Refinery MCP server

Refinery MCP

Clean HTML before your agent burns tokens.

Landing page · Apify Actor

<!-- mcp-name: io.github.LareLabs/refinery-mcp -->

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:

Shortened. The full README is on GitHub.