -> send clean text to RAG / embeddings / LLM
Why
Agents can fetch pages, but raw HTML is noisy and expensive:
- scripts, styles, tracking tags
- nav, footers, cookie banners
- repeated links and layout markup
- huge token burn before the model sees the real content
Refinery is the middle step your agent can call before it stuffs web context into a prompt:
fetch/render -> clean/refine -> chunk/embed/answer
It is not a crawler. Use Firecrawl, Crawl4AI, Playwright, browser automation, or your own fetcher when you need rendering. Use Refinery when you already have a URL or raw HTML and want a cheap cleanup pass before the LLM.
When To Use It
Use Refinery MCP when:
- your agent already fetched a page but got bloated HTML
- you want a deterministic cleanup step before RAG ingestion
- you need
word_count/ token-ish savings before embedding - you want to separate crawling from content cleanup
Do not use it as your browser renderer, anti-bot layer, or site crawler.
Tools
clean_url
Fetches a URL through the Refinery Apify Actor and returns dataset rows with clean text and metadata.
Example input:
{
"url": "https://docs.stripe.com/payments",
"removeScripts": true,
"removeStyles": true
}
clean_html
Cleans raw HTML your agent, crawler, or browser session already fetched.
Example input:
{
"html": "<html><body><nav>Home Pricing Login</nav><article><h1>Vendor security update</h1><p>We now support SOC 2 exports for enterprise accounts.</p></article><footer>Legal Privacy Careers</footer></body></html>",
"extractMentions": false,
"extractHashtags": false
}
Example result:
{
"text": "Vendor security update\n\nWe now support SOC 2 exports for enterprise accounts.",
"word_count": 10,
"content_type": "web",
"language": "en",
"processing_time_ms": 44.96,
"success": true
}
estimate_savings
Local helper that compares raw HTML vs cleaned text and estimates token savings. This does not call Apify.
Example output:
{
"raw_chars": 168,
"clean_chars": 41,
"estimated_raw_tokens": 42,
"estimated_clean_tokens": 11,
"estimated_token_savings": 31,
"reduction_pct": 76
}
Install
npx -y @larelabs/refinery-mcp
Set your Apify token:
export APIFY_TOKEN=apify_api_xxx
export REFINERY_ACTOR_ID=larelabs/refinery-html-to-llm-cleaner
Cursor / Claude Desktop config
Use the published package:
{
"mcpServers": {
"refinery": {
"command": "npx",
"args": ["-y", "@larelabs/refinery-mcp"],
"env": {
"APIFY_TOKEN": "apify_api_xxx",
"REFINERY_ACTOR_ID": "larelabs/refinery-html-to-llm-cleaner"
}
}
}
}
Or run from source during development:
git clone https://github.com/LareLabs/refinery-mcp
cd refinery-mcp
npm install
npm run build
{
"mcpServers": {
"refinery": {
"command": "npm",
"args": ["run", "dev", "--prefix", "/absolute/path/to/refinery-mcp"],
"env": {
"APIFY_TOKEN": "apify_api_xxx"
}
}
}
}
Smoke Test
npm run build
APIFY_TOKEN=apify_api_xxx npm run smoke
The smoke test starts the MCP server over stdio, lists tools, and calls estimate_savings without spending Apify credits.
Example Agent Prompt
Use Refinery MCP to clean this docs page before summarizing it:
https://docs.stripe.com/payments
Return the clean text, word_count, and a short summary. Do not summarize raw HTML.
Another useful prompt:
I fetched this page HTML with Playwright. Use Refinery MCP clean_html before adding it to my RAG ingestion queue. Return the cleaned text and estimated token savings.
Roadmap
- Glama listing (
glama.jsonadded — submit at https://glama.ai/mcp/servers) - mcp.so directory PR (pending)
- Hosted HTTP/SSE MCP transport
- Batch URL cleanup tool
- Glama / PulseMCP / FindMCP / mcp.so listings
- Optional direct REST wrapper for RapidAPI
- Token savings benchmark page
License
MIT
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