Refinery MCP

Refinery MCP

Clean HTML before your agent burns tokens.

Landing page · Apify Actor

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>

Home · Pricing · Login · Docs · Blog · Careers Subscribe to our newsletter

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.

Legal · Privacy · Cookie settings · LinkedIn · X

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": " Home Pricing Login Vendor security update We now support SOC 2 exports for enterprise accounts. Legal Privacy Careers ", "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.json added — 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