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]

<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>

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.

fetch page -> refine HTML -> send clean text to RAG / embeddings / LLM

fetch/render -> clean/refine -> chunk/embed/answer

{
  "url": "https://docs.stripe.com/payments",
  "removeScripts": true,
  "removeStyles": true
}

{
  "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
}

{
  "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
}

{
  "raw_chars": 168,
  "clean_chars": 41,
  "estimated_raw_tokens": 42,
  "estimated_clean_tokens": 11,
  "estimated_token_savings": 31,
  "reduction_pct": 76
}

npx -y @larelabs/refinery-mcp

export APIFY_TOKEN=apify_api_xxx
export REFINERY_ACTOR_ID=larelabs/refinery-html-to-llm-cleaner

{
  "mcpServers": {
    "refinery": {
      "command": "npx",
      "args": ["-y", "@larelabs/refinery-mcp"],
      "env": {
        "APIFY_TOKEN": "apify_api_xxx",
        "REFINERY_ACTOR_ID": "larelabs/refinery-html-to-llm-cleaner"
      }
    }
  }
}

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"
      }
    }
  }
}

npm run build
APIFY_TOKEN=apify_api_xxx npm run smoke

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.

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.